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Wireless Networks
Erschienen in: Wireless Networks 6/2010

01.08.2010

Hybrid simulation of Sensor and Actor Networks with BARAKA

Erschienen in: Wireless Networks | Ausgabe 6/2010

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Abstract

We present BARAKA, a new hybrid simulator for Sensor and Actor Networks (SANETs). This tool provides integrated simulation of communication networks and robotic aspects. It allows the complete modelling of co-operation issues in SANETs including the performance evaluation of either robot actions or networking aspects while considering mutual impact. This hybrid simulation enables new potentials in the evaluation of algorithms developed for communication and co-operation in SANETs. Previously, evaluations in this context were accomplished separately. On the one hand, network simulation helps to measure the efficiency of routing or medium access. On the other hand, robot simulators are used to evaluate the physical movements. Using two different simulators might introduce inconsistent results, and might make the transfer on real hardware harder. With the development of methods and techniques for co-operation in SANETs, the need for integrated evaluation environment increased. To compensate this demand, we developed BARAKA.
Vorheriger Artikel Distributed fault-tolerant topology control in wireless multi-hop networks
Nächster Artikel A context-constrained authorisation (CoCoA) framework for pervasive grid computing

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Fußnoten
1
We use the word agent in the following when we refer to either the entities of a SANET.
 
2
Robots can sense obstacles using several devices: infra-red sensors, lasers, radars, cameras, bumpers, etc. Each of them has some advantages and disadvantages. Infrared-sensors, for instance, are cheap but work at short range and cannot return the detailed shape of an object. It might difficult to tell an obstacle, which has to be avoided, from a target, which has to be reached. Cameras can give more detailed information, but image processing requires a lot of computational power. It is possible to combine data coming from different sensors to overcome such problems, but this increases the complexity of sensor data elaboration. The reader can refer to [7] and [8] for some examples.
 
15
It is not the purpose of our work to address this problems, but it might be done, e.g., by triangulating the signal emitted by a node, by using directional antennas, or by means of a vision system.
 
Literatur
1.
Akyildiz, I., & Kasimoglu, I. (2004). Wireless sensor and actor networks: Research challenges. Ad Hoc Networks, 2, 351–367.
2.
Melodia, T., Pompili, D., Gungor, V., & Akyildiz, I. (2005). A distributed coordination framework for wireless sensor and actor networks. In: Proceedings of the 6th ACM International Symposium on Mobile Ad Hoc Networking and Computing (ACM Mobihoc 2005) (pp. 99–110). New York, NY: ACM Press.
3.
Batalin, M., & Sukhatme, G. (2004). Using a sensor network for distributed multi-robot task allocation. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA2004) (Vol. 1, pp.158–164). New York, NY: IEEE Press.
4.
Melodia, T., Pompili, D., & Akyildiz, I. (2006). A communication architecture for mobile wireless sensor and actor networks. In: Proceedings of IEEE Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON 2006). New York, NY: IEEE Press.
5.
Dressler, F. (2006). Network-centric actuation control in sensor/actuator networks based on bio-inspired technologies. In: 3rd IEEE International Conference on Mobile Ad Hoc and Sensor Systems (IEEE MASS 2006): 2nd International Workshop on Localized Communication and Topology Protocols for Ad hoc Networks (LOCAN 2006), Vancouver, Canada.
6.
Labella, T., & Dressler, F. (2006). A bio-inspired architecture for division of labour in SANETs. In: Proceedings of the First IEEE/ACM International Conference on Bio Inspired Models of Network, Information and Computing Systems (BIONETICS 2006). Italy: Cavalese.
7.
Manduchi, R., Castano, A., Talukder, A., & Matthies, L. (2005). Obstacle detection and terrain classification for autonomous off-road navigation. Autonomous Robots, 18, 81–102.CrossRef
8.
Jia, S., Sheng, J., Chugo, D., & Takase, K. (2007). Obstacle recognition for a mobile robot in indoor environments using RFID and stereo vision. In: Proceedings of International Conference on the Mechatronics and Automation, (ICMA 2007) (pp. 2789–2794). New York, NY: IEEE Press.
9.
Law, A., & David Kelton, W. (2000). Simulation modeling and analysis (3rd ed.). Boston: McGraw-Hill.
10.
Johnson, D., Hu, Y. C., & Maltz, D. (2007). The dynamic source routing protocol (DSR) for mobile ad hoc networks for IPv4. IETF RFC 4728.
11.
Perkins, C., Belding-Royer, E., & Das, S. (2003). Ad hoc on demand distance vector (AODV) routing. IETF RFC 3561.
12.
Fujimoto, R., Perumalla, K., Park, A., Wu, H., Ammar, M., & Riley, G. (2003). Large-scale network simulation: How big? how fast? In: Modeling, Analysis and Simulation of Computer Telecommunications Systems (MASCOTS 2003) (pp. 116–123). New York, NY: IEEE Press.
13.
Breslau, L., Estrin, D., Fall, K., Floyd, S., Heidemann, J., Helmy, A., et al. (2000). Advances in network simulation. IEEE Computer, 33, 59–67.
14.
Heidemann, J., Bulusu, N., Elson, J., Intanagonwiwat, C., Lan, K. C., Xu, Y., et al. (2001). Effects of detail in wireless network simulation. In: SCS Multiconference on Distributed Simulation, pp. 3–11
15.
Pawlikowski, K., Jeong, H. D., & Lee, J. S. (2002). On credibility of simulation studies of telecommunication networks. IEEE Communications Magazine, 40(1), 132–139.
16.
Sundresh, S., Kim, W., & Agha, G. (2004). SENS: A sensor, environment and network simulator. In: Proceedings of the 37th Annual Simulation Symposium (pp. 221–228). New York, NY: IEEE Press.
17.
Camp, T., Boleng, J., & Davies, V. (2002). A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing: Special Issue on Mobile Ad Hoc Networking: Research, Trends and Applications, 2, 483–502.
18.
Bai, F., & Helmy, A. (2004). A survey of mobility modeling and analysis in wireless ad hoc networks. In: Wireless ad hoc and sensor networks. Dordrecht, The Netherlands: Kluwer Academic Publishers.
19.
Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., Osawa, E., & Matsubara, H. (1997). Robocup: A challenge AI problem. AI Magazine, 18, 73–85.
20.
Gerkey, B., Vaughan, R., & Howard, A. (2003). The player/stage project: Tools for multi-robot and distributed sensor systems. In: Proceedings of the International Conference on Advanced Robotics (ICAR 2003) (pp. 317–323). Portugal: Coimbra.
21.
Parker, L. (1997). L-ALLIANCE: Task-oriented multi-robot learning in behavior-based systems. Journal of Advanced Robotics, 11(4), 305–322.
22.
Varga, A. (2001). The OMNeT++ discrete event simulation system. In: Proceedings of the 15th European Simulation Multiconference (ESM’2001). Nottingham, UK: European Council for Modelling and Simulation.
23.
Dorigo, M., Trianni, V., Şahin, E., Groß, R., Labella, T., Baldassarre, G., et al. (2004). Evolving self-organizing behaviors for a Swarm-Bot. Autonomous Robots, 17, 223–245.CrossRef
24.
Di Caro, G., Ducatelle, F., & Gambardella, L. (2005). AntHocNet: An adaptive nature-inspired algorithm for routing in mobile ad hoc networks. European Transactions on Telecommunications, Special Issue on Self-organization in Mobile Networking, 16, 443–455.
25.
Labella, T. (2007). Division of Labour in Groups of Robots. PhD thesis, Université Libre de Bruxells.
Metadaten
Titel
Hybrid simulation of Sensor and Actor Networks with BARAKA
Publikationsdatum
01.08.2010
Erschienen in
Wireless Networks / Ausgabe 6/2010
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-008-0134-1

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