In Fig. 4.3, we show how to integrate the above coverage and connectivity determina-tion protocol, sleep protocol, and power control protocol together into one protocol.
The purpose is to save energy while maintaining the quality of the network. Ba-sically, these sub-protocols are executed in that order. We assume that the goal is to achieve a ks-covered, kc-connected network, where ks ≥ kc. In particular, we set up two timers, one called Tsleep for sleeping sensors to wake up themselves, and one called Tcycle for sensors to re-check their local coverage and connectivity (this is to prevent neighboring sensors from running out of batteries, thus resulting in a network weaker than ks-covered and kc-connected). Also, a new HELP message is designed for sensors to call others’ assistance to increase the coverage and connec-tivity of the network (if possible) when some sensors run out of energy. Note that whenever a sensor goes to the initial state, it will use the largest transmission power to determine its local network coverage and connectivity. For example, this applies to a sensor when it receives a HELP message under a reduced transmission power status.
Chapter 5
Simulation Results
In this section, we present two sets of simulation experiments. Experiment 1 tests the network coverage and connectivity at different sensing ranges and communication ranges. Experiment 2 evaluates the performance of the proposed energy-saving protocol.
5.1 Experiment 1: Coverage and Connectivity
We have developed a simulator to compare the network coverage and connectivity calculated by Theorem 5 and by an exhausted search algorithm. All results in this section are from averages of at least 100 runs. The simulation environment is a 100x100 square area, on which sensors are randomly deployed. The sensing range and communication range of each sensor are uniformly distributed in certain ranges.
Fig. 5.1 shows the coverage and connectivity under different communication ranges. Note that Theorem 5 may not be able to find the exact coverage and con-nectivity levels because it only relies on local information. Our goal is to compare the results obtained by Theorem 5 (which implies coverage as well as connectivity) against the minimum of the actual coverage and actual connectivity obtained by an exhausted search. So Fig. 5.1(a) represents an ideal situation because what are found by Theorem 5 match closely with the actual values. The gaps increase as we move to Fig. 5.1(b), (c), and (d). This is because the ratios of average communi-cation range to average sensing range are reduced, which means that a sensor may not be able to know the existence of another sensors which have overlapping with its own sensing range if it only examines its direct neighbors. So a certain degrees
Figure 5.1: Network coverage and connectivity under different communication ranges.
Figure 5.2: Network coverage and connectivity under different means and variations of communication ranges.
of coverage and connectivity are not discovered by Theorem 5.
Next, we keep the sensing ranges fixed, but change the communication ranges variations. Fig. 5.2 shows the coverage and connectivity in a 300-nodes network when we vary the mean and variation of communication ranges. Note that in each point of Fig. 5.2(a), sensors’ communication ranges have no variation, while in each point of Fig. 5.2(b), the variation range is 20. As can be seen, although in both cases Theorem 5 finds about the same values of coverage and connectivity, since the actual connectivity reduces, Theorem 5 actually matches closer to the actual situations in the case of Fig. 5.2(b). In Fig. 5.3, we conduct the similar simulation by keeping the communication ranges unchanged but changing the mean and variation of sensing ranges. The trend is similar – Theorem 5 matches closer to the actual situations when there are larger variations in sensing ranges. Also, by comparing Fig. 5.2 and Fig. 5.3, we observe that the gaps reduce when the ratios of average communication range to average sensing range increase. The reason is that as the ratio increases, a sensor is able to collect more information about its neighborhood.
5.2 Experiment 2: Network Life Time
This section verifies our integrated energy-saving protocol for prolonging network lifetime while ensuring both coverage and communication quality. We consider three performance metrics: number of alive nodes, coverage level, and connectivity level.
In these experiments, there are 300 sensors randomly deployed in a 100x100 square area with sensing range = 15 ∼ 25, communication range = 30 ∼ 50, and initial energy = 8000 ∼ 12000 (all in a uniform distribution). Our goal is to achieve a ks
-Figure 5.3: Network coverage and connectivity under different means and variations of sensing ranges.
covered and kc-connected network. We sample the network status every 10 seconds.
For each sensor, the energy consumed every second is proportional to the sum of its sensing range and communication range. Two versions of protocols are evaluated, one with the Sleep protocol only and the other with Sleep+Power Control protocol (denoted by Sleep+PC). We compare our results against a naive protocol, where all sensors are always active, and the CCP+SPAN protocol [24]. CCP (Coverage Configuration Protocol) is a protocol that can dynamically configure a network to achieve guaranteed degrees of coverage and connectivity if sensors’ communication ranges are no less than twice their sensing ranges. If sensors’ communication ranges are less than twice their sensing ranges, [24] suggests to integrate CCP with SPAN, which is a decentralized protocol that tries to conserve energy by turning off un-necessary nodes while maintaining a communication backbone composed of active nodes.
Fig. 5.4(a) shows the number of alive sensors when the goal is to maintain a 2-covered and 1-connected network. In the naive protocol, because nodes are always active, the number of alive sensors drops sharply at around 150 sec. Sensors in CCP+SPAN protocol fail at a slower speed. Both Sleep and Sleep+PC protocols can significantly reduce the rate that sensors fail. Overall, Sleep+PC performs the best. This can be explained by the levels of coverage and connectivity provided by a protocol, as shown in Fig. 5.4(b) and Fig. 5.4(c). There is too much redundancy in coverage and connectivity in both the naive and CCP+SPAN protocols. The Sleep protocol maintains the level of coverage pretty well, but the level of connectivity is still much higher than our expectation. Only Sleep+PC can maintain the best-fit coverage and connectivity levels. This justifies the usefulness of adopting power
Na ve CCP+SPAN Sleep Sleep+PC Na ve CCP+SPAN Sleep Sleep+PC
Figure 5.4: Comparisons of the naive, CCP+SPAN, Sleep, and Sleep+PC protocols.
Figure 5.5: Network lifetime under different communication ranges (Sensing Range
= 15 ∼ 25).
Figure 5.6: Network lifetime under different sensing ranges (Communication Range
= 30 ∼ 50).
Figure 5.7: Network lifetime under different coverage and connectivity requirements (Sensing Range = 15 ∼ 25 and Communication Range = 30 ∼ 50).
control to adjust the communication topology of the network. Fig. 5.4(d) shows the network lifetime, which is defined as the time before the levels of coverage and connectivity drop below our expectations. The lifetime of the naive protocol is around 150 sec. The lifetime of CCP+SPAN is around 200 sec. The Sleep and Sleep+PC protocols can significantly prolong network lifetime to around 340 and 410 sec., respectively. Fig. 5.4(e), (f), (g), and (h) are from similar experiments when the goal is to maintain a 3-covered and 2-connected network. The trend is similar.
In the following, only the network lifetime is shown. Fig. 5.5 shows the network lifetime under the same sensing range (15∼ 25) but different communication ranges.
In all situations, Sleep+PC performs the best. In fact, when the communication range increases, the gaps between Sleep+PC and other protocols enlarge relatively.
So power control can effectively reduce network connectivity and prolong network lifetime, especially when communication ranges are relatively larger than sensing ranges. Fig. 5.6 shows the similar experiments under the same communication range (30 ∼ 50) but different sensing ranges. In Fig. 5.7, we further test under different coverage and connectivity requirements. Around 1 to 2 times more lifetime can be seen when comparing Sleep+PC to CCP+SPAN.
Chapter 6
Conclusions and Future Work
We have proposed fundamental theorems for determining the levels of coverage and connectivity of a sensor network. Earlier works are all based on stronger assump-tions that the sensing distances and communication distances of sensors must satisfy some relations. We study this issue under an arbitrary relationship between sensing and communication ranges. Based on the proposed theorems, we have developed distributed protocols for determining the levels of coverage and connectivity of a sensor network and even for adjusting a sensor network to achieve the expected levels of coverage and connectivity. The approaches that we take are to put some sensors into the sleep mode and to reduce some sensors’ transmission power. As far as we know, the combination of these mechanisms has not been well studied in this field, especially when coverage and connectivity issues are concerned. In our work, a deterministic model is used to formulate sensors’ sensing and communica-tion ranges. In reality, these values may follow a probabilistic model (such as a sensor can successfully detect an object in a distance d with a probability prob(d)).
The coverage-connectivity-combined issue still requires further investigation in this direction.
Bibliography
[1] Z. Abrams, A. Goel, and S. Plotkin. Set K-cover algorithms for energy effi-cient monitoring in wireless sensor networks. In Int’l Symp. on Information Processing in Sensor Networks (IPSN), pages 424–432, 2004.
[2] P. Bahl and V. N. Padmanabhan. RADAR: An in-building RF-based user location and tracking system. In IEEE INFOCOM, pages 775–784, 2000.
[3] D. Braginsky and D. Estrin. Rumor routing algorithm for sensor networks. In ACM Int’l Workshop on Wireless Sensor Networks and Applications (WSNA), 2002.
[4] M. Burkhart, P. von Rickenbach, R. Wattenhofer, and A. Zollinger. Does topology control reduce interference? In ACM Int’l Symp. on Mobile Ad Hoc Networking and Computing (MobiHOC), pages 9 – 19, 2004.
[5] B. Chen, K. Jamieson, H. Balakrishnan, and R. Morris. Span: an energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks. ACM/Kluwer Wireless Networks, 8(5):481–494, Sep. 2002.
[6] D. Ganesan, R. Govindan, S. Shenker, and D. Estrin. Highly resilient, energy efficient multipath routing in wireless sensor networks. ACM Mobile Comput.
and Commun. Review, 5(4):11–25, Oct. 2001.
[7] H. Gupta, S. R. Das, and Q. Gu. Connected sensor cover: Self-organization of sensor networks for efficient query execution. In ACM Int’l Symp. on Mobile Ad Hoc Networking and Computing (MobiHOC), pages 189–200, 2003.
[8] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan. Energy-efficient communication protocols for wireless microsensor networks. In Hawaii Int’l Conf. on Systems Science (HICSS), 2000.
[9] C.-F. Huang and Y.-C. Tseng. The coverage problem in a wireless sensor net-work. In ACM Int’l Workshop on Wireless Sensor Networks and Applications (WSNA), pages 115–121, 2003.
[10] C.-F. Huang, Y.-C. Tseng, and L.-C. Lo. The coverage problem in three-dimensional wireless sensor networks. In IEEE Global Telecommunications Conference (Globecom), 2004.
[11] M. Inanc, M. Magdon-Ismail, and B. Yener. Power optimal connectivity and coverage in wireless sensor networks. Technical Report TR-03-06, Department of Computer Science, Rensselaer Polytechnic Institute, July 2003.
[12] N. Li and J. C. Hou. FLSS: A fault-tolerant topology control algorithm for wireless networks. In ACM Int’l Conf. on Mobile Computing and Networking (MobiCom), 2004.
[13] X.-Y. Li, P.-J. Wan, and O. Frieder. Coverage in wireless ad hoc sensor net-works. IEEE Trans. Comput., 52(6):753–763, June 2003.
[14] S. Meguerdichian, F. Koushanfar, M. Potkonjak, and M. B. Srivastava. Cover-age problems in wireless ad-hoc sensor networks. In IEEE INFOCOM, pCover-ages 1380–1387, 2001.
[15] S. Meguerdichian, F. Koushanfar, G. Qu, and M. Potkonjak. Exposure in wireless ad-hoc sensor networks. In ACM Int’l Conf. on Mobile Computing and Networking (MobiCom), pages 139–150, 2001.
[16] G. J. Pottie and W. J. Kaiser. Wireless integrated network sensors. Commun.
ACM, 43(5):51–58, May 2000.
[17] A. Savvides, C.-C. Han, and M. B. Strivastava. Dynamic fine-grained localiza-tion in ad-hoc networks of sensors. In ACM Int’l Conf. on Mobile Computing and Networking (MobiCom), pages 166–179, 2001.
[18] S. Shakkottai, R. Srikant, and N. Shroff. Unreliable sensor grids: coverage, connectivity and diameter. In IEEE INFOCOM, pages 1073 – 1083, 2003.
[19] E. Shih, S.-H. Cho, N. Ickes, R. Min, A. Sinha, A. Wang, and A. Chandrakasan.
Physical layer driven protocol and algorithm design for energy-efficient wireless
sensor networks. In ACM Int’l Conf. on Mobile Computing and Networking (MobiCom), pages 272–287, 2001.
[20] K. Sohrabi, J. Gao, V. Ailawadhi, and G. J. Pottie. Protocols for self-organization of a wireless sensor network. IEEE Personal Commun., 7(5):16–27, Oct. 2000.
[21] D. Tian and N. D. Georganas. A node scheduling scheme for energy conserva-tion in large wireless sensor networks. Wireless Commun. and Mobile Comput.
(WCMC), 3:271–290, 2003.
[22] Y.-C. Tseng, S.-P. Kuo, H.-W. Lee, and C.-F. Huang. Location tracking in a wireless sensor network by mobile agents and its data fusion strategies. In Int’l Symp. on Information Processing in Sensor Networks (IPSN), 2003.
[23] G. Veltri, Q. Huang, G. Qu, and M. Potkonjak. Minimal and maximal exposure path algorithms for wireless embedded sensor networks. In ACM Int’l Conf. on Embedded Networked Sensor Systems (SenSys), pages 40–50, 2003.
[24] X. Wang, G. Xing, Y. Zhang, C. Lu, R. Pless, and C. Gill. Integrated coverage and connectivity configuration in wireless sensor networks. In ACM Int’l Conf.
on Embedded Networked Sensor Systems (SenSys), pages 28–39, 2003.
[25] R. Wattenhofer, L. Li, P. Bahl, and Y.-M. Wang. Distributed topology control for power efficient operation in multihop wireless ad hoc networks. In IEEE INFOCOM, pages 1388 – 1397, 2001.
[26] A. Woo and D. E. Culler. A transmission control scheme for media access in sensor networks. In ACM Int’l Conf. on Mobile Computing and Networking (MobiCom), pages 221–235, 2001.
[27] Y. Xu, J. Heidemann, and D. Estrin. Geography-informed energy conservation for ad hoc routing. In ACM Int’l Conf. on Mobile Computing and Networking (MobiCom), pages 70–84, 2001.
[28] T. Yan, T. He, and J. A. Stankovic. Differentiated surveillance for sensor net-works. In ACM Int’l Conf. on Embedded Networked Sensor Systems (SenSys), pages 51–62, 2003.
[29] W. Ye, J. Heidemann, and D. Estrin. An energy-efficient MAC protocol for wireless sensor networks. In IEEE INFOCOM, pages 1567–1576, 2002.
[30] H. Zhang and J. C. Hou. Maintaining sensing coverage and connectivity in large sensor networks. In NSF International Workshop on Theoretical and Algorithmic Aspects of Sensor, Ad Hoc Wireless, and Peer-to-Peer Networks, 2004.
[31] H. Zhang and J. C. Hou. On deriving the upper bound of lifetime for large sensor networks. In ACM Int’l Symp. on Mobile Ad Hoc Networking and Computing (MobiHOC), pages 121–132, 2004.
Curriculum Vita
Hsiao-Lu Wu (hlwu@csie.nctu.edu.tw) received her B.S. degree in Computer Science from the National Chiao-Tung University, Taiwan, in 2003. Her research interests include wireless network, sensor network and ad hoc network.