Distributed Algorithms for Improving Wireless Sensor Network Lifetime with Adjustable Sensing Range

Wireless sensor networks are made up of a large number of sensors deployed randomly in an ad-hoc manner in the area/target to be monitored. Due to their weight and size limitations, the energy conservation is the most critical issue. Energy saving in a wireless sensor network can be achieved by scheduling a subset of sensor nodes to activate and allowing others to go into low power sleep mode, or adjusting the transmission or sensing range of wireless sensor nodes. In this thesis, we focus on improving the lifetime of wireless sensor networks using both smart scheduling and adjusting sensing ranges. Firstly, we conduct a survey on existing works in literature and then we define the sensor network lifetime problem with range assignment. We then propose two completely localized and distributed scheduling algorithms with adjustable sensing range. These algorithms are the enhancement of distributed algorithms for fixed sensing range proposed in the literature. The simulation results show that there is almost 20 percent improvement of network lifetime when compare with the previous approaches

A Comprehensive Survey of Potential Game Approaches to Wireless Networks

Potential games form a class of non-cooperative games where unilateral improvement dynamics are guaranteed to converge in many practical cases. The potential game approach has been applied to a wide range of wireless network problems, particularly to a variety of channel assignment problems. In this paper, the properties of potential games are introduced, and games in wireless networks that have been proven to be potential games are comprehensively discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on Communications, vol. E98-B, no. 9, Sept. 201

An Energy Balanced Dynamic Topology Control Algorithm for Improved Network Lifetime

In wireless sensor networks, a few sensor nodes end up being vulnerable to potentially rapid depletion of the battery reserves due to either their central location or just the traffic patterns generated by the application. Traditional energy management strategies, such as those which use topology control algorithms, reduce the energy consumed at each node to the minimum necessary. In this paper, we use a different approach that balances the energy consumption at each of the nodes, thus increasing the functional lifetime of the network. We propose a new distributed dynamic topology control algorithm called Energy Balanced Topology Control (EBTC) which considers the actual energy consumed for each transmission and reception to achieve the goal of an increased functional lifetime. We analyze the algorithm's computational and communication complexity and show that it is equivalent or lower in complexity to other dynamic topology control algorithms. Using an empirical model of energy consumption, we show that the EBTC algorithm increases the lifetime of a wireless sensor network by over 40% compared to the best of previously known algorithms

Towards Optimal Distributed Node Scheduling in a Multihop Wireless Network through Local Voting

In a multihop wireless network, it is crucial but challenging to schedule transmissions in an efficient and fair manner. In this paper, a novel distributed node scheduling algorithm, called Local Voting, is proposed. This algorithm tries to semi-equalize the load (defined as the ratio of the queue length over the number of allocated slots) through slot reallocation based on local information exchange. The algorithm stems from the finding that the shortest delivery time or delay is obtained when the load is semi-equalized throughout the network. In addition, we prove that, with Local Voting, the network system converges asymptotically towards the optimal scheduling. Moreover, through extensive simulations, the performance of Local Voting is further investigated in comparison with several representative scheduling algorithms from the literature. Simulation results show that the proposed algorithm achieves better performance than the other distributed algorithms in terms of average delay, maximum delay, and fairness. Despite being distributed, the performance of Local Voting is also found to be very close to a centralized algorithm that is deemed to have the optimal performance

Beaconing Approaches in Vehicular Ad Hoc Networks: A Survey

A Vehicular Ad hoc Network (VANET) is a type of wireless ad hoc network that facilitates ubiquitous connectivity between vehicles in the absence of fixed infrastructure. Beaconing approaches is an important research challenge in high mobility vehicular networks with enabling safety applications. In this article, we perform a survey and a comparative study of state-of-the-art adaptive beaconing approaches in VANET, that explores the main advantages and drawbacks behind their design. The survey part of the paper presents a review of existing adaptive beaconing approaches such as adaptive beacon transmission power, beacon rate adaptation, contention window size adjustment and Hybrid adaptation beaconing techniques. The comparative study of the paper compares the representatives of adaptive beaconing approaches in terms of their objective of study, summary of their study, the utilized simulator and the type of vehicular scenario. Finally, we discussed the open issues and research directions related to VANET adaptive beaconing approaches.Ghafoor, KZ.; Lloret, J.; Abu Bakar, K.; Sadiq, AS.; Ben Mussa, SA. (2013). Beaconing Approaches in Vehicular Ad Hoc Networks: A Survey. Wireless Personal Communications. 73(3):885-912. doi:10.1007/s11277-013-1222-9S885912733ITS-Standards (1996) Intelligent transportation systems, U.S. Department of Transportation, http://www.standards.its.dot.gov/about.aspCheng, L., Henty, B., Stancil, D., Bai, F., & Mudalige, P. (2005). Mobile vehicle-to-vehicle narrow-band channel measurement and characterization of the 5.9 Ghz dedicated short range communication (DSRC) frequency band. IEEE Transactions on Selected Areas in Communications, 25(8), 1501–1516.van Eenennaam, E., Wolterink, K., Karagiannis, G., & Heijenk, G. (2009). Exploring the solution space of beaconing in vanets. In Proceedings of the 2009 IEEE international vehicular networking conference, Tokyo (pp. 1–8).Torrent-Moreno, M. (2007). Inter-vehicle communications: Assessing information dissemination under safety constraints. In Proceedings of the 2007 IEEE conference wireless on demand network systems and services, Austria (pp. 59–64).Lloret, J., Canovas, A., Catalá, A., & Garcia, M. (2012). Group-based protocol and mobility model for vanets to offer internet access. Journal of Network and Computer Applications 2224–2245 doi: 10.1016j.jnca.2012.02.009 .Nzouonta, J., Rajgure, N., Wang, G., & Borcea, C. (2009). Vanet routing on city roads using real-time vehicular traffic information. IEEE Transactions on Vehicular Technology, 58(7), 3609–3626.Fukui, R., Koike, H., & Okada, H. (2002). Dynamic integrated transmission control(ditrac) over inter-vehicle communications. In Proceedings of the 2002 IEEE vehicular technology conference, Birmingham (pp. 483–487).Schmidt, R., Leinmuller, T., Schoch, E., Kargl, F., & Schafer, G. (2010). Exploration of adaptive beaconing for efficient intervehicle safety communication. IEEE Network, 24(1), 14–19.Ghafoor, K., Bakar, K., van Eenennaam, E., Khokhar, R., Gonzalez, A. A fuzzy logic approach to beaconing for vehicular ad hoc networks, Accepted for publication in Telecommunication Systems Journal.Ghafoor, K., & Bakar, K. (2010). A novel delay and reliability aware inter vehicle routing protocol. Network Protocols and Algorithms, 2(2), 66–88.Mittag, J., Thomas, F., Härri, J., & Hartenstein, H. (2009). A comparison of single-and multi-hop beaconing in vanets. In Proceedings of the 2009 ACM international workshop on vehicular internetworking, Beijing (pp. 69–78).Sommer, C., Tonguz, O., & Dressler, F. (2010). Adaptive beaconing for delay-sensitive and congestion-aware traffic information systems. In Proceedings of the 2010 IEEE international vehicular networking conference (VNC), New Jersey (pp. 1–8).Guan, X., Sengupta, R., Krishnan, H., & Bai, F. (2007). A feedback-based power control algorithm design for vanet. In Proceedings of the 2007 IEEE international conference on mobile networking for vehicular environments, USA (pp. 67–72).AL-Hashimi, H., Bakar, K., & Ghafoor, K. (2011). Inter-domain proxy mobile ipv6 based vehicular network. Network Protocols and Algorithms, 2(4), 1–15.Rawat, D., Popescu, D., Yan, G., & Olariu, S. (2011). Enhancing vanet performance by joint adaptation of transmission power and contention window size. Transactions on Parallel and Distributed Systems, 22(9), 1528–1535.European-ITS (2009) Eits-technical report 102 638 v1.1.1, European Telecommunications Standards Institute (ETSI), http://www.etsi.org/WebSite/homepage.aspxNHTSA, I. Joint program office”, report to congress on the national highway traffic safety administration its program, program progress during 1992–1996 and strategic plan for 1997–2002, US Department of Transportation, Washington, DC.Godbole, D., Sengupta, R., Misener, J., Kourjanskaia, N., & Michael, J. (1998). Benefit evaluation of crash avoidance systems. Transportation Research, 1621(1), 1–9.Reinders, R., van Eenennaam, M., Karagiannis, G., & Heijenk, G. (2004). Contention window analysis for beaconing in vanets. In Proceedings of the 2011 IEEE international conference on wireless communications and mobile computing (IWCMC), Istanbul (pp. 1481–1487).Yang, L., Guo, J., & Wu, Y. (2008). Channel adaptive one hop broadcasting for vanets. In Proceedings of the 2008 IEEE international conference on intelligent transportation systems, Beijing (pp. 369–374).Tseng, Y., Ni, S., Chen, Y., & Sheu, J. (2002). The broadcast storm problem in a mobile ad hoc network. Wireless Networks, 8(2), 153–167.van Eenennaam, E. M., Karagiannis, G., & Heijenk, G. (2010). Towards scalable beaconing in vanets. In Proceedings of the 2010 ERCIM workshop on eMobility, Lulea (pp. 103–108).Ros, F., Ruiz, P., & Stojmenovic, I. (2012). Acknowledgment-based broadcast protocol for reliable and efficient data dissemination in vehicular ad-hoc networks. IEEE Transactions on Mobile Computing, 11(1), 33–46.Torrent-Moreno, M., Santi, P., & Hartenstein, H. (2006). Distributed fair transmit power adjustment for vehicular ad hoc networks. In Proceedings of the 2007 IEEE international conference on sensor and ad hoc communications and networks, Reston, VA (pp. 479–488).Artimy, M. (2007). Local density estimation and dynamic transmission-range assignment in vehicular ad hoc networks. IEEE Transactions on Intelligent Transportation Systems, 8(3), 400–412.Caizzone, G., Giacomazzi, P., Musumeci, L., & Verticale, G. (2005). A power control algorithm with high channel availability for vehicular ad hoc networks. In Proceedings of the 2005 IEEE international conference on communications, Seoul (pp. 3171–3176).Torrent-Moreno, M., Santi, P., & Hartenstein, H. (2009). Vehicle-to-vehicle communication: Fair transmit power control for safety critical information. IEEE Transaction for Vehicular Technology, 58(7), 3684–3703.Torrent-Moreno, M., Schmidt-Eisenlohr, F., Fubler, H., & Hartenstein, H. (2006). Effects of a realistic channel model on packet forwarding in vehicular ad hoc networks. In Proceedings of the 2007 IEEE conference on wireless communications and networking, USA (pp. 385–391).NS, Network simulator (June 2011). http://nsnam.isi.edu/nsnam/index.php/MainPageNakagami, M. (1960). The m-distribution: A general formula of intensity distribution of rapid fadinge. In W. C. Hoffman (Ed.), Statistical method of radio propagation. New York: Pergamon Press.Narayanaswamy, S., Kawadia, V., Sreenivas, R., & Kumar, P. (2002). Power control in ad-hoc networks: Theory, architecture, algorithm and implementation of the compow protocol. In Proceedings of the 2002 European wireless conference next generation wireless networks: technologies, protocols, Italy (pp. 1–6).Cheng, P., Lee, K., Gerla, M., & Harri, J. (2010). Geodtn+ nav: Geographic dtn routing with navigator prediction for urban vehicular environments. Mobile Networks and Applications, 15(1), 61–82.Gomez, J., & Campbell, A. (2004). A case for variable-range transmission power control in wireless multihop networks. In Proceedings twenty-third annual joint conference of the IEEE computer and communications societies, Hong kong (pp. 1425–1436).Ramanathan, R., & Rosales-Hain, R. (2000). Topology control of multihop wireless networks using transmit power adjustment. In Proceedings nineteenth annual joint conference of the IEEE computer and communications societies, Hong kong (pp. 404–413).Artimy, M., Robertson, W., & Phillips, W. (2005). Assignment of dynamic transmission range based on estimation of vehicle density. In Proceedings of the 2nd ACM international workshop on vehicular ad hoc networks, Germany (pp. 40–48).Samara, G., Ramadas, S., & Al-Salihy, W. (2010). Safety message power transmission control for vehicular ad hoc networks. Computer Science, 6(10), 1027–1032.Rezaei, S., Sengupta, R., Krishnan, H., Guan, X., & Student, P. (2008). Adaptive communication scheme for cooperative active safety system.Rezaei, S., Sengupta, R., Krishnan, H., & Guan, X. (2007). Reducing the communication required by dsrc-based vehicle safety systems. In Proceedings of the 2007 IEEE international conference on intelligent transportation systems, Bellevue, WA (pp. 361–366).Sommer, C., Tonguz, O., & Dressler, F. (2011). Traffic information systems: Efficient message dissemination via adaptive beaconing. IEEE Communications Magazine, 49(5), 173–179.Thaina, C., Nakorn, K., & Rojviboonchai, K. (2011). A study of adaptive beacon transmission on vehicular ad-hoc networks. In Proceeding of the 2011 IEEE 13th international conference on communication technology (ICCT), Vancouver (pp. 597–602).Boukerche, A., Rezende, C., & Pazzi, R. (2009). Improving neighbor localization in vehicular ad hoc networks to avoid overhead from periodic messages. In Proceedings of the 2009 IEEE global telecommunications conference, USA (pp. 1–6).Bai, F., Sadagopan, N., & Helmy, A. (2008). Important: A framework to systematically analyze the impact of mobility on performance of routing protocols for adhoc networks. In Proceedings of the 2003 22th annual joint conference of the IEEE computer and communications, USA (pp. 825–835).Nguyen, H., Bhawiyuga, A., & Jeong, H. (2012). A comprehensive analysis of beacon dissemination in vehicular networks. In Proceedings of the 75th IEEE vehicular technology conference, Korea (pp. 1–5).Djahel, S., & Ghamri-Doudane, Y. (2012). A robust congestion control scheme for fast and reliable dissemination of safety messages in vanets. In Proceeding of the 2012 IEEE conference wireless communications and networking, Paris, France (pp. 2264–2269).O. Technologies (Augast 2012) Opnet modeler, http://www.opnet.com/Huang, C., Fallah, Y., Sengupta, R., & Krishnan, H. (2010). Adaptive intervehicle communication control for cooperative safety systems. IEEE Network, 24(1), 6–13.OPNET (June 2012) Opnet modeler, http://www.opnet.com/Kerner, B. (2004). The physics of traffic: Empirical freeway pattern features, engineering applications, and theory. Berlin: Springer.Vinel, A., Vishnevsky, V., & Koucheryavy, Y. (2008). A simple analytical model for the periodic broadcasting in vehicular ad-hoc networks. In Proceedings of the 2008 IEEE international GLOBECOM workshops, Philadelphia, PA (pp. 1–5).Mariyasagayam, N., Menouar, H., & Lenardi, M. (2009). An adaptive forwarding mechanism for data dissemination in vehicular networks. In Proceedings of the 2009 IEEE Vehicular Networking Conference, Boston (pp. 1–5).Hung, C., Chan, H., & Wu, E. (2008). Mobility pattern aware routing for heterogeneous vehicular networks. In Proceedings of the 2008 international conference on wireless communications and networking, Las Vegas (pp. 2200–2205).Yang, K., Ou, S., Chen, H., & He, J. (2007). A multihop peer-communication protocol with fairness guarantee for ieee 802.16-based vehicular networks. IEEE Transactions on Vehicular Technology, 56(6), 3358–3370.Lequerica, I., Ruiz, P., & Cabrera, V. (2010). Improvement of vehicular communications by using 3G capabilities to disseminate control information. IEEE Network Magazine, 24(1), 32–38.Oh, D., Kim, P., Song, J., Jeon, S., & Lee, H. (2005). Design considerations of satellite-based vehicular broadband networks. IEEE Wireless Communications Magazine, 12(5), 91–97.Ko, Y., Sim, M., & Nekovee, M. (2006). Wi-fi based broadband wireless access for users on the road. BT Technology Journal, 24(2), 123–129.Choffnes, D., & Bustamante, F. (2005). An integrated mobility and traffic model for vehicular wireless networks. In Proceedings of the 2005 ACM international workshop on vehicular ad hoc networks, Cologne (pp. 69–78).TIGER (October 2010) Topologically integrated geographic encoding and referencing system, http://www.census.gov/geo/www/tiger/Mittag, J., Thomas, F., Harri, J., & Hartenstein, H. (2009). A comparison of single and multi-hop beaconing in vanets. In Proceedings of the 2009 ACM international workshop on vehiculaar internetworking, Beijing (pp. 69–78).Rappaport, T. (1996). Wireless communications: Principles and practice (2nd ed.). New Jersey: Prentice Hall PTR

Low-Complexity Energy-Efficient Broadcasting in One-Dimensional Wireless Networks

In this paper, we investigate the transmission range assignment for N wireless nodes located on a line (a linear wireless network) for broadcasting data from one specific node to all the nodes in the network with minimum energy. Our goal is to find a solution that has low complexity and yet performs close to optimal. We propose an algorithm for finding the optimal assignment (which results in the minimum energy consumption) with complexity O(N^2). An approximation algorithm with complexity O(N) is also proposed. It is shown that, for networks with uniformly distributed nodes, the linear-time approximate solution obtained by this algorithm on average performs practically identical to the optimal assignment. Both the optimal and the suboptimal algorithms require the full knowledge of the network topology and are thus centralized. We also propose a distributed algorithm of negligible complexity, i.e., with complexity O(1), which only requires the knowledge of the adjacent neighbors at each wireless node. Our simulations demonstrate that the distributed solution on average performs almost as good as the optimal one for networks with uniformly distributed nodes.Comment: 17 page