From Physical to Virtual Wireless Sensor Networks using Cloud Computing

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In the modern world, billions of physical sensors are used for various dedications: Environment Monitoring, Healthcare, Education, Defense, Manufacturing, Smart Home, Agriculture Precision and others. …

In the modern world, billions of physical sensors are used for various dedications: Environment Monitoring, Healthcare, Education, Defense, Manufacturing, Smart Home, Agriculture Precision and others. Nonetheless, they are frequently utilized by their own applications and thereby snubbing the significant possibilities of sharing the resources in order to ensure the availability and performance of physical sensors. This paper assumes that the immense power of the Cloud can only be fully exploited if it is impeccably integrated into our physical lives. The principal merit of this work is a novel architecture where users can share several types of physical sensors easily and consequently many new services can be provided via a virtualized structure that allows allocation of sensor resources to different users and applications under flexible usage scenarios within which users can easily collect, access, process, visualize, archive, share and search large amounts of sensor data from different applications. Moreover, an implementation has been achieved using Arduino-Atmega328 as hardware platform and Eucalyptus/Open Stack with Orchestra-Juju for Private Sensor Cloud. Then this private Cloud has been connected to some famous public clouds such as Amazon EC2, ThingSpeak, SensorCloud and Pachube. The testing was successful at 80%. The recommendation for future work would be to improve the effectiveness of virtual sensors by applying optimization techniques and other methods.

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  • 1. International Journal of Research in Computer Science eISSN 2249-8265 Volume 3 Issue 1 (2013) pp. 19-25 www.ijorcs.org, A Unit of White Globe Publications doi: 10.7815/ijorcs. 31.2013.057 FROM PHYSICAL TO VIRTUAL WIRELESS SENSOR NETWORKS USING CLOUD COMPUTING Maki Matandiko Rutakemwa PhD Student, Computer Science, Christ University Bangalore, INDIA Email: makinsoft@gmail.com, makimatandiko.rutakemwa@christuniversity.inAbstract: In the modern world, billions of physical But the two hallmark features of sensor networks,sensors are used for various dedications: Environment namely customized network applications and theMonitoring, Healthcare, Education, Defense, collaborative in-network processing, are notManufacturing, Smart Home, Agriculture Precision achievable beyond the boundary of the usersand others. Nonetheless, they are frequently utilized by administrative domains (except for limited scope datatheir own applications and thereby snubbing the sharing through Internet gateways).significant possibilities of sharing the resources in We argue that even quite mature networking andorder to ensure the availability and performance of duty cycling protocols for sensor networks exists, butphysical sensors. This paper assumes that the immense approaches to wireless sensor network sharing andpower of the Cloud can only be fully exploited if it is management are still immature.impeccably integrated into our physical lives. Theprincipal merit of this work is a novel architecture In particular, typical wireless sensor networks arewhere users can share several types of physical designed and deployed to serve a single application.sensors easily and consequently many new services Indeed, the common approach in the design of sensorcan be provided via a virtualized structure that allows networks is to deploy networks that are fit-for-purposeallocation of sensor resources to different users and with the primary aim of supporting a single applicationapplications under flexible usage scenarios within that belongs to a single authority (usually the owner ofwhich users can easily collect, access, process, the infrastructure)[13]. While this is a sensiblevisualize, archive, share and search large amounts of approach for short-term and small-scale deployments,sensor data from different applications. Moreover, an in sensor network deployments that consist ofimplementation has been achieved using Arduino- thousands of nodes with a life span of multiple years,Atmega328 as hardware platform and inducing high costs of deployment and maintenance,Eucalyptus/Open Stack with Orchestra-Juju for the single application approach can lead to inefficientPrivate Sensor Cloud. Then this private Cloud has use of resources and low cost-benefit results.been connected to some famous public clouds such as Moreover, the requirement for dedicated sensingAmazon EC2, ThingSpeak, SensorCloud and Pachube. infrastructure to support new applications belonging toThe testing was successful at 80%. The different organizations can lead to unnecessaryrecommendation for future work would be to improve replication of sensing infrastructure. One example thatthe effectiveness of virtual sensors by applying illustrates this problem is the deployment ofoptimization techniques and other methods. temperature sensors on a single environment by different authorities (Government, University, SocialKeywords: Virtual Wireless Sensor Networks, Agency, Hospital and others.Ubiquitous Computing, Cloud Computing In this work we propose a departure from the notion I. INTRODUCTION of sensor networks aimed at supporting a single application and serving a single user. We introduce a Wireless sensor networks have been traditionally tactic that is based on the separation of substructuredesigned to be privately owned and used. Therefore, and application ownership.an increasing number of sensor networks have beendeployed to monitor a variety of conditions and The primary objective of this work is to create asituations. At the same time, more and more framework that allows sensor network infrastructuresapplications are starting to rely on the data from sensor to be shared among multiple applications that cannetworks to provide users with (near) real-time potentially belong to different authorities. Byinformation and conditions. This increasing demand of achieving this level of partaking, sensingusers for accurate information about natural and infrastructures can be viewed as an accessible resourcesurrounding phenomena is creating a business case for that can be dynamically re-purposed and re-application providers. www.ijorcs.org
  • 2. 20 Maki Matandiko Rutakemwaprogrammed by different authorities, in order to support for multiple gateways. Most communication issupport multiple applications. done by addressing individual devices. As device addresses are related to the gateway being used, The main task in apprehending this journey is the changing the gateway on the fly is difficult.Users needdesign of a novel wireless sensor network architecture to know the specifications of different kinds ofthat supports multiple applications, dynamically physical sensors.uploaded by different owners and simultaneouslyrunning over a shared infrastructure. OGC (Open Geospatial Consortium) [9] defined Sensor Modeling Language (SensorML) [1] to provide In this work we illustrate our efforts in exploring standard models and an XML encoding for physicalthis vision. The key characteristics of our approach sensors’ description and measurement processes.are: SensorML can represent the metadata for any physicali. A virtualization layer that is running on each sensor sensor (such as the type of physical sensor, the node abstracts the access to sensor resources and location, and the accuracy). We used SensorML to allows the management of these resources through describe the metadata of physical sensors. We have policies expressed by the infrastructure owner. added the mapping between physical sensors andii. A runtime environment on each node that allows virtual sensors to describe how to translate commands multiple applications to run inside each node. coming from users to virtual sensors into commandsiii. A policy based application deployment that enables for the corresponding physical sensors. multiple applications to be deployed over the shared infrastructure. Although there are many kinds of physical sensors, no application uses all of them. Each application needs The following sections offer an overview of the sufficient physical sensors for its requirements (such asarchitecture and a description of our implementation. physical sensors in a certain location). A We have discussed the related work in Section 2. publish/subscribe mechanism [11] is used to selectWe present an overview of our proposed architecture physical sensors in [8]. When there are multiple sensorin Section 3, the details of our implementation are in networks, each sensor network publishes sensor dataSection 4 and conclude while projecting future and metadata that describes the type of physicalresearch opportunities in Section. sensors. Each application subscribes to one or more sensor networks to receive a real-time data stream II. RELATED WORKS from their physical sensors. Such publish/subscribe mechanism allows each application to select only a There have been a few of studies on the particular type of physical sensors it collects data from.virtualization of physical wireless sensor networks. Sensor-Cloud infrastructure makes virtual sensorsNaturally, the design we propose here shares some of from multiple physical sensors. Because every virtualthe design goals that are common to other research sensor is not created from a sensor network, theprojects, while addressing unique problems that are grouping is more flexible. Users can select groups ofcomplementary in nature. virtual sensors or virtual sensors. The goal of the urban participatory sensing project Users should check whether the physical sensorsat the Center for Embedded Networked Sensing are available and detect physical sensors’ faults for(CENS) is to engage sensors built into portable devices keeping the quality of the data coming from physicalsuch as mobile phones and PDAs in sharing their sensors. FIND [13] provided a novel method to detectcapabilities [10]. A tiered architecture and related physical sensors with data faults. FIND ranks theprotocols for people-centric urban sensing is under physical sensors based on their sensing readings asdevelopment as part of the MetroSense project at well as their physical distances from an event. FINDDartmouth, which involves mobile devices in considers a physical sensor(s) faulty if there is aopportunistic interactions with wireless Internet significant mismatch between the sensor data rank andgateways and available static sensornet infrastructure the distance rank. This approach focuses on detecting[5]. physical sensor(s) faults, while we focus on Mires [6] is a publish/subscribe architecture for monitoring the virtual sensors. Because there is aWSNs. Basically sensors only publish readings if the relationship between the status of a virtual sensor anduser has subscribed to the specific sensor reading. the status of its sensors, the virtual sensor will alsoMessages can be aggregated in cluster heads. report incorrect results if the linked physical sensorsSubscriptions are issued from the sink node (typically are faulty. The users of the cloud computing servicedirectly connected to a PC), which then receives all check the status of their virtual servers, not the statuspublications. of the linked physical server. We also focus on TinySIP [7] supports session semantics, monitoring the status of virtual sensors.publish/subscribe, and instant messaging. It offers www.ijorcs.org
  • 3. From Physical to Virtual Wireless Sensor Networks using Cloud Computing 21 III. VIRTUAL WIRELESS SENSOR NETWORK INFRASTRUCTURE Let us now explore how one can temporarily“borrow” several nodes from several separate domains,virtual sensors that extend the area of coverage beyondsome physical boundary. Figure 1: Virtual vs Physical SensorsA. Assumptions Standardization: Different kinds of physical sensors have Our motivation is to explore the possibilities of different specifications. Each physical sensor provides itsforming scalable virtual wireless sensor network under own functions for control and data collection. Standard mechanism enables users to access sensors withoutthe following design assumptions: concern for the differences among the physical sensors.−A group of users (private, corporate and/or Standard functions for virtual sensors, are used so that Government) is willing to deploy sensor node the users can access the virtual sensors with the hardware to cover areas under their power. standardized functions. Cloud infrastructure translates the− There exists a level of willingness from all members standard functions for the virtual sensors into specific of the group to share the sensing and processing functions for the different kinds of physical sensors. capabilities of their physical sensors’ infrastructure This virtualization implies the following layers of the as needed, without interruption. architecture :− A typical user belonging to the group often borrows − A virtualization layer that is running on each sensor sensor node capabilities from parts of other sensor network infrastructure to form a virtual sensor node, abstracts access to sensor resources and allows network over a desired area of coverage. the management of these resources through policies expressed by the infrastructure owner.− The rules of sharing are dictated by the owner of the − A runtime environment on each node that allows resource and at no time the sensing task of a local or remote user violates the authority, privacy and multiple applications to run inside each node. security of the provider. − A policy based application deployment that enables− Optionally, a supporting business model accounts multiple application to be deployed over the shared for balancing the cost of sharing resources (such as infrastructure. hardware and battery costs) between private and community use. When users request virtual sensors, the Cloudinfrastructure automatically provisions them from theirtemplates. Users can control their virtual sensorsdirectly or via their Web browsers. Cloudinfrastructure also provides the users with monitoringfunctions for the virtual sensors.B. StepsThe steps to be achieved in the process are:Virtualization: There are various kinds of scatteredphysical sensors. Virtual sensor enables the use ofsensors without worrying about the locations and thespecifications of physical sensors. Fig.2 describes therelationships between virtual sensors and physical Figure 2: Application – Architecturesensors. Each virtual sensor is created from one ormore physical sensors. Users can create virtual sensorsand freely use them as if they owned sensors. Forexample, they can activate or inactivate their virtualsensors, check their status, and set the frequency ofdata collection from them. If multiple users freelycontrol the physical sensors, some inconsistentcommands may be issued. The users can freely controltheir own virtual sensors by virtualizing the physicalsensors as virtual sensors. Figure 3: Mapping Physical - Virtual Sensors www.ijorcs.org
  • 4. 22 Maki Matandiko RutakemwaC. Major ActorsSensor Owner: A sensor owner is an actor who ownshis physical sensors. A sensor owner allows others touse those physical sensors through Cloudinfrastructure. A sensor owner registers the physicalsensors with their properties to the Cloud. The ownerdeletes the registration of them when s/he quits sharingthem.Cloud Administrator: is the actor who manages theCloud Infrastructure service. The administrator Figure 5: Arduino Uno Atmega 328, Arduino Ethernetmanages the IT resources for the virtual sensors, Shield, LDR Sensor, LM35 Sensormonitoring, and the user interfaces. Figure 6: Arduino Uno, Arduino Ethernet Shield and sensors connected to the System running Ubuntu 11.10 Figure 4: Federation of Clouds - Cloud AdministratorEnd User: An end user is an actor with one or moreapplications or services that use the sensor data. Anend user requests the use of virtual sensor that satisfiesthe requirements from the templates. The user cancontrol her/his virtual sensors directly or via a Webbrowser. The user can monitor the status of the virtualsensors. When they become unnecessary, the user canrelease them. The end users can use the virtual sensorsby paying for usage and with no detailed knowledgeabout the physical sensors.IV. IMPLEMENTATION AND DEVELOPMENT Figure 7: Ubuntu 11.10 with Eucalyptus connected to the hardware for Private Cloud with virtual sensors− Using Arduino Uno Atmega 328 and Arduino Shield along with an LDR sensor and a LM35 sensor, a prototype of the architecture has been implemented and tested as shown in Figure 6.− Firstly Ubuntu 11.10 has been installed on 2 systems to constitute a private cloud. To this cloud the two physical sensors have been connected using the Arduino Shield, Figure 7.− Next, the private cloud based mainly on Eucalyptus structure was connected to several public clouds such as Pachube, ThingSpeak, Amazon EC2, SensorCloud and Github shown in Figure 8. Figure 8: Connection to Pachube from Private Cloud www.ijorcs.org
  • 5. From Physical to Virtual Wireless Sensor Networks using Cloud Computing 23 Figure 9: Temperature and Light Graphs on Pachube after sending readings from physical sensors Figure 10: Connection to ThingSpeak Public Cloud with LDR readings graph generation Figure 11: Connection to SensorCloud Public Cloud www.ijorcs.org
  • 6. 24 Maki Matandiko Rutakemwa Figure 12: Temperature readings sent to ThingSpeak Public Cloud V. CONCLUSION [2] J. Hill, R. Szewczyk, A. Woo, S. Hollar, D. Culler, K. Pister, “System architecture directions for networked We present Cloud infrastructure which virtualizes sensors,” International Conference on Architectural physical sensors so that end users can share them with Support for Programming Languages and Operating no concerns about the details of them (i.e. location and Systems, 2000. doi: 10.1145/356989.356998 specification). This infrastructure enables end users to [3] J. Koo, R. K. Panta, S. Bagchi, L. Montestruque, “A create virtual sensors dynamically by selecting the Tale of Two Synchronizing Clocks,” The 7th ACM templates of virtual sensors with IT resources. Conference on Embedded Networked Sensor Systems (SenSys 2009). doi: 10.1145/1644038. 1644062 Within this novel architecture users can share [4] J. Scott Miller, Peter A. Dinda, Robert P. Dick, several types of physical sensors easily and “Evaluating A BASIC Approach To Sensor Network consequently many new services can be provided via a Node Programming,” The 7th ACM Conference on virtualized structure which allows allocation of sensor Embedded Networked Sensor Systems (SenSys 2009). resources to different users and applications under doi: 10.1145/1644038. 1644054 flexible usage scenarios within which users can easily [5] J. Shneidman, P. Pietzuch, J. Ledlie, M. Roussopoulos, collect, access, process, visualize, archive, share and M. Seltzer, M. Welsh, "Hourglass: An Infrastructure for search large amounts of sensor data from different Connecting Sensor Networks and Applications," applications. Harvard Technical Report TR-21-04, 2004. Moreover, an implementation has been achieved [6] Kevin Klues, Chieh-Jan Mike Liang, Jeongyeup Paek, using Arduino-Atmega328 as hardware platform and Razvan Musaloiu-E, Philip Levis, Andreas Terzis, Eucalyptus/Open Stack with Orchestra-Juju for Private Ramesh Govindan, “TOSThreads: Thread-Safe and Sensor Cloud. Then this private Cloud has been Non-Invasive Preemption in TinyOS,” The 7th ACM Conference on Embedded Networked Sensor Systems connected to some famous public clouds such as (SenSys 2009). doi: 10.1145/1644038.1644052 Amazon EC2, ThingSpeak, SensorCloud and Pachube. The testing was successful at 80%. [7] Keiji Matsumoto, Ryo Katsuma, Naoki Shibata, Keiichi Yasumoto, Minoru Ito, “Extended Abstract: The recommendation for future work would be to Minimizing Localization Cost with Mobile Anchor in improve the effectiveness of virtual sensors by Underwater Sensor Networks,” The Fourth ACM applying optimization techniques and other methods. International Workshop on UnderWater Networks (WUWNet), 2009. doi: 10.1145/1654130.1654144 VI. REFERENCES [8] M. Gaynor, M. Welsh, S. Moulton, A. Rowan, E. LaCombe, and J. Wynne, “Integrating Wireless Sensor [1] C. Lenzen, P. Sommer, R. Wattenhofer, “Optimal Networks with the Grid,” IEEE Internet Computing, Clock Synchronization in Networks,” The 7th ACM Special Issue on Wireless Grids, 2004. doi: Conference on Embedded Networked Sensor Systems 10.1109/MIC.2004.18 (SenSys 2009), 2009. doi: 10.1145/1644038.1644061 www.ijorcs.org
  • 7. From Physical to Virtual Wireless Sensor Networks using Cloud Computing 25[9] Open Geospatial Consortium. and K. Whitehouse, “Macrodebugging: Global Views http://www.opengeospatial.org/ of Distributed Program Execution,” The 7th ACM[10] Shuo Guo, Ziguo Zhong, Tian He, “FIND: Faulty Node Conference on Embedded Networked Sensor Systems Detection for Wireless Sensor Networks,” Proc. The 7th (SenSys 2009), 2009. ACM Conference on Embedded Networked Sensor [13] The Jython Project. http://www.jython.org/ Systems (SenSys 2009), pp. 253-266. doi: 10.1145 [14] Ziguo Zhong, Tian He, “Achieving Range-Free /1644038.1644064 Localization Beyond Connectivity,” The 7th ACM[11] SensorML. http://vast.uah.edu/SensorML/ Conference on Embedded Networked Sensor Systems[12] T. I. Sookoor, T. W. Hnat, P. Hooimeijer, W. Weimer (SenSys 2009). doi: 10.1145/1644038.1644066 How to cite Maki Matandiko Rutakemwa, "From Physical to Virtual Wireless Sensor Networks using Cloud Computing". International Journal of Research in Computer Science, 3 (1): pp. 19-25, January 2013. doi: 10.7815/ijorcs. 31.2013.057 www.ijorcs.org