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Abstract This paper investigates the proposition of a new routing protocol for wireless sensor networks called TSRP (Tabu Search based Routing Protocol).
Wireless Pers Commun DOI 10.1007/s11277-011-0367-7

Routing Protocol Based on Tabu Search for Wireless Sensor Networks Fouzi Semchedine · Louiza Bouallouche-Medjkoune · Leila Bennacer · Naim Aber · Djamil Aïssani

© Springer Science+Business Media, LLC. 2011

Abstract This paper investigates the proposition of a new routing protocol for wireless sensor networks called TSRP (Tabu Search based Routing Protocol). In this protocol, we use the mechanism of the meta-heuristic Tabu search to route the data from the sensor (that has sense the events) to the sink. This mechanism is used to select the next sensor that will route the data based on a cost function (considering the energy and the visibility of this sensor compared to the sink). Simulation results, on a car parking application, show that TSRP prolongs the network lifetime than the existing protocols. Keywords Wireless sensor networks · Routing protocols · Tabu search · Discrete events simulation · Environmental applications

1 Introduction The development of wireless and mobile networks opens a new area in the telecommunication domains. The wireless communication became one of the most recognized technologies. It offers open solutions to provide crucial services where the installation of cable based infrastructure is impossible or mainly difficult. However, to make this type of communication attractive, flexible and with fewer infrastructures, a new generation of networks, called wireless sensor networks, appears. These sensors contain devices of sensing and wireless communication in only one circuit, with system on chip design and cost-effectiveness. Nevertheless, one of the major problems in this type of network is the energy consumption. The emission and the reception of the packet, at the time of the communication, is a costly process in term of energy. Then, the routing protocols must preserve the energy of the sensors when communicating the data to the sink. Several protocols of different classes were proposed in the literature to deal with the problem of the energy consumption such as: the

F. Semchedine (B) · L. Bouallouche-Medjkoune · L. Bennacer · N. Aber · D. Aïssani LAMOS Laboratory, University of Béjaïa, Béjaïa 06000, Algeria e-mail: [email protected]

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linear routing [5,7,15–17,21,22,25], the hierarchical routing [6,9,8,10,20,23,24], the localization based routing [13,22], the QoS based routing [18], the multi paths routing [5,17,21], the negotiation based routing [5,18,25] and the flow control based routing [8,17]. However, the problem of conservation of energy to prolong the network lifetime remains the major challenge when proposing an efficient routing protocol. In this paper, we propose a new routing protocol called Tabu Search base Routing Protocol (TSRP). In TSRP, we introduce a mechanism of the meta-heuristic Tabu Search. The idea is to use this mechanism to route the data from the sensor (that has senses the event) to the sink. Thus, when a sensor senses an event, the choice of the next sensor is based on maximizing the cost function which considers the energy and the visibility of this sensor compared to the sink. Simulation results showed that TSRP prolongs the network lifetime than the protocols Gossiping and MFR. The remainder of the paper is organized as follows: Sect. 2 reviews the conception of the new protocol TSRP. In Sect. 3, we discuss the simulation results and we conclude the work in Sect. 4.

2 The TSRP Protocol At the aim of proposing an efficient routing protocol, in term of energy consumption, we adapt the mechanism of the meta-heuristic Tabu Search on the routing for the wireless sensor networks. Hence, we propose the protocol TSRP (Tabu Search based Routing Protocol). The meta-heuristic Tabu search was proposed in the middle of the 80th by Fred Glover to resolve some combinative problems. It is an advanced local research method that uses rules and some mechanisms to lead toward the optimal solution. The Tabu search requires an initial solution and a structure of neighbors. This method proceeds by moving from a solution to another. For each iteration, all the possible solutions are inspected and the “best” is selected. In this manner, we risk to have a cycle in which we repeat the same solutions. So, the last t solutions where considered prohibited “Tabus” (where t is the length of the Tabu list) [3]. Although Tabu Search was introduced in the sensor networks to deploy the sensors and build the clusters [2], in our new protocol TSRP, we introduce the mechanism of the Tabu search for routing in wireless sensor networks. When the sensor receives a packet of the data, it sends it to one of its neighbors, selected by using the cost function that calculates the cost in term of energy and distance from the sink of each neighbor. This cost function, inspired from the Ant Colony method [12] and, that depends to the residual energy and the visibility of all the neighbor sensors compared to the sink, defines the probability, for one packet of the sensor i, to choose a sensor k about the neighbors Ni : F(k) = 

ER(k) ∗ V (k) n∈N (ER(n) ∗ V (n))

(1)

where, ER(k) : is the residual energy of the sensor k V (k) : is the visibility of the sensor k, which is equal to: V (k) =

1 Dk

where, Dk : is the Euclidian distance between the sensor k and the sink.

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(2)

Routing Protocol Based on Tabu Search

Thus, the aim is to maximize the cost function. Hence, the choice will be based on the sensor which has the highest cost (high residual energy and nearest to the sink), and this last will be the next sensor to be reached (designed as j). To avoid that the sensor i will be revisited during the same process of routing data, a boolean variable noted tabu (initialized to false) is associated with each sensor of the network. This variable is used to prohibit the choice of a sensor having already been visited (avoided the cycles). Thus, each time a sensor i sends the packet of data it sets its variable tabu to true. Once the packet reached the sink, each sensor (which belongs to the path routing) sets its variable tabu to false (i.e. preparing for the next event).

3 Simulation Results In order to evaluate the performance of the protocol TSRP, we implement this last on a discrete events simulator developed with C++ language and called SENSIM (SENsor networks SIMulator) [14]. Comparisons will be performed with two routing protocols already implemented on this platform, which are: – Gossiping [1] is a variant of Flooding technique [a naïve mechanism where each sensor receiving a data packet broadcasts it to all its neighbors until the packet reached its maximum of hops (flooding the network)]. In Gossiping, a sensor receiving a packet does not broadcasts it to all its neighbors, but send it to one of the neighbors (selected randomly). Indeed, each sensor in the network chooses randomly one sensor from its neighbors and sends it the received packet, and so on until the packet reaches the sink. – MFR (Most Forward within Radius) [19] is the first localization based protocol. In MFR, each sensor receiving the packet sends it to one of its neighbors selected according to its position in which the orthogonal projection is closest to the position of the sink. This process continues until the packet reached its destination (the sink). The implementation of the protocols is based on a car parking application. This parking has 359 of parking place and one agent at a principal portal. To manage this parking, 539 of sensors will be deployed in all its area (when 359 are sensors parking, and 180 are routing sensors) with one sink. These components are deployed in a grid form. The components of the system are: – The sensors: there are two kinds of sensors: (a) parking sensors: each parking place will be affected by one sensor that senses the parking and the departure of the car; (b) routing sensors: they are used to sense the possible breakdowns of cars and participate in the routing process. – The sink: placed at the principal portal. It is an interface between the network and the agent and, it is responsible for gathering the data of the network. – The agent: it collects the data from the sink to manage the parking. – Messages: the sink and the sensors exchange periodically messages. – Cars: they are the clients of the system that require parking places when reach the system. The implementation of the protocols is based on a discrete events simulation. The events which can occur during the time and which causes state changes of the descriptive variables of the system model are presented in the Table 1.

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F. Semchedine et al. Table 1 Discrete events of the system Event

Description

Arrival

Parking

This event occurs when a car arrives at the principal portal. The agent reserves a place and records some information about the car (for example: car identifier). The car leaves the system when all the places are assigned. This event occurs when the parking sensor senses a car.

Departure

This event occurs when the car leaves its parking place.

Breakdown

This event occurs when the routing sensor senses a breakdown of a car.

Leave

This event occurs when the car has starting problems.

Table 2 Simulation parameters Constant definition

Initial value

Type

Measure unit

Initial energy of the sink

Unlimited

Real

Joule

Initial energy of the sensor

1

Real

Joule

Amplification Factor

10

Integer

pJ/bit/m 2

Processing energy

5

Integer

nJ

Sensing energy

50

Integer

nJ

Radio zone

4

Real

M

Sensing Zone (length of the parking place)

2

Real

M

Sink position

(26,0)

Integer



Maximum number of sensors

539

Integer



Message length

256

Integer

Bit

Time of simulation

220,000

Integer

Minute

Arrival rate

0.5

Real

Arrival/Second

Table 3 Simulation results of the protocols

Protocol

Network lifetime

Gossiping

6 days 16 h 51 min

MFR

66 days 0 h 13 min

TSRP

104 days 3 h 58 min

Table 2 summarizes the various parameters of simulation inspired from previous works in simulation of sensor networks [4,11]. The parameter of evaluation of the Gossiping, MFR and TSRP protocols is the network lifetime. It is the interval of time that separates the time of the deployment of the network from the time of disconnection of the network. For ease of analysis we consider that the network disconnects when one sensor in the network exhausts totally its energy. The simulation results of the different protocols are summarized in Table 3. 3.1 Discussion The Gossiping protocol uses 82% of the average energy of the network at a number of events equal to 14,000. Its network lifetime is estimated by 6 days, 16 h and 51 min. Gossiping

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Routing Protocol Based on Tabu Search

protocol uses just some sensors (but a large part) of the network to route the packets. This means that one sensor can participate several times in the routing process for a given event which exhausts quickly its energy and deteriorates the performance of the protocol. MFR uses only 4% of the average energy of the network at a number of events equal to 140,000. Its network lifetime is estimated by 66 days, 0 h and 13 min. The amount of energy used by MFR is very low compared to that of the other protocols. This is due to the fact that MFR uses a minimum and a sufficient number of sensors to route the data (in MFR each sensor sends the packet to the sensor nearest to the sink). However, by using the algorithm of MFR, the sensors that are located around the sink exhaust quickly their energy. TSRP uses 12% of the average energy of the network at a number of events equal to 220,000. Its network lifetime is estimated by 104 days, 3 h and 58 min. This rate of consummation of energy is due to the control messages (requesting the residual energy, sending the residual energy) exchanged between the sensors to calculate the cost function and routing the data. However, the network lifetime, for TSRP, is the best one in a wide range. This is due to the best management of the energy consumed by the sensors of the network. Indeed, in TSRP, each sensor sends the data to the sensor which has a high residual energy and nearest to the sink.

4 Conclusion In this paper, we have proposed a new routing protocol, called TSRP (Tabu Search based Routing Protocol), for wireless sensor networks. In this protocol, we used a meta-heuristic Tabu Search to route a data by considering the residual energy and the distance, from the sink, of the sensors. Simulation results showed that TSRP prolongs the network lifetime than the protocols Gossiping and MFR. This improvement is due to the mechanism of the meta-heuristic Tabu Search where each sensor receiving the packet sends it, based on the cost function, to the sensor that has the high residual energy and nearest to the sink. In future works, we will implement other well-known protocols, for routing in wireless sensor networks, on the simulator SENSIM and the standard Network Simulator 2 (NS2) in order to compare them with the TSRP protocol and highlight its improvement in term of prolongation of the network lifetime. We envisage, also, to implement the protocol in a real car parking. Acknowlegments The authors would like to thank the editor in chief, the editor and the anonymous reviewers for their valuable comments and suggestions.

References 1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks: The International Journal of Computer and Telecommunications Networking, 38, 393–422. 2. El Rhazi, A., & Pierre, A. (2009). Tabu search algorithm for cluster building in wireless sensor networks. IEEE Transactions on Mobile Computing, 8, 433–444. 3. Glover, F., & Laguna, F. (1997). Tabu search. Norwell MA: Kluwer Academic Publishers. 4. Heinzelman W. B., Chandrakasan A. P., & Balakrishnan H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Hawaii international conference on system sciences. Maui, Hawaii (pp. 3005–3014).

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F. Semchedine et al. 5. Intanagonwiwat, C., Govindan, R., & Estrin, D. (2000). Directed diffusion: A scalable and robust communication paradigm for sensor networks.In Proceedings of the 6th annual international conference on mobile computing and networking. Boston, Massachusetts, United States (pp. 56–67). 6. Kim, H. S., Abdelzaher, T. F., & Kwon, W. H. (2003). Minimum energy asynchronous dissemination to mobile sinks in wireless sensor networks. In Proceedings of the first international conference on embedded networked sensor systems (SenSys 2003) (Vol. 7, pp. 193–204). New York: ACM Press. 7. Krishnamachari, B., Estrin, D., & Wicker, S. (2002). Modelling data-centric routing in wireless sensor networks. In Proceedings of IEEE INFOCOM. 8. Li, Q., Aslam, J., & Rus, D. (2001). Hierarchical power-aware routing in sensor networks. In Proceedings of the DIMACS workshop on pervasive networking. 9. Lin, C. R., & Gerla, M. (1997). Adaptive clustering for mobile wireless networks. IEEE Journal on Selected Areas in Communications, 15, 1265–1275. 10. Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power efficient gathering in sensor information systems. In Proceedings of the IEEE aerospace conference (pp. 1125–1130). 11. Lindsey, S., Raghavendra, C. S., & Sivalingam, K. (2001). Data gathering in sensor networks using the energy delay metric. In Proceedings of the 15th international parallel & distributed processing symposium, San Francisco, CA (pp. 2001–2008). 12. Okdem, S., & Karaboga, D. (2006). Routing in wireless sensor networks using ant colony optimization. In Proceedings of the first NASA/ESA conference on adaptive hardware and systems (Istanbul, Turkey) IEEE computer society (pp. 401–404). 13. Rodoplu, V., & Meng, T. H. (1999). Minimum energy mobile wireless networks. IEEE Journal of Selected Areas in Communications, 17, 1333–1344. 14. Semchedine, F., Bouallouche-Medjkoune, L., Moad, S., Makhloufi, R., & Aïssani, D. (2009). Discrete events simulator for wireless sensor networks. In Proceedings of the third international workshop on verification and evaluation of computer and communication systems (VECoS 2009) (Rabat, Morroco) eWiC series, British Computer Society. 15. Sadagopan, N., Krishnamachari, B., & Helmy, A. (2003). The ACQUIRE mechanism for efficient querying in sensor networks. In Proceedings of the first international workshop on sensor network protocol and applications, Anchorage, Alaska (pp. 149–155). 16. Schurgers, C., & Srivastava, M. B. (2001). Energy efficient routing in wireless sensor networks. In Military communications conference (MILCOM) on communications for network-centric operations: Creating the information force, IEEE (Vol. 1, pp. 357–361). 17. Shah, R., & Rabaey, J. M. (2002). Energy aware routing for low energy ad-hoc sensor networks. In Proceedings of wireless communications and networking conference (WCNC) (pp. 350–355). 18. Sohrabi, K., Gao, J., Ailawadhi, V., & Pottie, G. J. (2000). Protocols for self-organization of a wireless sensor network. IEEE Personal Communications, 7, 16–27. 19. Takagi, H., & Kleinrock, L. (1984). Optimal transmission ranges for randomly distributed packet radio terminals. IEEE Transactions on Communications, 32, 246–257. 20. Xu, Y., Heidemann, J., & Estrin, D. (2001). Geography-informed energy conservation for ad-hoc routing. In Proceedings of the 7th annual international conference on mobile computing and networking, NY, USA (pp. 70–84). New York: ACM Press. 21. Yang, Z., Dong, M., Tong, L., & Sadler, B. M. (2004). On the MAC for optimal information retrieval pattern in sensor networks with mobile access. IEEE military communications conference (MILCOM 2004) (pp. 232–237). 22. Yao, Y., & Gehrke, J. (2002). The cougar approach to in-network query processing in sensor networks. ACM SIGMOD Record, 31, 9–18. 23. Ye, F., Luo, H., Cheng, J., Lu, S., & Zhang, L. (2002). Sensor networks : A two-tier data dissemination model for largescale wireless sensor networks. In Proceedings of the eighth annual ACM/IEEE international conference on mobile computing and networks (MOBICOM 2002) (pp. 23–28). 24. Younis, M., Youssef, M., & Arisha, K. (2002) Energy-aware routing in cluster-based sensor networks. In Proceedings of the 10th IEEE international symposium on modeling, analysis and simulation of computer and telecommunications systems (MASCOTS 2002) (pp. 129–136). 25. Zhao, F., Liu, J., Liu, J., Guibas, L., & Reich, J. (2003). Collaborative signal and information processing: An information directed approach. In Proceedings of the IEEE (Vol. 91, pp. 1199–1209).

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Author Biographies Fouzi Semchedine is currently an Assistant Professor at the University of Béjaïa, Algeria. He just received his Ph.D. degree in computer science from the University of Béjaïa (Algeria) and he received his Master degree in 2007 in computer science from the University of Béjaïa, (Algeria). He is a member of a research group for the project: “Performance Evaluation in Computer Science Systems (Distributed Web Server Systems and IEEE 802.11 Protocol)”. He worked in the area of Quality of Service in distributed Web server systems for studying and modifying existing algorithms in local and distributed Web server systems which contribute to improve the performance of such systems. He interested, also, to the area of sensor networks when many of works were done for routing in such networks in order to conserve energy.

Louiza Bouallouche-Medjkoune is currently an Associate Professor at the University of Béjaïa, Algeria. She was received the engineer degree in computer science from the University of Sétif (Algeria) and the Master Degree in applied mathematics from the University of Bejaia (Algeria). She received her Ph.D. in 2006 in computer science from the University of Sétif (Algeria). She works as a teacher at the department of Computer Science at the University of Bejaia (for Data Structures, Programming and Algorithmic, Performance evaluation, Queuing Theory and Markov chains, Simulation of Systems and Networks, Seminar on Performance Evaluation of Networks and Systems), and as a researcher at the laboratory LAMOS of the University of Bejaia. Her research interests are in: Performance Evaluation of Computer systems and telecommunication networks, Stability of Systems, Markov chains, Queuing Theory, Computer Networks (wired, wireless), Quality of Service of Networks and Systems, Routing and Protocols.

Leila Bennacer is currently a Ph.D. student at the laboratory of images, signals and intelligent systems, France. She works on the autonomous networks. She received her Engineer Degree in 2008 in computer science (Networking and Distributed Systems option) from the University of Béjaïa (Algeria). Its engineer work was on routing in wireless sensor networks.

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F. Semchedine et al. Naim Aber is currently a Ph.D. student at the computer science laboratory of Paris – North. He work on the verification of the temporised systems. He received his Engineer Degree in 2008 in computer science (Networking and Distributed Systems option) from the University of Béjaïa (Algeria). Its engineer work was on routing in wireless sensor networks.

Djamil Aïssani was born in 1956 in Biarritz (Basque Country, France). He started his career at the University of Constantine in 1978. He received his Ph.D. in 1983 from Kiev State University (Soviet Union). He is at the University of Béjaïa since it’s opened in 1983/1984. Director of Research, Head of the Faculty of Science and Engineering Science (1999—2000), Director of the LAMOS Laboratory (Modelling and Optimisation of Systems), Scientific Head of the Doctoral Computer School (since 2004), he has taught in many universities (Algiers, Annaba, Rouen, Dijon, Montpellier,…). He has published many papers on Markov chains, queuing systems, reliability theory, performance evaluation and their applications in such industrial areas as electrical networks and computer systems. He was the president of the national Mathematical Committee (Algerian Ministry of Higher Education and Scientific Research).

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