attributes accuracy, reliability and timeliness has proved to be important in the co-design. Moreover, it proves to be a necessity for any information qual-ity aware design to consider the user requirements and also the respective attributes and metrics. On the other hand, it is trivial to make sure to have the user view and design view for the optimal deployment of the WSNs and to achieve the optimal results.
Second, as it has been quite complex to achieve the goal of information quality aware co-design, it is necessary to first tackle the information trans-port block. It is also trivial to know that the tunability of the information transport makes the co-design more user oriented. The reliability and time-liness attributes are always interdependent and whenever one or the other attribute is modified, there is always an effect on the other. Moreover, the tunable information transport lays out the first path to consider the sampling block and also it proves that reliability is a hidden requirement of accuracy.
Third, to ensure the co-design, exploiting the spatial correlation is impor-tant. The complex problem of sampling and information transport co-design provides the first basic step towards the optimal combination of the both functional blocks. On the other hand, considering accuracy as a user re-quirement and combining it with reliability proves the first cross-operation of both the functional blocks.
Finally, to represent the physical phenomenon accurately by selecting the representative nodes is necessary for optimal network performance. The op-timal co-design of sampling and information transport needs a very complex technique and indeed needs a realistic modeling of each of the attribute (ac-curacy, reliability and timeliness). For the optimal deployment of WSNs, the key goal of our thesis, which was achieved through the multi-attribute co-relation and the optimized function of sampling and information trans-port, is trivial. On the other hand, the optimal co-design also maximizes the efficiency by saving resources such as energy. Finally, the core part of the thesis has been achieved through these lessons which we have learned.
9.3 Open Ends - Basis for Future Work
While the work presented in this thesis addressed the research questions driving it towards making the discussed contributions, it also opened new and interesting research perspectives along its way. In the following, we briefly present some of the most promising ones.
In-network Processing Co-design
As we have shown in our information assessment, it would be interesting to consider the in-network processing functional block for the co-design. The challenge lies in how could the information be processed within the network and also will it be possible to maximize the efficiency by saving resources.
On the other hand, mapping the relevant attributes to the in-network pro-cessing block and combining it with the provided co-design along with the accuracy, reliability and timeliness would be a bigger challenge. Moreover, working on the optimal co-design of sampling, information transport and in-network processing to minimize the loss of information, to satisfy evolv-able user requirements and maximize the efficiency is very interesting and challenging.
Information Quality Attributes and Metrics
Though information attributes are relatively well discussed, information met-rics definition and their efficient computation are still in their infancy. Ac-cordingly, the future research directions may progress on the aspects of defin-ing attributes and metrics and the techniques to efficiently compute them on the fly in all information extraction stages. However, as one need to narrow research into fewer attributes, one will take some must considered attributes during the flow of information from the source to the sink. One can define and defend how it is relevant and necessary to use these attributes and viola-tion of this lead to informaviola-tion which does not satisfy the user requirements.
Metrics and their run-time quantification represent a powerful tool to assess the dependability of WSN, which allows for efficient and tunable information quality provisioning.
Tuning and Adapting MAC-protocols
Tuning and adapting the existing MAC-protocols for the generic co-design.
As the MAC-protocols are very important in the co-design, it would be a challenge in itself to tune and adapt the MAC-protocols according to the evolvable user requirements. On the other hand, it would be also interesting to try various different MAC-protocols in combination with our provided optimal co-design.
Minimize Information Loss
As we have always tried to maximize the efficiency, it would be also interest-ing to minimize the loss of information. It would be a challenge to consider
9.3. OPEN ENDS - BASIS FOR FUTURE WORK 129 the information loss as the cost function in the optimal co-design. Trying to adapt with different evovlable user requirements of accuracy, reliability and timeliness and satisfying them while minimizing the loss of information would play a vital and challenging role. As an example, Akaike’s information criterion (Akaike’s information criterion, is a measure of the goodness of fit of an estimated statistical model grounded on the concept of entropy) can be used to measure information quality when certain information is lost from the source to the sink.
Heterogeneous WSNs and Applications
As a preliminary effort we have started to explore the possibilities of using our co-design for mobility assisted WSNs. Alongside mobility, heterogeneous sensing as well heterogeneous mobile nodes can be a part of WSNs. It is worth investigating to include different modules in our co-design which keeps the heterogeneity of the environment and devices intact while providing ap-plication specific accuracy, reliability and timeliness.
Bibliography
A. M. Abbas and O. Kure. Quality of service in mobile ad hoc networks: a survey. International Journal of Ad Hoc and Ubiquitous Computing, 6(2):
75–98, 2010.
I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. Wireless sensor networks: a survey. Computer Networks, 38(4):393–422, 2002a.
I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. Wireless sensor networks: a survey. Computer networks, 38(4):393–422, 2002b.
J. N. Al-Karaki and A. E. Kamal. Routing techniques in wireless sensor networks: a survey. Wireless Communications, IEEE, 11(6):6–28, 2004.
M. Anwar Hossain, P. K. Atrey, and A. El Saddik. Context-aware qoi com-putation in multi-sensor systems. In Mobile Ad Hoc and Sensor Systems, 2008. MASS 2008. 5th IEEE International Conference on, pages 736–741.
IEEE, 2008.
S. Arnborg, J. Brynielsson, H. Artman, and K. Wallenius. Information aware-ness in command and control: Precision, quality, utility. In Information Fusion, 2000. FUSION 2000. Proceedings of the Third International Con-ference on, volume 2, pages THB1–25. IEEE, 2000.
A. Arora, P. Dutta, S. Bapat, and V. K. et al. A line in the sand: a wireless sensor network for target detection, classification, and tracking. Computer Networks, 46(5):605–634, 2004.
L. Atzori, A. Iera, and G. Morabito. The internet of things: A survey.
Computer networks, 54(15):2787–2805, 2010.
D. Ballou, R. Wang, H. Pazer, and G. K. Tayi. Modeling information manu-facturing systems to determine information product quality. Management Science, 44(4):462–484, 1998.
131
J. Barbancho, C. Le´on, F. J. Molina, and A. Barbancho. A new qos rout-ing algorithm based on self-organizrout-ing maps for wireless sensor networks.
Telecommunication Systems, 36(1-3):73–83, 2007.
C. Bisdikian. On sensor sampling and quality of information: A starting point. In Pervasive Computing and Communications Workshops, 2007.
PerCom Workshops’ 07. Fifth Annual IEEE International Conference on, pages 279–284. IEEE, 2007.
C. Bisdikian, J. Branch, K. K. Leung, and R. I. Young. A letter soup for the quality of information in sensor networks. InPervasive Computing and Communications, 2009. PerCom 2009. IEEE International Conference on, pages 1–6. IEEE, 2009a.
C. Bisdikian, L. M. Kaplan, M. B. Srivastava, D. J. Thornley, D. Verma, and R. I. Young. Building principles for a quality of information specification for sensor information. In Information Fusion, 2009. FUSION’09. 12th International Conference on, pages 1370–1377. IEEE, 2009b.
Q. Chang, X. Wang, and X.-L. Wang. Local information embedding by di-mension extension and information risk minimization. In Machine Learn-ing and Cybernetics, 2005. ProceedLearn-ings of 2005 International Conference on, volume 7, pages 4335–4339. IEEE, 2005.
Z. Charbiwala, S. Zahedi, Y. Kim, Y. Cho, and M. Srivastava. Toward quality of information aware rate control for sensor networks. In Fourth International Workshop on Feedback Control Implemenation and Design in Computing Systems and Networks, 2009.
D. Chen and P. K. Varshney. Qos support in wireless sensor networks: A survey. In International Conference on Wireless Networks, volume 233, 2004.
C.-Y. Chong and S. P. Kumar. Sensor networks: evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8):1247–1256, 2003.
F. C. Delicato, P. F. Pires, L. Pinnez, L. Fernando, and L. da Costa. A flexible web service based architecture for wireless sensor networks. In Distributed Computing Systems Workshops, 2003. Proceedings. 23rd In-ternational Conference on, pages 730–735. IEEE, 2003.
J. Dutka. The incomplete beta functionˆaa historical profile. Archive for history of exact sciences, 24(1):11–29, 1981.
BIBLIOGRAPHY 133 S. A. Ehikioya. A characterization of information quality using fuzzy logic. In Fuzzy Information Processing Society, 1999. NAFIPS. 18th International Conference of the North American, pages 635–639. IEEE, 1999.
E. Felemban, C.-G. Lee, and E. Ekici. Mmspeed: multipath multi-speed protocol for qos guarantee of reliability and. timeliness in wireless sensor networks. Mobile Computing, IEEE Transactions on, 5(6):738–754, 2006.
E. Gelenbe and L. Hey. Quality of information: An empirical approach. In Mobile Ad Hoc and Sensor Systems, 2008. MASS 2008. 5th IEEE Inter-national Conference on, pages 730–735. IEEE, 2008.
A. Ghosh and S. K. Das. Coverage and connectivity issues in wireless sensor networks: A survey.Pervasive and Mobile Computing, 4(3):303–334, 2008.
O. Gnawali, R. Fonseca, K. Jamieson, D. Moss, and P. Levis. Collection tree protocol. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, pages 1–14. ACM, 2009.
P. E. Guerrero, A. P. Buchmann, A. Khelil, and K. Van Laerhoven. Tudµnet, a metropolitan-scale federation of wireless sensor network testbeds. sen-sors, 20:Z1s, 2012.
P. E. Guerrero, I. Gurov, S. Santini, and A. Buchmann. On the selection of testbeds for the evaluation of sensor network protocols and applications. In Signal Processing Advances in Wireless Communications (SPAWC), 2013 IEEE 14th Workshop on, pages 495–499. IEEE, 2013.
P. Gupta and P. R. Kumar. The capacity of wireless networks. Information Theory, IEEE Transactions on, 46(2):388–404, 2000.
D. Hakkarinen and Q. Han. Data quality driven sensor reporting. In Mobile Ad Hoc and Sensor Systems, 2008. MASS 2008. 5th IEEE International Conference on, pages 772–777. IEEE, 2008.
Q. Han and N. Venkatasubramanian. Timeliness-accuracy balanced collec-tion of dynamic context data. Parallel and Distributed Systems, IEEE Transactions on, 18(2):158–171, 2007.
T. He and M. Zafer. Adaptive sampling for transient signal detection in the presence of missing samples. InMobile Ad Hoc and Sensor Systems, 2008.
MASS 2008. 5th IEEE International Conference on, pages 760–765. IEEE, 2008.
L. Hey and E. Gelenbe. Adaptive packet prioritisation for large wireless sensor networks. Telecommunication Systems, 48(1-2):125–150, 2011.
X. Huang and Y. Fang. Multiconstrained qos multipath routing in wireless sensor networks. Wireless Networks, 14(4):465–478, 2008.
U. Hunkeler and P. Scotton. A quality-of-information-aware framework for data models in wireless sensor networks. In Mobile Ad Hoc and Sensor Systems, 2008. MASS 2008. 5th IEEE International Conference on, pages 742–747. IEEE, 2008.
B. Jiang, B. Ravindran, and H. Cho. Cflood: A constrained flooding protocol for real-time data delivery in wireless sensor networks. In Stabilization, Safety, and Security of Distributed Systems, pages 413–427. Springer, 2009.
R. Kannan, S. Sarangi, S. S. Iyengar, and L. Ray. Sensor-centric quality of routing in sensor networks. In INFOCOM 2003. Twenty-Second An-nual Joint Conference of the IEEE Computer and Communications. IEEE Societies, volume 1, pages 692–701. IEEE, 2003.
K. Karenos and V. Kalogeraki. Real-time traffic management in sensor net-works. In Real-Time Systems Symposium, 2006. RTSS’06. 27th IEEE In-ternational, pages 422–434. IEEE, 2006.
K. Karenos, V. Kalogeraki, and S. V. Krishnamurthy. A rate control frame-work for supporting multiple classes of traffic in sensor netframe-works. In Real-Time Systems Symposium, 2005. RTSS 2005. 26th IEEE International, pages 11–pp. IEEE, 2005.
R. T. Kessel. The dynamics of information fusion: Synthesis versus misas-sociation. In Information Fusion, 2006 9th International Conference on, pages 1–6. IEEE, 2006.
A. Khelil and N. Suri. Gossiping: Adaptive and reliable broadcasting in manets. In Dependable Computing, pages 123–141. Springer, 2007.
A. Khelil, F. K. Shaikh, A. Ali, and N. Suri. gmap: efficient construction of global maps for mobility-assisted wireless sensor networks. In Wire-less On-Demand Network Systems and Services, 2009. WONS 2009. Sixth International Conference on, pages 189–196. IEEE, 2009.
A. Khelil, C. Reinl, B. Ayari, F. K. Shaikh, P. Szczytowski, A. Ali, and N. Suri. Wireless sensor cooperation for a sustainable quality of informa-tion1. Pervasive Computing and Networking, pages 71–100, 2011.
BIBLIOGRAPHY 135 S. Kim and S. O. Yang. Wireless sensor gathering data during long time.
In Ubiquitous Information Technologies and Applications (CUTE), 2010 Proceedings of the 5th International Conference on, pages 1–5. IEEE, 2010.
S. Kim, R. Fonseca, and D. Culler. Reliable transfer on wireless sensor networks. In IEEE Sensor and Ad Hoc Communications and Networks (SECON), pages 449– 459, 2004.
L. Krishnamachari, D. Estrin, and S. Wicker. The impact of data aggre-gation in wireless sensor networks. In Distributed Computing Systems Workshops, 2002. Proceedings. 22nd International Conference on, pages 575–578. IEEE, 2002.
M. Kuorilehto, M. H¨a, T. D. H¨a, et al. A survey of application distribution in wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 1900(5):774–788, 2005.
S. Kwon, J. Shin, J. Ko, and C. Kim. Distributed and localized construction of routing structure for sensor data gathering.Telecommunication Systems, 44(1-2):135–147, 2010.
H. Li, S. Jiang, and G. Wei. Information-accuracy-aware jointly sensing nodes selection in wireless sensor networks. In Mobile Ad-hoc and Sensor Networks, pages 736–747. Springer, 2006.
H. Lin, M. Lu, N. Milosavljevic, J. Gao, and L. J. Guibas. Composable information gradients in wireless sensor networks. In Proceedings of the 7th international conference on Information processing in sensor networks, pages 121–132. IEEE Computer Society, 2008.
C. Lu, B. M. Blum, T. F. Abdelzaher, J. A. Stankovic, and T. He. Rap:
A real-time communication architecture for large-scale wireless sensor net-works. In Real-Time and Embedded Technology and Applications Sympo-sium, 2002. Proceedings. Eighth IEEE, pages 55–66. IEEE, 2002.
J.-F. Mart´ınez, A.-B. Garc´ı, I. Corredor, L. L´opez, V. Hern´andez, and A. Dasilva. Qos in wireless sensor networks: survey and approach. In Proceedings of the 2007 Euro American conference on Telematics and in-formation systems, page 20. ACM, 2007.
A. Meliou, D. Chu, J. Hellerstein, C. Guestrin, and W. Hong. Data gathering tours in sensor networks. InProceedings of the 5th international conference on Information processing in sensor networks, pages 43–50. ACM, 2006.
D. L. Moody, G. Sindre, T. Brasethvik, and A. Sølvberg. Evaluating the quality of information models: empirical testing of a conceptual model quality framework. InProceedings of the 25th International Conference on Software Engineering, pages 295–305. IEEE Computer Society, 2003.
K. Morita, A. Aikebaier, T. Enokido, and M. Takizawa. A data transmis-sion protocol for reliable and energy-efficient data transmistransmis-sion in a wire-less sensor-actuator network. Telecommunication Systems, 38(3-4):71–82, 2008.
M. F. Munir, A. A. Kherani, and F. Filali. On stability and sampling schemes for wireless sensor networks. In Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks and Workshops, 2007. WiOpt 2007. 5th International Symposium on, pages 1–10. IEEE, 2007.
R. A. Nichols. Qoi in dtn-based directional networks. InSarnoff Symposium, 2009. SARNOFF’09. IEEE, pages 1–5. IEEE, 2009.
A. M. Ortiz, F. Royo, T. Olivares, J. C. Castillo, L. Orozco-Barbosa, and P. J. Marron. Fuzzy-logic based routing for dense wireless sensor networks.
Telecommunication Systems, pages 1–11, 2011.
P. Park, C. Fischione, A. Bonivento, K. H. Johansson, and A. Sangiovanni-Vincent. Breath: an adaptive protocol for industrial control applications using wireless sensor networks. Mobile Computing, IEEE Transactions on, 10(6):821–838, 2011.
R. Prasanth, J. Cabrera, J. Amin, R. Mehra, R. Purtell, and R. Smith. Qual-ity of information measures for autonomous decision-making. InAmerican Control Conference, 2004. Proceedings of the 2004, volume 2, pages 1002–
1007. IEEE, 2004.
V. Sachidananda, A. Khelil, and N. Suri. Quality of information in wireless sensor networks: A survey. ICIQ, 2010.
V. Sachidananda, A. Khelil, D. Umap, M. Majuntke, and N. Suri. Trading transport timeliness and reliability for efficiency in wireless sensor net-works. In Networking, Sensing and Control (ICNSC), 2013 10th IEEE International Conference on, pages 720–727. IEEE, 2013.
A. Sahoo and P. Baronia. An energy efficient mac in wireless sensor networks to provide delay guarantee. In Local & Metropolitan Area Networks, 2007.
LANMAN 2007. 15th IEEE Workshop on, pages 25–30. IEEE, 2007.
BIBLIOGRAPHY 137 Y. Sankarasubramaniam, ¨O. B. Akan, and I. F. Akyildiz. Esrt: event-to-sink reliable transport in wireless sensor networks. In Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing, pages 177–188. ACM, 2003.
F. K. Shaikh, A. Khelil, and N. Suri. On modeling the reliability of data transport in wireless sensor networks. InParallel, Distributed and Network-Based Processing, 2007. PDP’07. 15th EUROMICRO International Con-ference on, pages 395–402. IEEE, 2007.
F. K. Shaikh, A. Khelil, B. Ayari, P. Szczytowski, and N. Suri. Generic infor-mation transport for wireless sensor networks. InSensor Networks, Ubiqui-tous, and Trustworthy Computing (SUTC), 2010 IEEE International Con-ference on, pages 27–34. IEEE, 2010.
V. Shnayder, M. Hempstead, B.-r. Chen, G. W. Allen, and M. Welsh. Sim-ulating the power consumption of large-scale sensor network applications.
InProceedings of the 2nd international conference on Embedded networked sensor systems, pages 188–200. ACM, 2004.
J. Srinivasan. The role of trustworthiness in information service usage: The case of parry information kiosks, tamil nadu, india. In Information and Communication Technologies and Development, 2007. ICTD 2007. Inter-national Conference on, pages 1–8. IEEE, 2007.
M. Strasser, A. F. Meier, K. Langendoen, and P. Blum. Dwarf: Delay-aware robust forwarding for energy-constrained wireless sensor networks.
In Proceedings of the 3rd IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS 2007), pages 64–81, June 2007.
P. Szczytowski, A. Khelil, and N. Suri. Asample: Adaptive spatial sam-pling in wireless sensor networks. In Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC), 2010 IEEE International Conference on, pages 35–42. IEEE, 2010.
C. H. J. Tan and D. F. Gillies. Generating reliable quality of information (qoi) metrics for target tracking. InSPIE Defense, Security, and Sensing, pages 733607–733607. International Society for Optics and Photonics, 2009.
H.-X. Tan, M.-C. Chan, W. Xiao, P.-Y. Kong, and C.-K. Tham. Information quality aware routing in event-driven sensor networks. InINFOCOM, 2010 Proceedings IEEE, pages 1–9. IEEE, 2010.
W. L. Tan, O. Yue, and W. C. Lau. Performance evaluation of differentiated services mechanisms over wireless sensor networks. InVehicular Technology Conference, 2006. VTC-2006 Fall. 2006 IEEE 64th, pages 1–5. IEEE, 2006.
A. Tolstikov, C.-K. Tham, and J. Biswas. Quality of information assurance using phenomena-aware resource management in sensor networks. In Net-works, 2006. ICON’06. 14th IEEE International Conference on, volume 1, pages 1–7. IEEE, 2006.
A. Tolstikov, W. Xiao, J. Biswas, S. Zhang, and C.-K. Tham. Information quality management in sensor networks based on the dynamic bayesian network model. In Intelligent Sensors, Sensor Networks and Informa-tion, 2007. ISSNIP 2007. 3rd International Conference on, pages 751–756.
IEEE, 2007.
M. C. Vuran and I. F. Akyildiz. Spatial correlation-based collabora-tive medium access control in wireless sensor networks. Networking, IEEE/ACM Transactions on, 14(2):316–329, 2006.
M. W¨alchli and T. Braun. Efficient signal processing and anomaly detection in wireless sensor networks. In Applications of Evolutionary Computing, pages 81–86. Springer, 2009.
C. J. Walter and N. Suri. The customizable fault/error model for dependable distributed systems. Journal of Theoretical Computer Science, 290(2):
1223–1251, 2003.
J. Wang, Y. Liu, M. Li, W. Dong, and Y. He. Qof: Towards comprehensive path quality measurement in wireless sensor networks. InINFOCOM, 2011 Proceedings IEEE, pages 775–783. IEEE, 2011.
R. Y. Wang, D. M. Strong, and L. M. Guarascio. Beyond accuracy: What data quality means to data consumers. J. of Management Information Systems, 12(4):5–33, 1996.
S. Wolfram.The MATHEMATICAR Book, Version 4. Cambridge university press, 1999.
N. Xu. A survey of sensor network applications. IEEE Communications Magazine, 40(8):102–114, 2002.
H. Yan, X.-c. Diao, and K.-q. Li. Research on information quality driven data cleaning framework. In Future Information Technology and Management Engineering, 2008. FITME’08. International Seminar on, pages 537–539.
IEEE, 2008.
BIBLIOGRAPHY 139 S. Zahedi and C. Bisdikian. A framework for qoi-inspired analysis for sen-sor network deployment planning. InProceedings of the 3rd international conference on Wireless internet, page 28. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2007.
S. Zahedi, M. B. Srivastava, and C. Bisdikian. A computational frame-work for quality of information analysis for detection-oriented sensor net-works. In Military Communications Conference, 2008. MILCOM 2008.
IEEE, pages 1–7. IEEE, 2008.
H. Zhang, A. Arora, Y.-r. Choi, and M. G. Gouda. Reliable bursty con-vergecast in wireless sensor networks. Computer Communications, 30(13):
2560–2576, 2007.
J. Zhao, R. Govindan, and D. Estrin. Computing aggregates for monitoring wireless sensor networks. In Sensor Network Protocols and Applications, 2003. Proceedings of the First IEEE. 2003 IEEE International Workshop on, pages 139–148. IEEE, 2003.
Index
conclusion
future directions, 127 lessons learned, 126 thesis contributions, 124
information transport, 124 representing physical
phe-nomenon, 126
sampling and information transport, 125
design requirements, 45 information transport, 45 physical phenomenon, 46
sampling and information trans-port, 46
performance evaluation case study1, 102
information transport, 102 sampling and information
transport, 103
tunable and adaptable co-design, 104
case study2
dynamic system requirements, 110
case study3
evolving phenomenon, 113 experimentation, 115
results, 116 TUDUNet, 115
simulation environment, 100 studies, 100
summary, 120
problem statement, 50
information transport, 52
representing the physical phe-nomenon, 54
mathematical formulation, 55 sampling and information
trans-port, 53
mathematical formulation, 53 representing the physical phenomena
analytical evaluation, 94
binding sampling and transport, 92
co-design analysis, 89
generic holistic co-design, 96 multi-attribute co-design, 93 overview, 86
sampling accuracy, 89 summary, 97
transport reliability, 90 transport timeliness, 91 sampling and transport
analytical evaluation, 80 co-design, 75
function of sampling and trans-port, 75
optimal sampling and transport, 78
overview, 73
sampling and transport algo-rithm, 82
summary, 84 state of the art 141