- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
International Journal of Distributed Sensor Networks
Volume 2013 (2013), Article ID 694829, 13 pages
Integrated Validation System for the Simulation of Diverse Sensors in WSNs
Department of Multimedia Engineering, Dongguk University, 30 Pildongro 1 Gil, Jung-Gu, Seoul 100-715, Republic of Korea
Received 8 June 2013; Accepted 8 July 2013
Academic Editor: Jong Hyuk Park
Copyright © 2013 Hyun-Woo Kim and Young-Sik Jeong. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
In wireless sensor networks (WSNs), sensors use different types of information, such as temperatures, humidity, magnetic fields, and sound; their communication distances and battery sizes and capacities may be different. Differences in communication distances and battery capacities when the same communication method is used affect the life of the entire sensor network topology. Tests for the maximum life of the sensor network topology require many sensor nodes and huge proportional costs. In addition, experiments to verify new ideas for WSNs as alternatives to existing methods also require large expenditures. Since experiments with the arrangement of actual sensors are expensive, such experiments are conducted using simulators so that solutions for protocol design and verification can be compared through software. Existing simulators are tailored to certain sensor characteristics, so that only quite limited results can be obtained through the implementation of simulated operations, simulations of sensor arrangements, and the comparison of limited information transmission routing functions provided by simulators. This paper proposes diverse sensor definable simulators (DSDs) for the performance of experiments on diverse sensors with different communication distances in WSN environments where Geography Markup Language (GML) based coordinates are utilized.
Wireless sensor networks (WSNs) have been actively studied by many researchers thus far and are grafted on actual life areas (e.g., surveillance area, living room with sensors etc.). In WSN environments, diverse sensors are generally used depending on the types of data to be collected from sensing target regions, including temperature, humidity, magnetic fields, and sound. Events are sensed using these sensors and the data are transmitted to sink nodes through sensor networks. The transmitted information is applied to engines that have different roles in many application services. These include national safety, medical devices, senior citizens who live alone, traffic control and safety, process control, energy savings, weapon systems, distributed robots, manufacturing, and communication systems. To build up these WSN environments, many sensors and appropriate protocols for communication between them are necessary. Since protocols for communication between sensors are used differently according to their diverse types, it is not easy to set up optimum environments. Arranging sensors in target regions to test them for verification of theoretical contents is expensive. Sensor node arrangements require the use of many sensor nodes, along with arrangement algorithms. Finding and correcting errors in these sensor nodes require huge amounts of time and effort, and inspecting individual sensor nodes is practically impossible [1–4].
Therefore, to build up WSN environments, software simulators that enable prior testing, verification, and supplementation of theoretically designed contents are necessary. Thus far many simulator tools for the design and verification of WSNs have been developed, including GloMoSim , SNetSim , ATEMU , QualNet , NS2 , EmStar , TOSSIM , J-Sim , AVRORA , SWANS , SENSE , MSPsim , WSim , Atarraya , WSNet , AlgoSenSim , NetTopo , and SIDnet-SWANS . However, although such simulators have been developed, tests are conducted using limited sensor information, so that only very restricted results are obtained, or large- or small-scale networks are insufficiently treated. In addition, since simulations are basically conducted with those types of sensors that are built in simulators, these simulations are not suitable of experiments on other sensors, new sensors, or combinations of sensors [1, 2, 23, 24].
Therefore, this paper proposes a diverse sensor definable simulators (DSDs) for performance experiments on diverse sensors with different communication distances not only in experimental environments but also in WSN environments where geography-markup-language- (GML-) based coordinates are utilized. The DSDs use GMLs that can be mapped with actual topography so that the simulations of target topography can be provided in more detail. This can create dynamic and diverse static arrangements of mobile sensor nodes (MSNs) and fixed sensor nodes (FSNs). In addition, this paper also proposes a simulator that provides not only basic sensor-setting methods but also functions for users to define sensor types so that more concrete experiments can be conducted. It provides functions for users to define additional sensor node types other than MSNs and FSNs, so that not only new sensor types but also existing diverse sensors can be universally simulated.
The remainder of the paper is composed as follows. In Section 2, simulators that have been developed as tools for the design and verification of existing WSNs are introduced in detail. In Section 3, the specification of the definition of sensor types in the DSDs proposed in the present paper is explained. In Sections 4 and 5, contents regarding the design and implementation of the DSDs are described.
2. Related Works
As shown in Table 1, the tools operate simulations of certain sensors or provide limited results due to restrictions in their functions. Therefore, in the present paper, a framework will be provided in which diverse types of sensors, such as temperature, humidity, magnetic field, and sound sensors, can be used to simulate various performances, including the sensor arrangements necessary for WSNs.
3. Definition of Sensor Type
The DSDs proposed in the present paper provide a function to define basic sensor types necessary for the simulations of the functions and performance of WSNs using diverse sensor nodes. This definition of sensor types enables users to select the basic sensor node information necessary for simulator operation and colors for visualization.
First, information on sensors necessary for the definition of sensor types is given as follows. (1)Each sensor’s identifier should be unique. (2)The definition of the types of sensor functions refers to temperature sensing, illumination sensing, magnetic field sensing, sound sensing capabilities, and so on, and the sensing ranges of sensors by type can be inputted in detail. (3)As sensors’ operation periods, the periods of basic sensors’ active states are entered.(4)As sensors’ operating time, the time for which the active mode of sensors should be maintained for operation should be entered. (5)The sensor sleeping time refers to the sleeping time when sensors are in the resting state. (6)The range of sensor communication is the range of communication for information transmission between sensors, and the values of the maximum range of communication and the basic range of communication are required. (7)Sensors’ battery sizes refer to the basic battery sizes of sensors, which indicate their capacities. (8)The definitions of the amounts of battery consumption by sensor function should be entered. In the case of mobile sensors, the rate of battery consumption during movements should be given (for instance, if 50 mA is consumed per 1 m, the migration length (1 m) and the amount of consumption (50 mA) should be entered). When sensors are in operation in the active mode, the operation time and the amount of consumption are required. When sensors are in operation in the sleep mode, the operation time and the amount of consumption are required. (9)The details of the residual battery capacity should be entered. As methods for obtaining the residual battery capacity, two methods, one for the user to prepare the formula and another to obtain the residual battery capacity relying on the DSDs, are provided. The formula is to be entered by the user with the sensor information entered earlier (the formula can be generated for the four fundamental arithmetic operations and parentheses can be used based on the priorities of operations). In the case of the method relying on DSDs, the residual battery capacity after deducting the amounts of battery consumption applying the rates of battery consumption for the operations of individual functions of independently operating sensors is calculated. (10)The information necessary for the prediction of battery life should be entered. When the user has entered a formula into the details for the residual battery capacity, information is provided so that the user can predict the life based on the entered formula considering the residual battery capacity. In the case of the method relying on DSDs, the expected life time is calculated for the residual battery capacity after deducting the amounts of battery consumption, applying the rates of battery consumption for the operations of independently operating sensors. In this case, the rates of battery consumption are rates accumulated in proportion to operating time in accordance with operations by function.
The three pieces of information to be added on the definition of sensor types for the visualization functions of DSDs simulators are as follows. (1) Information for sensor type division should be entered. If sensor division by type has not been set, unique colors will be randomly selected and applied. If the user wishes to select the colors, a list of colors not overlapping with each other will be shown so that the user can choose. When images are selected, it must be ensured that the same image is not used. (2) Values for color setting for sensor communication should be entered. Some colors are provided in addition to the colors basically applied so that the user can revise the colors. The colors can be easily observed using the visual part. (3) Values for sensors’ sensing color setting should be set. The colors applied to the range of sensing should be selected so that they do not overlap with the colors for previous sensors using the color table.
Basic information on sensors necessary for conducting simulations is also essential. Through this function, information on existing sensors can be entered and experimented and changes can be made to existing sensors to verify the efficiency of topology in relation to sensor functions. Trial simulations can be conducted by entering new sensor information, and added functions for visualized expression can be selectively provided so that the user can quickly understand diverse experiments and their results. In addition, the defined types can be stored as XML documents so that the information can be reused and applied in many fields, by not only the user but also by other users in DSDs-based systems. Figure 1 shows an XML schema configured for sensor type definition.
4. Design of DSDs
The DSDs proposed in the present study are largely divided by function into a User Interface, a Target Area Manager, an Interaction Broker, a Map Manager, a Map Controller, a Node Manager, a Coordinate Converter, and a Viewer. The User Interface sets the user’s definition of sensor node types, revisions and deletions of the defined types, and screen configuration information. The Target Area Manager manages sensing target regions set by the user and the Interaction Broker connects the node- and map-setting values set by the user to the system. The Map Controller applies the map-setting values entered by the user and the Node Manager manages the sensor types defined by the user in addition to the sensor types basically provided for independent operations. The Coordinate Converter processes data in order to provide the simulated states of operation to the Viewer. The Viewer finally provides simulations visually. Figure 2 gives a structural diagram of all of the functions of the DSDs.
The User Interface Component is subdivided into a Map Interface, a Node Type Manager, and a View Mode. The Map Interface receives the entries of actual topography and mappable GML. The Node Type Manager consists of a Node Importer for adding node types defined by other DSDs in advance, a Node Exporter for storing the nodes types defined by the simulator currently in operation as XML documents, a Node Parser for adding read documents to the Node Manager, and Mobile Information and Fixed Information for receiving the entries of type addition, revision, and deletion for MSNs and FSNs. The View Mode is an interface for providing the screens operated by the DSDs so that they can be selected by the user; it is composed of Integration to operate MSN, FSN, or MSN and FSN simultaneously.
The Target Area Manager Component sets areas in target regions for which the GML documents read and analyze through the GML Importer should be observed for more detailed experiments. The Interaction Broker Component plays the role of a broker to send the set values entered by the user in the User Interface to the Map Controller and the Node Manager. The Map Manager Component plays the role of applying GML documents mappable with actual topography to the DSDs and managing the documents. It is composed of a GML Importer that adds the GML documents selected through the Map Interface of the User Interface to the simulator, a GML Parser that analyzes the added GML documents for application to the DSDs, a Map Layer that creates Map Objects for obstacles in the analyzed GML topography data and sends them to the Layer Manager, and a Layer Manager that manages the topographical information received from the Map Layer.
The Controller Component serves functions such as enlarging, reducing, area enlarging, and moving for the maps of the set values received through the Interaction Broker and managed by the Layer Manager. These functions are implemented when values have been entered through the Map Interface of the User Interface and the results are outputted on the Viewer through the Coordinate Converter. The Node Manager Component manages the sensor nodes types entered and defined by the user. It is subdivided into MSNs, which are mobile sensors, and FSNs, which are sensors at fixed locations. MSNs and FSNs are composed of Sensor Identifiers (S_IDs) that show the unique identifications of sensor nodes, Function Types (F_TYPEs) that define sensors’ sensing ranges and sensing types, Active Interval Time (A_IT) that shows the operating periods of sensors, Active Operator Time (A_OT) that shows operating times when sensors are in the active mode, Sleep Operator Time (S_OT) that shows operating times when sensors are in the sleep mode, Communication Max Ranges (CMRs) that show maximum communication ranges between sensors, and Communication Default Range (CDR) that shows the ranges of basic communication between sensors. MSNs are composed of Move Consumption (MC) that shows the amount of battery consumption during movements; Active Consumption (AC) that shows the amount of battery consumption when sensors are in the active mode; Sleep Consumption (SC) that shows the amount of battery consumption when sensors are in the sleep mode; the Formula of Battery Remaining (FBR) that shows a formula for obtaining the residual battery capacity of diverse sensors such as temperature, humidity, magnetic field, and sound sensors; and the Life Prediction of Battery (LPB) that predicts and shows lifecycles from the viewpoint of batteries in proportion to the residual battery capacity and operating time, which is calculated through the FBR when sensor nodes operate independently in cases where simulations are conducted.
The Coordinate Converter Component plays the role of processing and transmitting basic information on topography and nodes and data on their operating situations so that the information data can be displayed on the Viewer. The Viewer Component provides the data received through the Coordinate Converter to the user as visual information. The Viewer Component is composed of a Map Viewer for showing topography, an MSN Viewer that shows the situation of operation of MSNs and Mobile Sensor information, an FSN Viewer that shows the situation of operation of FSNs and Fixed Sensor information, and an Integration Viewer that shows MSNs and FSNs together so that they can be compared and analyzed.
5. Implementation of DSDs
The DSDs are composed of MSN Views, FSN Views, Integration Views, and Type List Views. Figure 3 shows a screen of MSN and FSN controlled by the DSDs. The control screen is set to hidden as a default option. The hidden state can be released through a right mouse click on the View to control sensor nodes for MSNs and FSNs. MSN Control enables the selection of sensing ranges, communication ranges, the number of sensor nodes to be operated, the range to be displayed on the screen, and the state of connection. Whereas the number of sensor nodes should be entered in the case of MSN Control, sensor node locations can be directly set on the screen in the case of FSN Control. FSN Control basically provides the same functions as those provided by the MSN Control.
Figure 4 shows a screen of MSN operation that illustrates MSN movements.
Figure 5 shows a screen after selecting the FSN on the tab at the top to operate FSNs. For FSNs, the user sets the locations of sensor nodes individually. The CR in Figure 5 refers to the Communication Range between sensors and the number indicates the set range value. It can be seen that the view of communication between sensors varies with the value of the CR and that communication is possible only when the value of the CR is at least 30.
Figure 6 shows the results of implementation of the Integration function in order to compare MSNs and FSNs simultaneously. The left side of Figure 6 shows an MSN operation screen and the right side shows FSNs for which the user sets sensor locations. The screen shows that the MSN has set the number of sensor nodes to 100 and gradually covers the target topography. When the MSN has finally completed its coverage, the number of sensor nodes falling short in terms of covering the entire target topography can be inferred.
Figure 7 shows an execution screen where the user sets the definition of sensor types. The TYPE to select between MSN and FSN as sensor information, unique numbers of sensors, sensors’ sensing functions, the Active Interval Time to enter the period of switching between the active and sleep modes, the Active Mode Operation Time and Sleep Mode Operation Time indicating the active and sleep mode operating times, the Communication Max Range indicating the maximum communication range between sensors, the Communication Default Range indicating the default communication range between sensors, basic sensor battery sizes, the rate of battery consumption during movements in the case of Mobile Sensors, the rate of battery consumption during the active state, and the rate of battery consumption during sleep states should be essentially entered while the residual battery capacity formula can be selectively entered by the user.
6. Conclusion and Future Research
The diverse sensor definable simulators (DSDs) proposed in the present paper are intended to provide frameworks in which various performances, including sensor arrangements necessary for WSNs, can be simulated using diverse types of sensors such as temperature, humidity, magnetic field, and sound sensors. Basically, functions to define the sensor types were provided so that the functions could be implemented. Since existing simulators operate simulations for the same types of sensor nodes or conduct simulations using only the limited types and functions of sensors defined in the simulator, restricted results are necessarily obtained. The DSDs were able simulate the target topography consisting of various types of sensors through experiments of diverse sensors. Furthermore, they could conduct additional experiments of new sensors to be made later. In addition, defined sensors were stored as XML documents so that they can be used in various application programs and the sensor types defined by the user can be reused.
In future work, precise logs will be set in the DSDs and log visualization will be provided. By setting precise logs, users will be able to select the activity types of individual sensors, and log visualization functions for the selected activities will be provided so that users can make the best selection during different simulation implementation processes.
This research supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2013-H0301-13-4007) supervised by the NIPA (National IT Industry Promotion Agency).
- B. Musznicki and P. Zwierzykowski, “Survey of simulators for wireless sensor networks,” International Journal of Grid and Distributed Computing, vol. 5, no. 3, pp. 23–50, 2012.
- J. Chen, M. B. Salim, and M. Matsumoto, “A single mobile target tracking in Voronoi-based clustered wireless sensor network,” Journal of Information Processing Systems, vol. 6, no. 4, pp. 17–28, 2010.
- A. U. Bandaranayake, V. Pandit, and D. P. Agrawal, “Indoor link quality comparison of IEEE 802. 11a channels in a multi-radio mesh network testbed,” Journal of Information Processing Systems, vol. 8, no. 1, pp. 1–20, 2012.
- Y. Jeong, Y. Han, J. J. Park, and S. Lee, “MSNS: mobile sensor network simulator for area coverage and obstacle avoidance based on GML,” EURASIP Journal on Wireless Communications and Networking, vol. 95, no. 1, pp. 1–15, 2012.
- GloMoSim, http://pcl.cs.ucla.edu/projects/glomosim.
- SNetSim, http://www.softpedia.com/get/Science-CAD/SNetSim.shtml.
- J. Polley, D. Blazakis, J. McGee, D. Rusk, J. S. Baras, and M. Karir, “ATEMU: a fine-grained sensor network simulator,” in Proceedings of the 2004 1st Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, pp. 145–152, October 2004.
- Qualnet, http://web.scalable-networks.com/content/qualnet.
- The Network Simulator—ns-2, http://www.isi.edu/nsnam/ns/.
- L. Girod, J. Elson, A. Cerpa, T. Stathopoulos, N. R. Ramanathan, and D. Estrin, “Emstar: a software environment for developing and deploying wireless sensor networks,” in Proceedings of the 2004 USENIX Annual Technical Conference, Boston, Mass, USA, August 2004.
- P. Levis, N. Lee, M. Welsh, and D. Culler, “TOSSIM: accurate and scalable simulation of entire TinyOS applications,” in Proceedings of the 1st ACM Conference on Embedded Networked Sensor Systems (SenSys '03), pp. 126–137, November 2003.
- “J-Sim: a simulation and emulation environment for wireless sensor networks,” http://icserv.kjist.ac.kr/mis/publications/data/2006/01678171.pdf.
- B. L. Titzer, D. K. Lee, and J. Palsberg, “Avrora: scalable sensor network simulation with precise timing,” in Proceedings of the 4th International Symposium on Information Processing in Sensor Networks (IPSN '05), pp. 477–482, Los Angeles, Calif, USA, April 2005.
- Java in Simulation Time/Scalable Wireless Ad hoc Network Simulator, http://jist.ece.cornell.edu/.
- G. Chen, J. Branch, M. J. Pflug, L. Zhu, and B. K. Szymanski, “SENSE: a wireless sensor network simulator,” http://www.ita.cs.rpi.edu/publications/sense-book-chapter.pdf.
- J. Eriksson, A. Dunkels, N. Finne, F. Österlind, and T. Voigt, “Mspsim—an extensible simulator for msp430-equipped sensor boards,” in Proceedings of the European Conference on Wireless Sensor Networks, Poster/Demo session (EWSN '07), pp. 29–31, Delft, The Netherlands, January 2007.
- WSim, http://wsim.gforge.inria.fr/tutorials/wasp/distributed.html.
- P. Wightman and M. A. Labrador, “Atarraya: a simulation tool to teach and research topology control algorithms for wireless sensor networks,” in Proceedings of the 2nd International Conference on Simulation Tools and Techniques (SIMUTools '09), pp. 2–6, Rome, Italy, March 2009.
- WSNet Overview, http://wsnet.gforge.inria.fr/overview.html.
- J. Fontignie and A. Marculescu, “AlgoSenSim: developer’s guide,” 2006.
- L. Shu, C. Wu, and M. Hauswirth, “NetTopo: beyond simulator and visualizor for wireless sensor networks,” Tech. Rep., Digital Enterprise Research Institute (DERI), Galway, Ireland, 2008.
- O. C. Ghica, “SIDnet-SWANS manual,” March 2010, http://users.eecs.northwestern.edu/~ocg474/SIDnet/SIDnet-SWANS%20manual.pdf.
- S. Silas, K. Ezra, and E. B. Rajsingh, “A novel fault tolerant service selection framework for pervasive computing,” Human-Centric Computing and Information Sciences, vol. 2, no. 5, pp. 1–14, 2012.
- X. Zhou, Y. Ge, X. Chen, Y. Jing, and W. Sun, “A distributed cache based reliable service execution and recovery approach in MANETs,” Journal of Convergence, vol. 3, no. 1, pp. 5–12, 2012.