Analysis of the Scalability and Stability of an ACO Based Routing Protocol for Wireless Sensor Networks

Transcription

1 th International Conference on Information Technology - New Generations Analysis of the Scalability and Stability of an ACO Based Routing Protocol for Wireless Sensor Networks Kashif Saleem Abdelouahid Derhab Center of Excellence in Information Assurance (CoEIA) King Saud University (KSU), Riyadh Kingdom of Saudi Arabia (KSA) {ksaleem, abderhab}@ksu.edu.sa Mehmet A. Orgun Intelligent Systems Group (ISG) Department of Computing Macquarie University NSW 2109, Australia [email protected] Jalal Al-Muhtadi Center of Excellence in Information Assurance (CoEIA), College of Computer and Information Sciences (CCIS), King Saud University (KSU), Riyadh, Kingdom of Saudi Arabia (KSA) [email protected] Abstract Wireless Sensor Networks (WSNs) are often deployed in remote and hostile areas and because of their limited power and vulnerability, the sensors may stop functioning after sometime leading to the appearance of holes in a network. A hole created by the non-functioning sensors in turn severs the connection between one side and the other side of the network and alternative routes need to be found for the network traffic. Prior research tackled the holes problem only when packets reach some nodes near the hole. In this case, the feedback packets are generated and accordingly the data packets need to be rerouted to avoid the holes. The traffic overhead for rerouting consumes additional battery power and thus increases the communication cost as well as reducing the lifetime of the sensors. To deal with the dynamical changes in network topologies in an autonomous manner, ant colony optimization (ACO) algorithms have shown very good performance in routing the network traffic. In this paper, we analyze the scalability and stability of the ACO-based routing protocol BIOSARP against the issues caused by holes in WSNs. Network simulator 2 (ns-2) is utilized to perform the analysis. Findings clearly demonstrate that BIOSARP can efficiently maintain the data packet routing over a WSN prior to any possible holes problems, by switching data forwarding to the most optimal neighboring node. Keywords Autonomous; Energy; Fault tolerance; Holes Issues; Routing protocols; Scalability; Wireless sensor networks I. INTRODUCTION Wireless sensor networks (WSNs) are meant to work independently and intelligently, because these kind of networks are mostly deployed in areas that are out of human reach. Generally, most low-power wireless networks usually have unreliable links with limited bandwidth and their link quality can also be heavily influenced by environmental factors [1]. In WSNs, network anomalies such as holes are caused by the limited power and limited lifetime of the deployed sensors are among the most common and critical problems. Regions effected by the holes are a set of nodes or an area that prevent data from being transferred from one side of network to another side. The holes appear due to inactivity periods, vulnerability to destruction and battery power depletion, link quality, network attacks, physical disasters, etc. [2, 3]. Most commonly known types of the holes problem are jamming holes, sink/black holes/worm holes, coverage holes and routing holes [2]. Due to the holes problem the sink node could not receive important information and that effects overall communication of the network and in some cases even the complete network goes offline, which results in huge loss [4]. Nature inspired algorithms [5] are shown to provide highly robust, self-organizing and autonomous systems to tackle holes problems in WSN. Routing protocols designed for WSNs should intelligently distribute the energy over the network to provide maximum lifetime with efficient network performance [2]. In biological inspired autonomous systems ACO algorithms are a class of constructive meta-heuristic algorithms that mimic the cooperative behavior of real ants to achieve complex computations and have been proven to be very efficient to many different discrete optimization problems [6]. Ant Colony Optimization (ACO) is widely used to solve difficult combinatorial optimization problems such as the ACO meta-heuristic, the quadratic assignment problem (QAP), and the traveling salesman problem (TSP) [7]. Due to nature inspired characteristics of the ACO algorithms, such as collaboration, cooperation, distributed computation and stochastic search, ACO is particularly suitable for scalability, robustness and suitability for dynamic environments. Based on the aforementioned advantages, ACO algorithms have received much attention in network applications. In this paper, we show that the recent improved ACO based Biological Inspired Secure Autonomous Routing Protocol (BIOSARP) [8] for WSNs can overcome the above mentioned holes problems [9]. In [8, 9], the authors have developed an improved ant colony optimization (IACO) algorithm to acquire optimal route decisions and artificial immune system (AIS) to detect abnormalities. The autonomous routing protocol BIOSARP depends on the probabilistic value called as pheromone value of neighboring nodes stored in a routing table. The pheromone value is calculated based on the Sincerest gratitude to Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh, Kingdom of Saudi Arabia /15 $ IEEE DOI /ITNG

2 remaining battery power, end to end delay, and packet reception rate (PRR) based on signal to noise ratio (SNR). These parameters are updated periodically in maintaining the regional knowledge in the network. BIOARP has shown an improved performance as compared to LQER, MM-SPEED, RPAR, RTPC, and RTLD [10]. With respect to security, BIOSARP has less processing time, and provides more efficient security measures over TinySec-AE, TinySec-Auth, TinyHash, LBRS-Auth, EBSS, SRTLD, and SPINS [11]. However, BIOSARP has not been studied extensively under networks anomalies such as the holes problem. Previously, we have analyzed the behavior and performance of BIOSARP in [12] to tackle the holes problems with and without a feedback mechanism. The analysis has shown that BIOSARP can reroute the network traffic effectively when the holes appear in a typical WSN configuration. In this paper, we extend the analysis to the study of the scalability and stability of BIOSARP to handle the holes problem. That is, we study the performance of BIOSARP under network topologies with an increasingly larger number of sensor nodes as well as an increasingly larger number of holes. Finally, the results of BIOSARP are compared with a recent state of the art routing protocol, ERTLD, in terms of packet delivery ratio, battery power, and data packet overhead. The paper is structured as follows. Ssection II reviews the related literature and BIOSARP. Section III presents the approach to the analysis of BIOSARP. The implementation, results and comparison are given in Section IV. Section V states the conclusions and future work. II. RELATED WORK In [13], the authors have used ACO to solve the state justification problem and shown its capability to avoid deadend states in sequential circuits. Later in [14], to reduce the state explosion problem that arises because of deadlocks in complex networks, the authors have used the ACO algorithm. They have evaluated their given technique on several benchmarks; it is shown that the ACO algorithm performs better over other heuristic-based search strategies. In WSNs, the utilization of the ACO algorithm to specifically handle holes problems or dead-ends has been discussed in [12]. The authors have shown that the ACO algorithm is promising in avoiding holes and rerouting the network traffic but it remains to be seen whether the ACO algorithm could scale up to larger networks and/or larger numbers and sizes of holes that are typical in real applications of WSNs. Below we discuss some recent ACO based routing protocols for WSNs. In [15], the author have proposed a routing protocol for Mobile WSN (MWSN) called Enhanced RealTime with Load Distribution (ERTLD), which is based on real-time load distribution (RTLD)[16]. In ERTLD, the author has additionally utilized a corona mechanism to forward the data packets to their destination. The ERTLD protocol has been compared with baseline routing protocols of RTLD, RACE, and MM-Speed [15]. The author claims that ERTLD gives better delivery ratio while minimizing end-to-end delay in MWSN and WSN. Saleem et al. [8] have proposed the improved ACO based routing protocol BIOSARP. The given ACO based routing mechanism reduces the cost of the broadcast function for neighbor discovery at every hop. To avoid a huge traffic overhead, the BIOSARP ACO function is designed with only two agents: Search Ant and Data Ant. The Data Ant selects the next forwarding node based on the pheromone value calculated and stored in the neighbor table. The Data Ant (data packet) moves hop by hop towards the destination by selecting the optimal pheromone values from the neighbor table. The routing management will forward a data packet to the neighbor that has an optimal pheromone value Best p k cv(t). While forwarding, the current node looks for the sink node id in the neighbor table. If the sink node is found, it forwards the data packet to the sink. Otherwise, it looks for an optimal pheromone value in the neighbor table. BIOSARP keeps track of all possible routes over per hop basis via pheromone values stored in the neighbor table of every neighboring node. Moreover, BIOSARP has a security management module on top of autonomous routing [17]. The security module of BIOSARP is based on the artificial immune system (AIS). Preventive measures based on the AIS mechanism [17] and reactive measures are based on a random key encryption mechanism [11]. The BIOSARP routing mechanism is validated by experimentation performed under TOSSIM and later implemented in TinyViz by using 10 TELOSB motes [18]. BIOSARP has shown better performance over the state of art routing protocols for WSNs. The consumption of resources is reduced by avoiding rediscoveries, replies and recalculations. Above mentioned algorithms, except BIOSARP, maintain the best path knowledge by knowing all node ids until the destination and then transfer the data packets directly. In BIOSARP, the improved ACO [8] is adapted to perform autonomous routing in WSNs. BIOSARP, rather than wasting resources on forward and backward agents, starts transferring data hop by hop and deposits pheromone values in routing table on the way. To handle the holes problem, the other routing protocols require huge and complex mathematical computations that generate a huge data packet overhead and consume extra battery power due to the involvement of additional processing. III. APPROACH TO PERFORMANCE ANALYSIS A well-known problem is the failure of finding routes when forwarding data packets in WSN. It occurs due to the existence of holes in the network and can be there even after neighbor discovery. The presence of holes may appear because of the large gaps in between nodes or attacks or due to the inactive nodes. Our objective to analyze the performance of BIOSARP and ERTLD by implementing them in the ns-2 environment. We configure a WSN model in the network simulator 2 (ns-2) with 121 and 49 nodes distributed in 80m x 80m grid topology as shown in Fig.1 (a) and (b). Holes are introduced in the configured WSN by initializing certain nodes with lower energy level and setting some other nodes to perform jamming attacks by sending unauthentic packets as shown in Fig.1 (a) and (b). 235

3 (a) Fig. 1. (a) Small density WSN, (b) Large density WSN (Sink node is in Red, Source nodes are in Blue, and Malicious nodes are in Purple) We examine the behavior of data routing by BIOSARP while increasing the number of nodes causing the holes from 4, 8, and 12 in small density WSNs and from 10, 20, and 30 in large density WSNs. Furthermore, BIOSARP is compared with state of the art routing protocol ERTLD [15] in terms of delivery ratio, energy consumption and data packet overhead. We inspect the state of the routing problem handler to analyze BIOSARP with respect to routing holes as shown in Fig.1 (a) and (b). When a sensor node receives a data packet to forward from its parent, it looks for the sink in its neighbor table. If the sink is found in its neighbor table, the current node forwards the data packet directly to the sink. If the sink node id is not found, then it calculates the next optimal node based on the values available in the neighbor table and forwards the data packet. In the case where the next node could not be found, the neighbor rediscovery is invoked. The neighbor rediscovery function searches other neighboring nodes to find a route to the sink. The holes problem occurs due to a defective region and nodes given in Fig.1 (a) and (b) in purple color. IV. IMPLEMENTATION AND ANALYSIS In this section, the self-adaptive behavior of BIOSARP is explained through simulation implementation and results. The ns-2 based simulation has been conducted to simulate a WSN model with 121 and 49 nodes distributed in 80m x 80m grid topology. MAC and physical layer of IEEE have been embedded in the WSN model to function similar to the MICAz motes. The end-to-end deadline was fixed at 250 ms and (b) simulation time at 400s, while the traffic load has been varied from 1 to 10 packet/s. The network model as shown in Fig. 1 (a) and (b), is configured based on the IEEE standard. All nodes are homogenous except malicious nodes that are enabled and the number of malicious nodes is increased gradually in the next simulation. While simulating the malicious node, they are initialized with 0.9J and some nodes perform jamming attacks periodically. Under large density, 90, 100, 110 and 120 are the source nodes and node 0 is the sink node. Under small density, 37, 43, 31 and 25 are the source nodes and node 0 is the sink node. The simulations are done in six categories that are based on the node density, and in every category the packet rate per second varies from 1.16, 1.86, 4.22, 6.17, 7.3, and 9.6. In the first category, a small density WSN with 49 nodes, simulation is performed with 4 malicious nodes (1, 3, 5, 7), and then in the next category of simulation, 4 more malicious nodes are added (10, 18, 21, 23), and in the third category further 4 malicious nodes are added (20, 12, 15, 13). In the end, there are a total of 12 malicious nodes as shown in Fig.1 (a). Furthermore, the large density WSN is configured with 121 nodes and simulation is performed by involving 10 malicious nodes (22, 4, 14, 6, 16, 2, 24, 8, 7, 5) in the fourth category. In the fifth category simulation, 10 malicious nodes are added (35, 37, 39, 41, 43, 45, 47, 31, 29, 27), and in the last category simulation, 10 more malicious nodes (33, 64, 68, 72, 76, 80, 56, 52, 58, 64) are added to make a total of 30 malicious nodes as shown in Fig.1 (b). A. BIOSARP routing in the presence of holes problems In order to show the effect of BIOSARP, one of the scenarios has been utilized as shown in Fig. 2. Initially, BIOSARP avoids the nodes that are non-self and secondly the nodes with less resources that can cause holes in the near future. While routing data, the next neighboring node or forwarding node is selected, based on the pheromone value calculated through a probabilistic rule. The data packets can flow through a path chosen by BIOSARP; the path is from source 37 to nodes 31, 29, 6, and 0. That means these nodes get the best pheromone values and provide the best performance, as compared to other neighboring nodes. With the continuous usage of the node id 29, the performance declines in terms of the remaining battery power. Fig. 2. BIOSARP switches to node with id 30 at seconds and the blue path continues 236

4 The energy consumption is also very little by BIOSARP as compared to the energy consumption by ERTLD. With respect to data packet overhead, BIOSARP generates very few control packets compared to ERTLD in the presence of 30 malicious nodes. This is because the protocol selects the most optimal neighboring self-node to transfer the data packet towards the destination. Hence, the results demonstrate that BIOSARP can avoid holes in the network and can efficiently route the data packets even in presence of the holes problem Fig. 3. The switching of nodes while routing data DELIVERY RATIO Accordingly, the pheromone value also drops because it is calculated based on the remaining battery power. At the time of forwarding, if node 31 finds a node with a better pheromone value, it selects it as optimal and starts forwarding to that particular node. Similarly, as shown in Fig. 2 and Fig. 3, when the simulation time is seconds, the path changes to nodes 31, 30, 28, 6, and 0. Node 31 starts to forward the data towards node 30, and then node 30 checks its neighbor table and finds node id 28. The data finally reaches to node 0 via node 6. The data forwarding continues on this path, until the parent node finds a node with a better pheromone value in its neighboring table. B. Results Comparison and Discussion This section shows the results obtained from the scenarios implemented in ns-2 and the comparisons. The results in Fig. 4 show that, in a small density network with the maximum number of malicious nodes, BIOSARP gives up to 79% delivery ratio and even in a large density network with the maximum number of malicious nodes, it maintains the delivery ratio of up to 60%. While maintaining the delivery ratio, the energy consumption goes higher as shown in Fig.5, but without any malicious activity (or without any holes), the energy consumption is very little. Moreover, in the presence of malicious nodes, the data packet overhead grows, because of more handshakes and thus more control packets are generated as shown in Fig. 6. Furthermore, BIOSARP is compared with state of the art routing protocol ERTLD to compare their performance. The scenarios are configured with a large density WSN and simulations are conducted in the presence of 30 malicious nodes and as well without any malicious activity. Fig. 7 shows that the delivery ratio given by BIOSARP is far better than ERTLD even in the presence of malicious nodes Fig. 4. Delivery Ratio ENERGY CONSUMPTION Nodes with 0 Malicious 49 Nodes with 4 Malicious 49 Nodes with 8 Malicious 49 Nodes with 12 Malicious 121 Nodes with 0 Malicious 121 Nodes with 10 Malicious 121 Nodes with 20 Malicious 121 Nodes with 30 Malicious Fig. 5. Energy Consumption 49 Nodes with 0 Malicious 49 Nodes with 4 Malicious 49 Nodes with 8 Malicious 49 Nodes with 12 Malicious 121 Nodes with 0 Malicious 121 Nodes with 10 Malicious 121 Nodes with 20 Malicious 121 Nodes with 30 Malicious 237

5 DATA PACKET OVERHEAD Nodes with 0 Malicious 49 Nodes with 4 Malicious 49 Nodes with 8 Malicious 49 Nodes with 12 Malicious 121 Nodes with 0 Malicious 121 Nodes with 10 Malicious 121 Nodes with 20 Malicious 121 Nodes with 30 Malicious DATA PACKET OVERHEAD ERTLD Nodes with 0 Malicious ERTLD Nodes with 30 Malicious BIOSARP Nodes with 0 Malicious BIOSARP Nodes with 30 Malicious 20 0 Fig. 6. Data Packet Overhead DELIVERY RATIO Fig. 7. Delivery Ratio of BIOSARP and ERTLD ENERGY CONSUMPTION ERTLD Nodes with 0 Malicious ERTLD Nodes with 30 Malicious BIOSARP Nodes with 0 Malicious BIOSARP Nodes with 30 Malicious ERTLD Nodes with 0 Malicious Fig. 8. Energy Consumption of BIOSARP and ERTLD ERTLD Nodes with 30 Malicious BIOSARP Nodes with 0 Malicious BIOSARP Nodes with 30 Malicious 10 0 Fig. 9. Data Packet Overhead of BIOSARP and ERTLD V. CONCLUSION AND FUTURE WORK In this article, we have analyzed the scalability and stability of ant colony optimization (ACO) based routing protocol BIOSARP to handle the holes issues in WSNs. The WSN scenario with holes problems is configured in the network simulator 2 (ns-2) to compare the performance of BIOSARP with state of art routing protocol ERTLD. The results show that BIOSARP can tackle holes problems effectively and offers a scalable and stable solution. In particular, the comparison shows that BIOSARP maintains the network performance, even in the presence of holes, in terms of delivery ratio, battery power consumption, and data packet overhead. Hence, BIOSARP is capable of handling the routing problems caused by holes in advance. The analysis reveals that BIOSARP can self-adapt to the dynamical changes in the network topology. Our immediate future work is to enhance the routing protocol with IPv6 and mobility factors to come up with mobile internet protocol 6 (MIPv6) for WSN. ACKNOWLEDGMENT The authors are grateful to the anonymous reviewers for their valuable comments and suggestions that helped improve the presentation of this paper. REFERENCES [1] A. Cerpa, J. L. Wong, L. Kuang, M. Potkonjak, and D.Estrin, "Statistical Model of Lossy Links in Wireless Sensor Networks," in ACM/IEEE IPSN, Los Angeles, USA, [2] N. Ahmed, S. S. Kanhere, and S. Jha, "The holes problem in wireless sensor networks: a survey," SIGMOBILE Mob. Comput. Commun. Rev., vol. 9, pp. 4-18, [3] T. He, J. Stankovic, C. Lu, and T. Abdelzaher, "SPEED: A stateless protocol for real-time communication in sensor networks," in 23rd International Conference on Distributed Computing Systems, Providence, Rhode Island, USA, 2003, pp [4] P. K. Singh and G. Sharma, "An Efficient Prevention of Black Hole Problem in AODV Routing Protocol in MANET," in Trust, Security and 238

6 Privacy in Computing and Communications (TrustCom), 2012 IEEE 11th International Conference on, 2012, pp [5] E. Patouni, D. Kypriadis, and N. Alonistioti, "A lightweight framework for prediction-based resource management in future wireless networks," EURASIP Journal on Wireless Communications and Networking, vol. 2012, p. 144, [6] B. Zhang, Y. Wu, J. Lu, and K.-L. Du, "Evolutionary Computation and Its Applications in Neural and Fuzzy Systems," Applied Computational Intelligence and Soft Computing, vol. 2011, pp. 1-20, [7] G. Chen, T.-D. Guo, W.-G. Yang, and T. Zhao, "An improved ant-based routing protocol in Wireless Sensor Networks," in Collaborative Computing: International Conference on Networking, Applications and Worksharing, CollaborateCom 2006., New York, NY, 2006, pp [8] K. Saleem and N. Fisal, "Enhanced Ant Colony algorithm for selfoptimized data assured routing in wireless sensor networks," in Networks (ICON), th IEEE International Conference on, 2012, pp [9] M. Adnan, M. Razzaque, I. Ahmed, and I. Isnin, "Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey," Sensors, vol. 14, pp , [10] K. Saleem and N. Fisal, "Energy Efficient Information Assured Routing Based on Hybrid Optimization Algorithm for WSNs," in Proceedings of the th International Conference on Information Technology: New Generations, 2013, pp [11] K. Saleem, M. S. Khalil, N. Fisal, A. A. Ahmed, and M. A. Orgun, "Efficient Random Key Based Encryption System for Data Packet Confidentiality in WSNs," in IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom2013), Melbourne, Australia, 2013, pp [12] K. Saleem, A. Derhab, J. Al-Muhtadi, and M. A. Orgun, "Analyzing ant colony optimization based routing protocol against the hole problem for enhancing user s connectivity experience," Computers in Human Behavior. doi: /j.chb [13] L. Min and M. S. Hsiao, "An ant colony optimization technique for abstraction-guided state justification," in Test Conference, ITC International, 2009, pp [14] G. Francesca, A. Santone, G. Vaglini, and M. L. Villani, "Ant Colony Optimization for Deadlock Detection in Concurrent Systems," in Computer Software and Applications Conference (COMPSAC), 2011 IEEE 35th Annual, 2011, pp [15] A. Ali Ahmed, "An enhanced real-time routing protocol with load distribution for mobile wireless sensor networks," Computer Networks, vol. 57, pp , [16] A. A. Ahmed and N. F. Fisal, "Secure real-time routing protocol with load distribution in wireless sensor networks," Security and Communication Networks, vol. 4, pp , Aug [17] K. Saleem, N. Fisal, S. Hafizah, and R. Rashid, "An Intelligent Information Security Mechanism for the Network Layer of WSN: BIOSARP," in Computational Intelligence in Security for Information Systems. vol. 6694, Á. Herrero and E. Corchado, Eds., ed: Springer Berlin / Heidelberg, 2011, pp [18] K. Saleem, N. Fisal, and J. Al-Muhtadi, "Empirical Studies of Bio- Inspired Self-Organized Secure Autonomous Routing Protocol," Sensors Journal, IEEE, vol. 14, pp ,