An Energy-Efficient Routing Protocol for UASN by

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 9, Number 24 (2014) pp. 30589-30602 © Research India Publications http://www.ripublication.com

An Energy-Efficient Routing Protocol for UASN by AFISHS Optimization Algorithm S.A.Kalaiselvan1*, V.Parthasarathy2 and G.VenkataSwaroop3 1*

Research Scholar, Department of Computer Science and Engineering, St.Peter's University, Avadi, Chennai, India, email:[email protected], 2 Professor, Department of Computer Science and Engineering, Vel Tech Multi Tech Dr .RR Dr.SR Engineering College, Avadi, Chennai ,India, email:[email protected], 3 Research Scholar, Department of Computer Applications, St.Peter's University,Avadi,Chennai,India,email:[email protected]. Abstract An effective communication in wireless sensor network depends mainly on the limited power of the nodes deployed in the network. When there is no effective protocol to save power, then the routing among the nodes is difficult. Underwater Acoustic Sensor networks are mainly used for monitoring instruments, controlling of pollution, recording of changes in climate, natural disaster prediction, search missions and marine life study. The underwater network data transmission among the nodes or in a rout, needs a continuous communication. Else it makes data loss and it cannot be recoverable by any other node or by the base station. In such a situation it is necessary to ensure that the energy efficient network is adopted. In order to provide energy efficiency in routing with successful data transmission, this paper introduces an optimized energy efficient routing protocol [OEERP] via nodes having highest energy in a shortest path where the node highest energy and path least distance is optimized using AFISHS optimization algorithm. FISH is a Bio-inspired optimization algorithm, where it is too dynamic, repetitive and reliable. It provides the optimized highest energy nodes sited in a least distance based shortest path in the network. The simulation result shows the performance evaluation of the proposed AFISHS optimization algorithm than the existing ACO algorithm in various factors like Energy, PDR, and throughput and data loss. In terms of energy, throughput, PDR are increased as 5.5% Joules, 12% kb/sec and 0.6% of packets respectively than the Existing approaches.

Keywords: Energy Efficiency, Underwater Acoustic Sensor Network, Artificial Fish Swarm Optimization, Shortest path Routing, Optimization.

30590

S.A.Kalaiselvan et al

Nomenclature: AFISHS-Artificial Fish Swarm AC-Acoustic Channel MAC- Medium Access Control DEEMAC – Distributed Energy Efficient Media Access Control UAC – Underwater Acoustic Communication

INTRODUCTION The concept of physical remote sensor has started gaining momentum from the Middle of 1990s.Though obliged by specific attributes of submerged AC such as restricted accessible data transmission and substantial proliferation, advancement of submerged sensor organization etc. Development of the idea of physical remote sensor in marine application leads way to many practical applications. As of now, the system for the MAC of submerged sensor system is typically focused around MACA convention, the effectiveness of which is moderately low, and ALOHA convention, the unwavering quality of which is lacking. In tune to the overwhelming system stack, the data exasperates more power utilization and crumbling in system execution. Also, the sea base instruments are generally battery controlled accordingly the force utilization of single hub is specifically applicable to the life of whole system with the goal that the outline of stumpy power hub structural engineering and stumpy power MAC convention are highly focused in late research. A synchronous MAC convention based upon S-MAC protocol was projected in reference (4). It chips away at 'slumber wake' mode, and keeps the synchronization of capture attempt/discharge between every hub to decrease power utilization and evade data impact. Moreover, another T-Lodi-MAC convention is portrayed in reference (10), which additionally deals with 'slumber wake' mode to diminish power utilization. Yet it obliged utilizing a particular, ultra-low power collector to awaken hub. This paper proposes a novel technique to select an optimization based energy efficient shortest best path using AFSO algorithm.

RELATED WORKS Various ideas and algorithms are studied and suggested on the proposed approach for providing more efficiency in energy saving. A comparative study of network technologies, its uses on underwater acoustic channels, also the shallow-water acoustic network based outlines and examples are given in [1] for understanding better concepts of multiple research directions. Some of the investigations, practical issues, concepts of UASN, techniques used for energy saving, node deployment in various layers, challenges and solutions are in given in [2]. In a 3D UASN, the data gathering problem is examined in network layer, by monitoring the communication

An Energy-Efficient Routing Protocol for UASN

30591

among the characteristics and routing functionality of the underwater channel [3]. For delay in sensitive sensor network applications two routing techniques are proposed. A DEEMAC protocol is proposed for UASN which differs from ALOHA, MACAW, and MACA in [4]. Several basic key aspects of UA communications which are analyzed with 2D, 3D underwater acoustic sensor network architectures are described in [5] with the underwater channel characterization. The author, in [6] initially describes an experimental design for a cost effective UAS nodes for placement in a trivial underwater atmosphere. Further, he suggested that the proposed binary MAC enhancement can adopt the delay in acoustic network. Some simulation tools, toolkit, channel based background models and Aqua Tool‟s usage, the result obtained using these tools, validation of the simulation and simulator with testing is given in detail in [7].The paper [8] provides some key points and notes for the researches to their key developments, in terms of solutions challenging the open problems faced in UASN. Current improvements in UAC and networking based overview are discussed in [9]. T-Lohi is a new class for DEEMAC protocols for UWSN and this is introduced in [10] where this MAC faces some significant challenges. A random access based MAC and hand-shaking based MAC„s modern characteristics and the effect is investigated in [11]. Also two kinds of solutions are provided for these two MAC protocols and it is used for increasing the nodal throughput and collision probability models representing the solutions. An adaptive underwater acoustic modern network can change their parameters due to the situation [12]. Investigation of Mobi-Cast problems in 3D UASN is overcome by providing in-time solutions and the energy is also saved while data collection in the sensor node [13]. In [14], the half-duplex problem is monitored and the system performance is improved. The issues like ranging, energy saving, deployment and routing based problems are analyzed and necessary corresponding solution is provided in [15] for UAWSN. The location spoofing is investigated by studying the vulnerabilities of geographic routing in UANs and a secured routing protocol are designed for UAWN is introduced in [16].

EXISTING APPROACH The existing approach ACO – [Ant Colony Optimization] provides energy aware, data gathering routing mechanism in WSN where it helps avoiding congestion, fast transmission, and energy saving in individual nodes. It provides multipath based data transmission for broadcasting. The objective is to have an efficient data transmission [1].

PROPOSED APPROACH The aim of the OEERP is to save the energy and increase the network lifetime with better throughput and it can be obtained by electing the next neighbor one who is having highest energy and the distance from source node is less than other neighbor nodes. The neighbor selection and data routing is done by AFISHS optimization algorithm. Efficiency of the paper is evaluated by comparing FISH algorithm with ACO algorithm.

30592


6 1

7

2 3

1 0 0

5 4

9 8

7 2 0

1 1 Fig.1: Proposed Network Model0

ANT COLONY OPTIMIZATION To achieve a global goal, many simple agents are used for local interaction in Swarm Intelligence, which is used to solve different kinds of problems and it is based on social insect metaphor, such as bees, ants and termites which live in colonies. They belong to a social insect with colony, and have their own agenda with no supervisor to monitor and integrate all the individual activities. Each individual is specialized in a set of tasks and all individual do not perform all tasks simultaneously. According to the specialization of individual labor the task can be performed sequentially and efficiently instead of unspecialized individuals lacking in completing the whole task. Indirect communication among nodes through simple agents is introduced in [17] where this communication provides, distributed, robustness, flexibility and direct as well as indirect communication. Since the agents are self-directed entities, the reactive and pro-active and having capabilities to adopt, co-operate and they can move intelligently and smoothly in the network communication. The main idea behind the ACO [finding feasible solution in a hard problem] is obtained from the food penetrating behavior of the original ants. There are two types of ant agents classified as FANT and BANT. The essential reason for separating the executors is to permit the BANT, to use the helpful data accumulated by FANTs on their excursion time from source to end. As indicated by this standards no hub steering data redesigns are performed by FANT. It‟s just significant reason in life is to report system deferral conditions to BANT.


Fig.2: Shortest Path Obtained by Ants Pseudo code for ACO 1. 2. 3. 4.

Initialize the Ants using software agents Make ant move by selecting the next Update the previous move and go ahead for the next move using probability to move the ant

ALGORITHM: ACO 1. Initialize a random population P, rep, solution S 2. is the network Where 3. Initialize pheromone as the path 4. While termination conditions not met do 5. P = { } 6. For 7. 8. = localSearch(S) where 9. P = P 10. End for 11. Update Pheromone(P) 12. Output: the path found NUMERICAL ILLUSTRATION 1. Let population

number of nodes

2. 3. Let 4. Let node 3 be the source node, node 7 be the destination node

30593

30594


5. 6. Example 7. 8. 9. 10. Change the 11. Else 12. 13. 14. EXAMPLE: COMBINATION OF PATHS

The Best Path = {1, 5, 9, 12}.

ARTIFICIAL FISH SWARM OPTIMIZATION ALGORITHM The motivation for the focus of AFSA is to simulate the behavior of fish such as preying, swarming, following and moving. To reach the global optimum in searching can be obtained with native hunt of fish individuality. Searching is random and parallel. It is observed that the swarm intelligence can be combined with artificial intelligence can bring the best solutions for optimization problems [21]. The relevant definition is given below to understand the AFSA in better manner. ARTIFICIAL FISH [AF] It is a true fish considered as an entity to carry out the problem explanation and analysis. It is used in the field of animal ecology. In this paper, with the help of Object oriented programming structure, the AF encapsulates its behavior and own data. Also it can intellect organs and stimulant feedback by the adjustment of tail and fin, which can be used to accept amazing information of environment. The fish living in the environment is the main result space and the conditions of the AF. The energy of the fish determines the fish state-owned and its ecological state. This can also influence the environment via its own activity and other nearest friend's [other AF] activities. The following Figure-1 shows the environment and AF‟s states with the visual perception [visual distance from its current position.


30595

Xi Offset

Visual Perception

X Xv

Fig.3: Artificial Fish Environment From the above Figure-1, it is noted that the Fish is in the initial state. It swarms in a fixed offset distance. The is the next random Fish neighbors. is the fish located with the visual distance. The mathematical representation of the AF environment can be written as: and

Where,

,

And where Where Rand() provides a sequence of random numbers among 0 to 1. Offset is the fixed step value of fish moving length. The Visual indicates the visual distance in the environment. is the iteration number and is the fish crowd in a particular place where there are four fish behaviors such as AF_Prey, AF_Swarm, AF_Follow and AF_Move. The two main parts of AFA are variables and functions.

30596


Here, X, Energy and swarm, move, follow are the functions. An amount of energy is reduced according to the fish behavior. AF_Prey This is a crucial biotic conduct that keeps an eye on the sustenance; generally the fish sees the hoarding of sustenance in water to center the advancement by vision or intellect and thereafter picks the slant. Conduct depiction: Let

be the

current state and select a state

heedlessly in its visual detachment,

is the food center (objective limit regard), the more significant Visual is, the more successfully the astonishing regard and merges

If

finds the overall

, it moves forward a step in the same direction.

Else, pick a state , haphazardly at the end of the day and choose whether it fulfills the forward condition or not. On the off chance that it is not ready to fulfill then attempt with different numbers after times and moves a step haphazardly. In the event of is little in AF_Prey, Else, pick a state , haphazardly at the end of the day and choose whether it fulfills the forward condition or not. On the off chance that it is not ready to fulfill then attempt with different numbers after times and moves a step haphazardly. In the event of is little in AF_Prey, the can swim haphazardly, and it makes it escape from the nearby compelling worth field.

AF_Swarm The fish will hoards in social events customarily in the moving philosophy, which is a kind of living inclinations to confirm the vicinity of the area and avoid dangers. Conduct delineation: Let be the AF current state, be the center position and be the measure of its associates in the current neighborhood ( ), is total fish number. In case and , which suggests that the sidekick center has more sustenance (higher wellbeing limit regard) and is not uncommonly assembled, it proceed a dare to the companion center.


30597

Generally, executes the preying conduct. The swarm variable restrains the scale of swarms, and more AF just bunch at the best territory, which guarantees that AF move to ideal in a wide field. AF_Follow In the moving procedure of the fish swarm, when a solitary fish or a few ones discover sustenance, the area accomplices will trail and achieve the nourishment rapidly. Conduct depiction: Let be the AF present state, and it investigates the sidekick in the area ( ), which has the best . On the off chance that and , which implies that the friend state has higher sustenance fixation and its‟ encompassing is not extremely gathered, it goes ahead a venture to the buddy ,

Else it implements the predatory manners. AF_Move Fish swim haphazardly in water; in fact, they are looking for sustenance or buddies in bigger reaches. Conduct depiction: Chooses a state at irregular in the vision, then it moves towards this state, actually, it is a default conduct of AF_Prey.

AF_Leap Fish stops at some place in water, each AF's conduct result will bit by bit be the same, the distinction of target qualities (sustenance focus, FC) get to be more diminutive inside a few cycles, it may fall into neighborhood compelling change the parameters arbitrarily to the still states for jumping out ebb and flow state. Conduct portrayal: If the goal capacity is practically the same or distinction of the destination capacities are more diminutive than an extent amid the given emphasess, Chooses some fish arbitrarily in the entire fish swarm, and set parameters haphazardly to the chose . The is a parameter or a capacity that can makes some fish have other strange activities (values), is a more diminutive steady.

30598


Swarm makes few fish bound in nearby great qualities move toward a couple of fishes having a tendency to worldwide compelling worth, which brings about escaping from the neighborhood amazing qualities. AF_Follow Accelerates moving to better states, and in the meantime, quickens moving to the worldwide great quality field from the nearby amazing qualities. ALGORITHM 1. Let P be the random population and R be a number of repetition 2. Let s be source node and d be the destination node 3. For j=1 to R 4. For I = 1 to p 5. Select all neighbor node from source to destination direction 6. Choose the appropriate neighbor and check the distance, energy 7. For k = 1 to 3 8. If ( 9. Else 10. Eliminate the node 11. Move to the next node in the same direction k 12. Move in the direction k 13. End 14. End i 15. End j 16. For I = 1 to length(RoutNode()) 17. Follow(i) 18. Construct path 19. Transmit data 20. End i NUMERICAL ILLUSTRATION Population P =50 Select P=10 Elect S as source node-1, D as destination node-7 Let the population first level are 2, 3, 5, 6 Compute the distance from 1 to 2, 1 to 3, 1 to 5 and 1 to 6 Check the energy among 2, 3, 5 and 6 and choose the node-6 having highest energy Add the pair as RoutNode={ 1, 5,..} Eliminate 2, 3 and 5 Move forward Choose directions N, N-E, E, S-E towards Node-7 P=20


30599

And repeat the above steps until reach 7, and obtain a shortest path with highest energy based nodes in the path Best Path obtained with less distance and more energy is Path = {1, 5, 9, 7}

SIMULATION RESULTS The complete functionality of OEERP is coded in NS2 and the results are given here to verify the performance OEERP in terms of energy and throughput. To evaluate the performance of OEERP the performance metrics such as throughput, time taken for route discovery and remaining energy are compared with the existing routing protocols TMR and NCMR. To conclude the efficiency the simulation is repeated for a number of times with different number of nodes deployed in the network like 10, 20, 30, to 100.

Fig.4: OEERP vs. TMR, NCMR Comparison in terms of Throughput The figure 5, shown above indicates the number of packets received with respect to number of nodes deployed in the network. It is observed from the figure 5 that proposed OEERP exhibits a better performance than that existing TMR and NCMR. For example when the number of nodes deployed is 10 to 100, the TMR has received 5345 packet and NCMR has received 5500 packet, where as the proposed OEERP has received 5700 number of packet when 100 number of nodes are deployed in the final round, which is much above the existing TMR and NCMR. The improved performance is achieved due to the fact that it identifies the neighbor discovery technique for highest energy and hence could obtain more number of packets. Hence, it is proved that the proposed OEERP outperforms the existing TMR and NCMR.

30600


Fig.5: OEERP vs. TMR, NCMR Comparison in terms of Optimized Routes The performance of proposed OEERP for optimum route identification time with respect to number of route is presented in Figure 6. This is obtained by assessing the time taken by each protocol to find the optimum route among the available routes in the network. Form the observation, it is determined that the proposed OEERP could fix the optimum route with shorter duration of time than the NCMR or TMR protocols. For example in a network several number of different routes are available, the time required to find the optimum path by TMR is 411 ms, NCMR is 360 ms where as the proposed OEERP could able to fix the optimum path within 340 ms when 100 number of nodes deployed in final round of the simulation. Hence the proposed OEERP identifies the optimum path for data transfer with shortest duration than the existing protocols. Figure-7, shows the remaining energy computed by different protocols from various rounds. In this figure it is evidently proved that OEERP saves highest energy than the existing protocol TMR and NCMR in each round of simulation.

Fig.6: OEERP-Performance Evaluation in Terms of Remaining Energy


30601

For example, the network with 100 nodes deployment, TMR has got 35.11% energy; NCMR has 36% energy remaining. However, OEERP is having highest energy saving of 59.6%. This improved performance is the outcome of efficient protocol which determines the energy efficient neighbors and excellent data transfer performance.

CONCLUSION The proposed OEERP has successfully been simulated with presumed parameters to determine the performance in terms of throughput, optimal route identification and energy utilization. It is proved that the OEERP excels over the existing TMR and NCMR. It is also recorded that the proposed OEERP achieves highest number of packet received as 6500 against 2900 in TMR and 4100 in NCMR. Similarly, the time required to fix the optimum path is also found to be only 75 ms whereas the existing TMR and NCMR took 150 and 200 ms respectively. Further, the proposed OEERP also recorded a comparatively high amount of remaining energy of 46% over 6% and 12% remaining energy levels in TMR and NCMR. It is therefore recommended that the proposed OEERP s a best protocol for the efficient data transmission with less amount of energy utilization and optimum path identification. In Future, OEERP can also be compared with upcoming new proposed techniques and prove the efficacy.

REFERENCES [1]. [2].

[4]. [5].

[6].

[7].

[8].

[9].

V. Parthasarathy, S.A.Kalaiselvan, P.Hemalatha,“ Performance Evaluation of FMMS using underwater sensor networks”, ICBEC -2014, USA. K.Syed Ali Fathima, Mr.K.Sindhanaiselvan, “Ant Colony Optimization Based Routing In Wireless Sensor Networks”, Int. J. Advanced Networking And Applications Volume: 04 Issue: 04 Pages: 1686-1689 (2013). Ethem M. Sozer, MilicaStojanovic, and , “Underwater Acoustic Networks”, IEEE Journal Of Oceanic Engineering, VOL. 25, NO. 1 ZaihanJiang , “Underwater Acoustic Networks – Issues And Solutions”, International Journal Of Intelligent Control And Systems VOL. 13, NO. 3,152-161 DarioPompili and Tommaso Melodia,“Three-Dimensional routing In Underwater Acoustic sensor Networks”, IEEE Transactions On Wireless Communications, VOL. 9, NO. 9. Min Kyoung Park AndRodoplu, “UWAN-MAC: An Energy-Efficient MAC Protocol For Underwater Acoustic Wireless Sensor Networks”, IEEE Journal Of Ocean Engineering, VOL.32, NO.3. Ian F. Akyildiz, Dario Pompili, Tommasomelodia, “Challenges For Efficient Communication In Underwater Acoustic Sensor Networks”, Published by ACM,VOL 1 Issue 2,July 2004. Fei Hu, Paul Tilghman,Yaminmalkawi, “A Prototype Underwater Acoustic Sensor Network Platform With Topology-Aware MAC Scheme”, International Journal Of Sensor Networks.

30602 [10]. [11]

[12]

[13]

[14]

[15]

[16]

[17].

[18].

[19].

[21].

S.A.Kalaiselvan et al Anujsehgal, Iyadtumar, Jürgen Schönwälder, “Aquatools: An Underwater Acoustic Networking Simulation Toolkit” IEEE Oceans 2010 . Mandarchitre, Shiraz Shahabudeen, Lee Freitag, Milica Stojanovic3, “Recent Advances InUnderwater Acoustic Communications &Networking “IEEE Oceans, Quebec city Conference, 2008. Marco Conti, Silvia Giordano AndIvan Stojmenovic, “ADVANCES IN UNDERWATER ACOUSTIC NETWORKING” The Institute Of Electrical And Electronics Engineers. Published 2013 By John Wiley &Sons, Inc Affan A. Syed, ,Wei Ye, John Heidemann, “Comparison And Evaluation Of The T-Lohi MAC For Underwater Acoustic Sensor Networks”, IEEE Journal On Selected Areas In Communications, VOL. 26, NO. 9 Yibo Zhu, Zaihanjiangy, Zhengpeng, Michael Zuba, Jun-Hong Cuiandhuifangchenz, “Toward Practical MAC Design For Underwater Acoustic Networks”INFOCOM 2013, IEEE proceeding. Lingjuan Wu, Jennifer Trezzo, Dibamirza, Paul Roberts, Jules Jaffe, Yangyuan Wang AndRyan Kastner, “Designing An Adaptive Acoustic Modem For Underwater Sensor Networks”IEEE Trans ,Embeded Systems,2012. Yuh-Shyan Chen And Yun-Wei Lin, “Mobicast Routing Protocol For Underwater Sensor Networks”, IEEE Sensors Journal, VOL. 13, NO. 2, FEBRUARY 2013 Yibo Zhu, Jun-Hong Cui, Zhongzhouy, Zhengpeng, Huifangchenz, ““Busy Terminal Problem” And Implications ForMAC Protocolsin Underwater Acoustic Networks”, WUWNet‟12, Nov. 5 - 6, 2012 Los Angeles, California, USA, JULY 2013. Amanpreet Singh Mann, Reenaaggarwal, “Review On An Underwater Acoustic Networks “,International Journal Of Advanced Research In Computer Engineering &Technology (IJARCET) Volume 2, Issue 2, February 2013 Michael Zuba, Michael Fagan, Jun-Hong Cui AndZhijie Shi, “A Vulnerability Study Of Geographic Routing In Underwater Acoustic Networks”, IEEE Conference On Communications And Network Security 2013 [20].M.Jian, Y.Wang, F.Rubio, D.Yuan, ” Spread Spectrum Code Estimation By Artificial Fish Swam Algorithm Block-Coding And Antenna Selection, ”IEEE International Symposium On Intelligent Signal Processing (WISP), October 2007. Lei Wang, Yuwang Yang* and Wei Zhao, “Network coding-based multipath routing forenergy efficiency in wireless sensor networks”, EURASIP Journal on Wireless Communications and Networking 2012.