Wireless Netw (2015) 21:1603–1612 DOI 10.1007/s11276-014-0872-1
A restricted flooding mechanism for efficient anycast server localization in MANETs Alexander Kostin • Gurcu Oz • Huseyin Haci
Published online: 12 December 2014 Springer Science+Business Media New York 2014
Abstract In wireless networks, reducing the number of redundant packets is one of the important mechanisms to minimize the required network bandwidth and the power consumed by network nodes. In this paper, an efficient and stateless flooding mechanism for anycast routing in wireless mobile ad hoc networks is proposed. The mechanism uses the technique of expanding ring search to decrease the related message traffic. A model of this mechanism is described. Based on this model, an extensive simulation study, together with real field experiments, has been conducted to investigate the performance of the proposed mechanism for anycast server localization. The simulation model has been developed in terms of a class of extended Petri nets that provide the possibility to conveniently represent parallelism of events and processes in the network. In simulation and real work experiments, fundamental performance metrics—response ratio, relative traffic and average response time—were investigated with varying distance of transmission and different combinations of model parameters. The obtained results show that the proposed approach to server localization in mobile ad hoc networks has good characteristics. As was demonstrated with a prototype system, the proposed routing method can
A. Kostin Girne American University, Girne, North Cyprus, Turkey e-mail:
[email protected] G. Oz (&) Department of Computer Engineering, Eastern Mediterranean University, via Mersin 10, Famagusta, North Cyprus, Turkey e-mail:
[email protected] H. Haci University of Kent, Kent, United Kindom e-mail:
[email protected]
be easily implemented at the application layer, without any changes at lower layers of the network protocol stack. Keywords Wireless ad hoc networks Anycast routing Restricted flooding Expanded ring search
1 Introduction Using replicated servers in mobile wireless networks to enhance the service availability and achieve load balance becomes a practically important task. To access one of the servers in the network, anycast communication paradigm can be used. The related anycast routing is a stateless, best effort form of communication in distributed client–server systems where a client tries to localize one of the servers in a specified group. In finding the shortest path to one of the contentequivalent servers, the minimum number of nodes should be involved to reduce the required network bandwidth and the number of collisions. With replicated servers, in a wireless network with mobile nodes and unreliable internode links, performance of the network can be improved with the use of an appropriate routing scheme. One of the simplest routing algorithms used in wireless networks is so called pure flooding [1]. Pure flooding is a topology-independent and stateless mechanism which provides sufficiently high reliability and requires minimal state information in nodes. However, due to the rapid rise of the number of transmitted packets and related collisions in the network, this scheme results in a high communication traffic when the size of the network increases. In this paper, we propose to use a scheme of the restricted flooding for anycast routing in wireless mobile networks. This scheme is based on the idea of an expanding ring search
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(ERS). Versions of ERS were used in DSR and AODV protocols for one-to-one routing [2, 3]. However, to the best of our knowledge, this scheme has never been investigated with respect to anycast routing in a MANET environment, where the existence of a group of servers gives the possibility to localize one of them with much lower traffic and less latency than in case of one-to-one routing. To investigate the proposed routing method, the detailed model of a mobile ad hoc network has been designed and implemented in terms of a class of extended Petri nets. Based on this model, the simulation model of anycast routing in a medium-sized ad hoc wireless mobile was then developed, and extensive simulation experiments were conducted with this model to study the dependence of performance characteristics on the varying value of TTL value. The rest of the paper is organized as follows. In Section 2 related work is analyzed. Section 3 explains the proposed anycast scheme. Section 4 outlines organization of the simulation model for evaluation of performance of the proposed routing scheme. In Section 5, simulation setup is presented and results of simulation are discussed. Section 6 explains real-word field experiments with a prototype system. Section 7 concludes the paper.
2 Related work A number of alternatives to the pure flooding scheme have been proposed and investigated in literature [4–8]. All these alternatives are based on the creation of the minimal flooding tree of nodes, so that only members of this tree will be able to re-transmit received messages and thereby to reduce the overall communication traffic. It is proved in [4] that constructing the minimal flooding tree is equivalent to finding the minimal connected domain set (MCDS) in the network. However, authors of [4] showed also that finding the MCDS in a network is an NPcomplete problem. In particular, to find an MCDS over all nodes, it is necessary to collect and maintain a large volume of state information in nodes. Performing continuously such a work in resource constraint and highly dynamic mobile ad hoc networks (MANETs) introduces difficult problems. The most serious of them is that the message overhead caused by neighborhood information exchange between nodes results in a prohibitively high network traffic that requires a broad network bandwidth. In addition, to keep the exchanged neighborhood information up to date, a complex time synchronization between nodes should be organized and dynamically maintained. In [9], another anycast routing mechanism is proposed, which uses the idea of electrostatic potentials for routing decisions in network nodes. Since this communication paradigm exploits a different semantics than IP, it is not
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compatible with IP networks and requires a further study to make it practically applicable. In [10], a temperature field-based anycast routing protocol is proposed for communication in mesh networks. However, to construct and maintain a temperature field, each node needs to periodically exchange service beacon messages with its neighbor nodes. Again, this produces a very high message traffic and requires to keep a large volume of state information in network nodes. The other related works on the use of flooding for routing in MANETs were recently published. In [11], a so called hybrid broadcast mechanism is proposed by combining different flooding schemes to reduce broadcast storm of the simple flooding for wireless mobile networks. However, the implementation of proposed approach, in order to get node positioning information Global Positioning System (GPS) or Received Signal System (RSS) should be used. Clearly, the necessity of using the GPS and RSS considerably complicates the proposed routing scheme. In [12], a flooding-limited based multicast routing scheme is described for MANETs that is based on the genetic algorithm. The method uses available resources and needs short computation time for route optimization in a dynamic wireless ad hoc network. It is claimed that, by selecting the appropriate values for some genetic algorithm parameters, routing performance could be considerably improved. However, the selection of such parameters can be a challenging problem. A number of anycast-based routing protocols have been developed for MANETs, and their performance was evaluated in the literature. In particular, in [13, 14] an anycast routing protocol, based on AODV [10], is proposed. In works [15] and [16], the A_DSR routing and Anycast Routing based Dynamic Source Routing (ARDSR) anycast routing protocols based on DSR [9] are described. In these works, it is shown that anycast service can improve wireless mobile ad hoc network performance when there is a high node mobility and frequent link disconnectivity without repair or rediscovery of servers in the network. On the other hand, these works require to maintain and use a routing table in each node to keep routing and network information. Such a routing table must be updated generally for each message processed by nodes. As a result, a scalability problem arises, especially when the number of anycast groups of servers is sufficiently large. An interesting distributed k-anycast routing protocol based on mobile agents is proposed in [17]. The k-anycast members are selected from a set of servers to deliver data packets. Each node maintains a routing table, and routing information needs to be exchanged between the neighbor nodes. The proposed method does not use any global network information for routing and shows good performance in highly dynamic networks.
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In [18, 19], authors exploit density of nodes in the routing strategy. In density-based routing packets are not forwarded by the shortest path, and routing table of neighbor nodes is used at each node for making routing decisions. In [20], an anycast routing protocol is presented based on the connectivity metric for MANETs. Connectivity is evaluated between receiver nodes and adjacent nodes and then is used with the hop count to evaluate the goodness of each adjacent node. For connectivity estimation, flooding measurements packets are distributed from receivers. In [21], another similar work based on the node degree (the number of adjacent nodes and the hop distance) is described. These schemes considerably complicate routing. In [22, 23], multi-constraint anycast routing protocols, based on swarm intelligence of ants and fuzzy agents, are described. In [24], authors use node movement stability and congestion aware anycast routing scheme, which is used together with DSR to select k-servers in MANETs. Then, based on the route stability, channel load, hop and server load, a server is selected among k-servers. All these scheme require sufficiently large computing power of involved network nodes. The work [25] proposes a so called geocasting protocol by combining anycast and flooding schemes. The MANET is divided into grids, and the shortest path route is created between hosts in the grids. It is assumed, that each node has a GPS receiver, so each receiver knows its own physical location. Initially, anycast is used to route packets from source to a node in the grid, and then packet is flooded to any member in the geocast region. Due to its complexity, this scheme is difficult to use in practice. In [26], an intelligent anycast routing mechanism is proposed that uses neural networks and fuzzy technique to select a server from an anycast group by considering quality of service (QoS) constraint route. Here, all nodes have GPS receiver to obtain location and time. They need to use localization algorithms in their operations. Works [27–32] contain other related studies of routing in wireless networks. In particular, an energy-efficient routing and scheduling scheme is presented in [27] for vehicular ad hoc delay-tolerant networks [28] by combining forwarding and replication methods. Having the learning ability, the scheme provides good energy-efficiency, with the delay-bound delivery ratio. A new data aggregation technique is generated from Compressed Sensing (CS) method in [29] in order to reduce amount of data to be transported in the network. As a result total energy consumption of the network is minimized. Energy-efficient wireless communication is also evaluated in [30] with the use of green techniques for mobile networks with energy-efficiency metrics. In [31], a reliable multicast protocol is proposed to improve energyefficiency, throughput and fairness in dynamic wireless networks.
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In [32], a topology preservation channel assignment scheme is proposed to minimize the co-channel interference in multi-hop wireless networks. This scheme implies the combination of ad hoc networks, sensor networks, cell networks and emerging mesh networks. Evaluation of any routing technique requires the use of some performance metrics. A detailed survey related to routing metrics for cognitive radio networks (CRNs) is presented in [33]. The authors state that traditional MANETs’ routing protocols could not be deployed directly in CRNs. In addition, traditional routing metrics, such as end to end delay and hop count, should be redesigned to fit characteristics of CRNs. In [34–36], some other related studies of wireless networks are described. In particular, work [34] addresses application of computational intelligence (CI) technologies for configuration and distribution of network resources in a composite radio environment, with wireless local area network as a co-operating component. It is shown that efficiency of the system could be improved in terms of cost and QoS. The work [36] is another study, which considers cost of learning schemes for dynamic heterogeneous wireless networks. In [35] the importance of peer-to-peer (P2P) systems is discussed to deliver Internet media content. Here, the idea of multicast is used by each node to push available information to the partners and pull the missing data from one of its partners.
3 Description of the scheme The proposed restricted flooding scheme (RESFLO) resembles somewhat the operation of the known Traceroute utility program, that varies the value of TTL in separate transmitted messages to subsequently reach intermediate routers on the path from a source node to a specified destination node in the Internet. In using this idea, RESFLO continuously expands its coverage ring by incrementing time-to-live (TTL) value in transmitted search requests. A coverage ring is an imaginary circular area that contains all neighbor nodes of the source node up to some distance measured in hops. Each search request, transmitted in a distance-restricted broadcast mode, is potentially received by all servers inside this ring. To expand the ring, the TTL value, starting from one, is incremented in subsequent request messages, up to some maximum value MAXTTL, which is a system configuration parameter, until some anycast server is reached and replied. There are two types of nodes in such a network, namely simple nodes and anycast server nodes. Correspondingly, there are two types of messages—search requests, generated by a simple source node and retransmitted by simple neighbor nodes in the coverage area, and reply messages
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from anycast servers to the source node. Each node in the coverage ring of the source node can be an internal node or a boundary node. Internal nodes are able to re-transmit received search requests and replies from anycast servers. However, boundary simple nodes do not re-transmit received search requests further in the network, while anycast server nodes in the coverage area are capable of replying to received search request. Here the TTL value of search requests is used to restrict their expanding in the network and thus to reduce communication traffic. In the proposed model, with the initial value of TTL = 1, the coverage ring includes only 1-hop neighbours (Fig. 1a). Here simple nodes are shown as small circles and server nodes as rectangles. Any message transmitted by the source node may not be re-transmitted further by any other simple node inside the ring, but can be replied by server nodes, if there is any at 1-hop distance. If there are no server nodes at 1-hop distance then, by incrementing the TTL by 1 in the next request message from the source, the coverage ring is expanded to cover all neighbor nodes at 1 and 2 hops. Figure 1b shows that, at 2-hop distance, there are two server nodes, and each of them can generate a reply to the received request. With the expanded ring, 1-hop neighbors are not at the border of the coverage area anymore (they are now internal nodes) and they may re-transmit search request messages from the source node or reply messages from anycast servers. With the further incrementing of TTL, the coverage area becomes larger, and ideally at least one server node will be reached for hop count. According to the RESFLO algorithm shown in Fig. 2, for each originated search message, initially with TTL = 1, a timeout t0 is started by the requesting source node. At the end of t0, this node checks whether a reply is received from at least one server. In case of one or more
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server replies, the first of them, together with the address of the replying server, is recorded, and then, in principle, a point-to-point communication could be done with the localized server. The protocol of such a communication depends on the purpose of the network and is not considered here. However, if no reply is received from any server during timeout t0, TTL is checked to see if its value reached MAXTTL value. If TTL is equal to MAXTTL without a reply from a server, the search request is considered as unsuccessful, and then the source node generates a new search message with TTL = 1. Then the described procedure is repeated for the new request message. Note that, in a dynamically changing environment of the mobile network, the situation for the next request can be quite different. In our study, a large number of requests with different identifiers are generated to collect a sufficient volume of statistical data to evaluate performance of the network.
4 The model and its components To investigate performance of the proposed routing scheme, a simulation model of the MANET under consideration was developed with the use of a class of extended Petri nets. A detailed description of extended Petri nets and their use for modelling of network and distributes systems is given in [37]. The starting point of the developed model is the generic model of WLAN described in [38, 39]. Structurally, the model consists of two types of modules as shown in Fig. 3. One type represents functionality of a node of the wireless network. The actual number of modules of this type is equal to the desired number of nodes in the network.
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Fig. 1 The expanding ring messaging (a) 1-hop ring (with TTL = 1), (b) 2-hop ring (with TTL = 2)
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The second type is switching module. Its main task is to support communication links between nodes. In particular, when a node transmits a message, the switching node determines those nodes, that are close enough to the transmitting node to be eligible to receive the transmitted message. The second important task of the switching module is to control a random movement of each network node in accordance to the chosen mobility scheme. For this purpose, the switching module maintains and periodically updates coordinates of each node. In addition, switching node delivers to each receiving node coordinates of this node and coordinates of the transmitting node. This information is used by each receiving node to implement a
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model of direction-dependent links. Finally, the switching module performs a necessary initialization of node modules at the start of simulation run. There are two types of requests handled by the model. The first type is a multicast routing request, and the second type is a unicast reply. A multicast request is generated by a node, when it wishes to localize an anycast server. When such a request reaches an anycast server, this server and other involved nodes will forward backwards a unicast reply. The entire set of network nodes is divided into two subsets. One subset consists of so called simple nodes, or client nodes. The second subset includes those nodes that work as anycast servers. To simplify the model, it is assumed that only one simple node may generate requests to localize an anycast server. The remaining simple nodes are used for forwarding requests from source node to anycast servers and replies from anycast servers back to the source node. Routing requests are generated periodically in a loop and passed to the switching module. For each generated routing request, a fixed time-out is started. In case when there is no reply, the next cycle of the loop is started. On the other side, if a reply to the generated request has been received during the time-out, its characteristics are registered. Initially, TTL value is set to one for each request message and incremented by one if there is no reply from any server. In any case, if at the moment of receiving a message (request or reply), the link between the receiver and sender is considered as broken, then the message is discarded.
5 Performance evaluation and simulation results The developed simulation model is implemented in the simulation system Winsim [37] according to the following setup. It is assumed that the network area is a square of 500 m 9 500 m, populated with 50 mobile, uniformly distributed nodes. The number of nodes is in agreement with many published experiments with medium-sized mobile networks. The anycast server group in the network contains five mobile server nodes. Thus, positions of all nodes are arranged in such a way that the network area with its nodes can be approximated as a point Poisson field [40]. In the simulation and experimental investigations, three practically important performance metrics—relative traffic, response ratio and response time—were considered. The first metric reflects the relative number of packets transmitted in the network for each search request generated by the source node. The second metric characterizes how the network is efficient in delivering packets from the source node to server nodes and back. The third metric represents
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ð1Þ
where Nf is the number of packets transmitted by all network nodes. This number includes nf C 1. Ns packets from the source node and packets transmitted by all other nodes. In general, nf C 1. The case nf = 1 corresponds to the situation when a request is transmitted by the source node, but is not retransmitted by any other node and not replied by any server node. The response ratio of requests is represented by the expression nd ¼
Nd ; Ns
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where Ns is the number of packets transmitted by the source node and Nd is the number of replies delivered back to the source node. Finally, the average response time, measured at the source node, is estimated with the use of expression R¼
Nr 1 X Ri ; Nr i¼1
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Trasmission radius, m Fig. 4 Relative traffic versus transmission radius for three routing schemes
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where Nr is the number of earliest replies at the source node and Ri is the round trip time for reply i, i = 1, 2, …, Nr. It is known that the success of delivering of messages in a mobile wireless network depends not only on distance, but also on the direction from the transmitting node to receiving nodes. This dependence is mainly due to different obstacles (such as buildings, hills, trees and so on) between the transmitter and receiving nodes. The success can be affected also by other factors, such as a fading effect. To simulate the dependence of communication success on the direction from each transmitting node to other nodes, each node is assumed to have eight directed links, and each of these links can fail randomly according to a Markov process with given probability of failure of the link, which is called a link availability and has values in the range (0, 1) [38, 39]. The number of directed links was chosen to have a sufficiently small granularity of directions, without a considerable complicating the model. In the simulation, three series of experiments were conducted to show the dependence of the chosen performance metrics on the used routing mechanism, transmission radius and link availability. In the first and second series, the pure flooding scheme with fixed TTL values of 4
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the round trip time at the source of requests for each received reply message. The formal definition of all performance metrics is given below. It is assumed that there is only one source node in the network. The relative traffic in the network is estimated according to expression
40 30 20 Pure flooding with TTL = 7 Pure flooding with TTL = 4 Restricted flooding
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Fig. 6 Response time versus transmission radius for three routing schemes
and 7 was used. In the last series of experiments, the proposed restricted flooding scheme with TTL values varying in the range of (1, MAXTTL) was implemented, where MAXTTL = 7. With transmission distance changing in the range of (30, 210) meters, link availability of 0.7 and the node random movement speed up to 3.6 km/h were used as simulation parameters. To obtain sufficiently stable statistical results, 2,000 search request messages were generated by the source node in each simulation experiment. The main simulation results are presented in Figs. 4, 5 and 6.
Wireless Netw (2015) 21:1603–1612 Fig. 7 Nodes in the network field: (a) initial distribution of node positions, (b) movement of source node and server 1
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Table 1 Simulation and experimental results for the restricted flooding scheme
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Experimental results
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On base of the obtained simulation results, we can arrive at the following observations and conclusions. 1.
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As Fig. 4 demonstrates, the relative traffic, independent of the used routing scheme, is quite low for a small transmission radius. The reason is that, with N = 50 nodes in the network, there is a high probability that each transmitting or forwarding node has no neighbors within its transmission radius. In fact, as was shown in [38], probability of having two nodes in the circle of radius of 30 m is only 0.09. This means that a transmitted message has a very low chance to be received and re-transmitted by at least one other node. On the other hand, with the increase of the transmission radius, especially with TTL = 7, pure flooding mechanism behaves poorly in terms of the relative traffic, since more and more nodes are involved in the re-transmission of packets. Obviously, with high relative traffic, re-transmissions result in the considerable overloading of the network and require high network bandwidth. With decreased value of TTL, the number of nodes involved in packet transmission is becomes smaller. However, as shown in Fig. 4, value of TTL = 4 has a small impact on the performance of the pure flooding scheme compared with TTL = 7. On the other side, the proposed RESFLO scheme demonstrates, as Fig. 4 shows, a significant performance gain even with the large transmission radius of 210 m, by considerably
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reducing the number of nodes involved in transmissions for each request. As Fig. 5 shows, for a small transmission radius, the response ratio is relatively low for the studied routing schemes. However, with the increase of the transmission radius, the response ratio rapidly grows in pure flooding scheme. On the other hand, in the restricted flooding scheme, the response ratio grows approximately linearly up to a value close to 0.8. Less values of the response ratio in RESFLO, compared with the pure flooding, can be explained by the fact that, with the reduced number of duplicated replies from servers, RESFLO has lower probability of reaching one of the servers. Figure 6 shows that the response time is quite low at a small transmission radius for all routing schemes. It initially increases with the increase of the transmission radius, reaches some maximum and then decreases. For the restricted flooding scheme, the response time is a little less for many transmission ranges. The decrease of response time in all three routing schemes after some maximum can be explained by the increased probability of having active directional links between nodes in the network for large transmission distances.
6 Real-world experiments The proposed restricted flooding scheme was also implemented and tested in a prototype system on a group of laptop computers in a wireless ad hoc network configuration, with 1 source of routing request messages, 3 server nodes and 6 intermediate nodes. The laptop computers used in the experiments as network nodes had Intel core 2 duo 2.2 GHz processor each. They were connected to the network with 802.11 b/g Wi-Fi wireless interface adapters. During the experiments, laptops were powered by 9-cell
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batteries. All the experiments were performed during daytime with temperature varying from 20 to 30 C. In each experiment, the number of requests, sent from the source node to the destination node, was 2000. In the prototype software system, the same program was used on all laptops under Windows 7. The program implemented a multithreaded Visual C?? process according to the decision mechanism of RESFLO in Fig. 2, in strict correspondence with the simulation model. For internode communication, the socket mechanism of interprocess communication was used with the UDP protocol in multicasting mode. The details of the program can be found in [41–43]. For comparison, simulation experiments were also conducted for the same number of nodes as in the prototype system. In the field experiments all laptops were distributed randomly in a 500 m 9 500 m open area by setting an intermediate node between source node and each server, so that source node requests could reach servers only via intermediate nodes. During the field experiments, laptops were carried by students. In this movement, each student selects a random direction and goes with a walking speed up the border of the area. Then another random direction is selected back in the area. Initial distribution of node positions are shown in Fig. 7a. Here Int1, Int2, …, Int6 represent intermediate nodes. Figure 7b illustrates random movement of a source node and one of server nodes (server 1). Other nodes of the network move in a similar random way. The comparative results of field experiments and simulation are shown in Table 1 for transmission radius of 210 m. This table contains average performance results for three different performance metrics. One can see, from this table, that the results of field experiments and simulation for the same network configuration are quite close. This can serve as a proof of correctness of the developed simulation model. 7 Conclusion An efficient and stateless restricted flooding scheme for anycast server localization in mobile ad hoc networks is proposed and investigated in simulation and field experiments with a prototype system. A search technique of expanding rings is used in this scheme. Simulation and prototype system experimental results for small and medium-sized network show the effectiveness of the proposed scheme in reducing relative traffic, with some impact on the response ratio. Also it is observed that real-world experimental results are slightly better than the simulation results. The applicability of this scheme to large networks and investigation of the related latency requires a further study.
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1611 36. Khan, M. A., Tembine, H., & Vasilakos, A. (2012). Game dynamics and cost of learning in heterogeneous 4G networks. IEEE Journal on Selected Areas in Communications, 30(1), 198–213. 37. Kostin, A., & Ilushechkina, L. (2010). Modeling and simulation of distributed systems. New Jersey: World Scientific C. 38. Kostin, A., Oz, G., & Haci, H. (2009). Performance study of a wireless mobile ad hoc network with orientation-dependent internode communication links. In Proceedings of 24th ISCIS (pp. 326–331). 39. Kostin, A., Oz, G., & Haci, H. (2014). Performance study of a wireless mobile ad hoc network with orientation-dependent internode communication scheme. International Journal of Communication Systems, 27, 322–340. 40. Kostin, A. (2010). Probability distribution of distance between pairs of nearest stations in a wireless network. Electronics Letters, 46(18), 1299–1300. 41. Oz, G. & Ozen, Y. (2009). Experimental investigation of data transmission in wireless ad hoc networks. In 5th International conference on soft computing, computing with words and perceptions in system analysis, decision and control (ICSCCW2009) (pp. 1–4). Gazimagusa, Northern Cyprus. 42. Azizi, R., & Oz, G. (2011). Performance evaluation of data dissemination in real-world ad hoc networks. In International conference on communications and information technology (ICCIT2011) (pp. 187–190). Agaba. 43. Abed, A. K., & Oz, G. (2013). Experimental study of pure flooding method for localizing an anycast server in wireless ad hoc networks. In Palestinian international conference on information and communication technology (PICICT’2013) (pp. 83–89). Gaza.
Alexander E. Kostin received his B.S. degree in electrical engineering from Ryazan Radiotechnical Institute, Ryazan, Russia, a Ph.D. in computer engineering from Moscow Engineering-Physical Institute (Technical University), Moscow, and a D.Sc. degree in computer science from Moscow Institute of Electronic Technology (Technical University), Moscow. Up to 1995, he worked as an Associate Professor and then as a Professor in the Department of Computer Engineering at Moscow Institute of Electronic Technology. Since 1995 he worked as a Professor in the Department of Computer Engineering of Eastern Mediterranean University and, since 2010, as a Professor at Girne American University. As a university professor, he prepared a variety of courses in computer science and computer engineering. His research and professional interests include computer systems analysis, distributed systems and their modeling and simulation, Petri nets and their extensions, advanced simulation systems. He is an author or co-author of eight textbooks for students, of over one hundred of other works, including papers in journals and conference proceedings, and surveys of computer science and computer engineering books in English and German languages.
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1612 Gurcu Oz received her B.S., M.S. degrees from the Electrical and Electronic Engineering department and Ph.D. degree from the Computer Engineering Department of Eastern Mediterranean University, in Mag˘usa, North Cyprus. Since 2001, she has been working as an Assistant Professor in the Department of Computer Engineering of Eastern Mediterranean University. Her research interests include computer networks, design of networks protocols for wireless ad hoc networks, distributed systems and system simulation. Huseyin Haci received his B.Sc. and M.Sc. from the Computer Engineering Department of Eastern Mediterranean University, North Cyprus. He was awarded as the top student in the faculty of engineering and received an outstanding student scholarship for his B.Sc. and M.Sc. degrees from North Cyprus Ministry of Education. He has been employed as a Research Assistant between 2008 and 2010, where he was involved in various National A Type and International European Commission Projects. He is
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Wireless Netw (2015) 21:1603–1612 currently continuing his Ph.D. degree in the School of Engineering and Digital Arts at University of Kent, UK. He is currently active in EC ICT FP7 ULOOP project and his current research interests are user-provided cooperative networks, wireless computer networks analysis and simulation.
