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Volume 2, Issue 5, May 2014
Integration of Wireless Sensor Networks and Cloud Computing Swathi B S1, Dr. H S Guruprasad2 1
PG Scholar, Dept. of ISE, BMSCE, Bangalore Professor and Head, Dept. of CSE, BMSCE, Bangalore
2
Abstract Cloud environments are mainly used for storage and processing of data. Cloud computing provides applications, platforms and infrastructure over the internet. Wireless Sensor Network is an very important technology in which sensor are placed in distributed manner to monitor physical and environment changes such as temperature, pressure etc. Combining these two technology helps in easy management of remotely connected sensor nodes and the data generated by these sensor nodes. For security and easy access of data, cloud computing is widely used in distributed/mobile computing environment. This paper discusses the integration of Wireless Sensor Network and Cloud computing with their applications, challenges and solutions.
Keywords: Cloud Computing, Wireless Sensor Network.
1. INTRODUCTION Cloud computing is becoming popular day by day in distributed computing environment. Cloud environments are used for storage and processing of data. Cloud computing provides applications, platforms and infrastructure over the internet. Cloud computing is a model for enabling convenient, on demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. The following models are presented by considering the deployment scenario: 1) Private Cloud: This cloud infrastructure is operated within a single organization, and managed by the organization or a third party irrespective of its location. 2) Public Cloud: Public clouds are owned and operated by third parties. 3) Community Cloud: This cloud infrastructure is constructed by number of organization jointly by making a common policy for sharing resources. 4) Hybrid Cloud: The combination of public and private cloud is known as hybrid cloud. Wireless Sensor Networks have been seen as one of the most emerging technology, where distributed connected sensor nodes automatically form a network for data communication. A sensor network is a group of specialized transducers with a communications infrastructure intended to monitor and record conditions at diverse locations. Commonly monitored parameters are temperature, humidity, pressure, wind direction and speed, illumination intensity, vibration intensity, sound intensity, power-line voltage, chemical concentrations, pollutant levels and vital body functions.
2. LITERATURE SURVEY Rajesh et. al. [1]presents Secured Wireless Sensor Network-integrated Cloud Computing architecture. The real-time sensor data must be processed and the action must be taken regularly. The integration controller module of the proposed architecture integrates the sensor network and Internet using Cloud Technology which offers the benefit of reliability, availability and extensibility. Peter et. al. [5] discusses the idea of combining wireless sensor networks and cloud computing from the existing approach. The paper proposes wireless sensor network by virtual sensor in the cloud. The idea is to store the data on both the real sensor and virtual sensor. The paper proposes an architecture to realize distributed shared memory in WSNs with the help of a middleware called tiny DMS.Tongrang Fan et. al. [8] proposes sensor data storage solutions based on the Hadoop cloud computing framework. Due to the rapid growth of sensor data storage and processing, traditional storage systems are not able to meet the data access requirements. By contrast to the private cloud system storage and traditional storage model, the characteristics and advantages of private cloud system storage are analysed. The designed storage solutions were performed by MapReduce programming model, and the experimental results indicated that the new cloud storage solution had higher data access performance.Geoffrey et. al. [10] discusses the characteristics of distributed cloud computing infrastructure for collaboration sensor-centric applications on the Future Grid. The paper mainly focuses on the performance, scalability and reliability at the network level using standard network performance tools. Sajjade t. al. [11] proposes a new framework for Wireless Sensor Network integration with Cloud Computing model with a possibility of an existing Wireless Sensor Network getting connected to the proposed framework. The integration controller unit of the proposed framework integrates the sensor network and cloud computing technology which offers
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IPASJ International Journal of Computer Science (IIJCS) A Publisher for Research Motivation ........
Volume 2, Issue 5, May 2014
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[email protected] ISSN 2321-5992
reliability, availability and extensibility. Peng Zhang et. al. [12] discusses some of the Wireless Sensor Network challenges like the limited resources of a sensor, limited battery life, limited bandwidth and limited processing power. The paper proposes a novel architecture based on cloud computing for wireless sensor network, which can improve the performance of wireless sensor network. Based on this architecture, a cloud acts as a virtual sink with many sink points that collect sensing data from sensors. Each sink point is responsible for collecting data from the sensors within a zone. Sensing data are stored and processed in distributed manner in cloud.Junya Terazono et. al. [13] describes the construction of a sensor network using a messaging network. A messaging network is an overlay network with a set of content-aware message handling capabilities. The use of a messaging network can reduce the complexity and maintenance burden of integrated sensor information systems. Message mediation (a function of the messaging network) enables interoperation of various applications and integration of diverse sensor data. Subscription (another concept in the messaging network) enables proper information delivery of the sensor data to each user.Khandakar et. al. [18] discusses novel and attractive solutions for information gathering across transportation, business, health-care, industrial automation, and environmental monitoring. The next generation of WSN will benefit when sensor data is added to blogs, virtual communities, and social network applications. The proposed architecture is based on cloud computing for wireless sensor network, which can improve the performance of wireless sensor network. Based on this architecture, a cloud acts as a virtual sink with many sink points that collect sensing data from sensors. Each sink point is responsible for collecting data from the sensors within a zone. Sensing data are stored and processed in distributed manner in cloud. Mohamed Firdhous et. al. [19] discusses the Trust models proposed for various distributed system and they have been summarized. The trust management systems proposed for cloud computing have been investigated with their capability, applicability in practical heterogonous cloud environment and feasibility of implementation. Wei Wang et. al. [21] explains strengthening of storage and processing for Wireless Sensor Networks with the help of cloud. The limitation associated with WSNs is the limited storage and processing resources and this could be overcome by the concept of cloud. Cloud computing can be used at the backend for WSNs for storage and processing. This paper proposes an architecture which has lightweight components and Dynamic proxy-based approach to integrate sensor and cloud. Rajesh et. al. [22] proposes an integration module to integrate be sensor information with cloud. The integration controller and Sensor node will communicate through Service oriented architecture. The cloud will provide functionality to deploy the service as well as store the data, makes data always available to users and it also offers new application server as well as new data base. This integration module supports sensor networking for industrial application need for integration of sensor networks with the internet. For sensor network deployed to record the industrial process parameter in various place of industry, it is important that collected information be delivered as fast as possible with minimum delays. Wen-Yaw Chung et. al. [2] discuss about the agricultural system which use the wireless sensors to monitor temperature, humidity, pH value etc. The purpose is to provide a faster and more convenient platform for the client to obtain information from the sensor nodes that has been set-up in an agricultural system. At the host end, Wireless Sensor Nodes will collect the values of various parameters from the front-end sensors host. At the client sides, internet is used to send request for Web Services that will store this big data into distributed SQL databases which are already in proposed cloud system. The benefits of this system include basic computing hardware and reasonable storage capacities making it suitable for any smart device which can monitor real-time farmland information anywhere. The customers can fully access the cloud service using devices that have internet capabilities.Rajeev Piyareet. al. [3] discusses the Wireless Sensor Network applications in important areas such as healthcare, military, critical infrastructure monitoring, environment monitoring, and manufacturing. Due to some of the limitations of WSNs in terms of memory, energy, computation, communication, and scalability, efficient management of the large number of WSNs data in these areas is an important issue. Cloud computing provides huge computing, storage, and software services in a scalable and virtualized manner at low cost. This paper proposes an architecture for integrating Wireless Sensor Networks with the Cloud. REST based Web service is used to monitoring e-health care service and smart environment. Data can be accessed from anywhere due to using of IP in REST based Web service.Srimathi et. al. [6] proposes the idea of combining underwater sensor network and cloud computing. The underwater sensor network with static sensor nodes is used for monitor environment. The underwater sensor cloud architecture provides a way for underwater sensor nodes to collect, store and retrieve environmental data. A Hadoop framework is proposed which serves as a middleware for underwater sensor cloud. The proposed sensor cloud architecture consists of three layers namely Underwater sensor network layer (This layer is the collection of underwater sensor nodes for environmental data analysis), Underwater sensor web layer (This layer consists of a sensor cloud middleware, a meta-modeling tool) and Underwater data computing layer (comprises of collection of data cloud nodes and a cloud interface service to perform sensor data computations).John Tooker et. al. [7] discusses a system which has a novel underground communication system and an
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online underground sensor network test bed. The system includes an underground antenna and underground sensor network test bed. The underground sensor network test bed consists of a network of underground communication systems with soil moisture sensors and a mobile data unit equipped with cellular communication.Wen Qiang Wang et. al. [23] proposes Smart Traffic Cloud, a software infrastructure to enable traffic data, manage, analyse and present the results in a flexible, scalable and secure manner using a Cloud platform. The infrastructure is used to handle distributed and parallel data management and analysis using ontology database and the popular Map-Reduce framework. The paper presents a prototype infrastructure in a commercial Cloud platform and a real-time traffic condition map is developed using data collected from commuter’s mobile phones. Traffic related application such as real time map and on demand travel route is proposed. Both archived and real-time data involved in these applications could be very big, depending on the number of deployed sensors. The cloud infrastructure is used to handle big data and enough computing and storage area. Carlos et. al. [4] proposes a solution for efficient medical data management by using sensors which will be attached to existing medical equipments that are inter-connected to exchange service. The information becomes available in the cloud from where it can be processed by expert systems and/or distributed to medical staff. By adopting this process cost will be less and simple to implement. This service acts like a door where medical staff devices can access all available information. Perumal et. al. [9] presents the study and development of a Wireless Sensor Network-integrated cloud computing for e-healthcare applications. WSNs can be deployed in hospitals or home environments. In this system sensor data is collected and sent to the clouds. Different users such as hospitals, clinics, researchers, or even patients themselves can access their data from the Clouds. The proposed architecture can provide cost efficient model for automating hospitals and other life care agencies, managing real-time data from various sensors, medical professionals, support privacy and strong authentication mechanism. This architecture makes it possible to launch web applications quickly and also upgrade e-healthcare applications easily as and when required. This framework is very cheap and main goal is for better health outcome. Sudarsan et. al. [16] presents a Secured Wireless Sensor Networkintegrated Cloud Computing for Life Care. This is used to monitors human health, activities, and shares information among doctors, care-givers, clinics, and pharmacies in the Cloud so that users can have better care with low cost. It incorporates various technologies with novel ideas including; sensor networks, Cloud computing security, and activities recognition. Ahmed Lounis et. al. [17] focuses on the major issue of data management in Wireless Sensor Networks. The huge amount of data generated and collected by medical sensor networks introduces several challenges for the existing architectures. This paper proposes an innovative architecture for collecting and accessing large amount of data generated by medical sensor networks. This architecture aims at resolving the challenges and makes easy information sharing between healthcare professionals. Giancarlo Fortino et. al. [24] presents Body Cloud, a system architecture based on Cloud Computing for the management and monitoring of body sensor data streams. It system includes scalability and flexibility of resources. An important application is monitoring human condition using distributed sensor nodes. A network of body sensors in a group of people generates large amounts of data that should be stored and processed. Cloud computing can provide a powerful, scalable storage and processing infrastructure to perform both online and offline analysis and mining of body sensor data stream. David Tracey et. al. [14] discusses about Wireless Sensor Network and cloud computing. Wireless Sensor Networks (WSNs) consist of large number of applications and services to interact with the physical world. This paper uses cloud services and big data approaches to store data and to analyse data. With the cloud and the big data approach, scalability and availability have increased, which will be needed for many of the devices in the Internet of Things (IoT). The paper also proposes an architecture to integrate information of WSNs and their services and it help in flowing of data from sensor through to services. RazviDoomunet. al. [15] defined a solution for the global attack problem on the overall data transmissions and the existing approaches do not provide sufficient privacy when the attacker can visualize the transmissions and infer contextual information. The Paper proposes SECLOUD: Source and Destination Seclusion using Clouds to confuse the true source/destination nodes and make them indistinguishable among a group of neighbor nodes which works well even under network-wide traffic visualization by a global attacker. Rosangelaet. al. [20] present the main aspects of a prototype to test Hadoop framework applied to process climate related data sets in a cloud computing infrastructure. Hadoop frame work is used to handle big data normally in unstructured forms, such as text, sensor data, audio, video, log files, and more. Climate and sensor network data are applied to assist the decision making process in agricultural production. Rao et. al. [25] describes how Internet of Things and Cloud computing together overcome the Big Data issues. The paper discusses about sensing service on cloud using few applications like Agriculture and Environment monitoring. The paper proposes a prototype model for providing sensing as a service on cloud. Wireless Sensor Network increasingly enables applications and services to interact with the physical world. Such services may be located across the Internet from the sensing network. Cloud services and big data approaches may be used to store and analyse this data to improve scalability and availability.
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Volume 2, Issue 5, May 2014
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3. CONCLUSION Both Wireless Sensor Network and Cloud Computing technologies along with their applications and overview of architectural extension to Wireless Sensor Network are discussed. The applications of cloud computing to enhance the reliability and availability of Wireless Sensor Networks are discussed with special emphasis on its real time applications. However, the security issues involved in the integration process are of key importance and need critical focus.
4. ACKNOWLEDGMENT The authors would like to acknowledge and thank Technical Education Quality Improvement Program [TEQIP] Phase 2, BMS College of Engineering and SPFU [State Project Facilitation Unit], Karnataka for supporting the research work.
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[14]David Tracey, Cormac Sreenan, “A Holistic Architecture for the Internet of Things, Sensing Services and Big Data”, 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, Delft, 13-16 May 2013, pp 546-553, Print ISBN: 978-1-4673-6465-2, DOI: 10.1109/CCGrid.2013.100. [15]RazviDoomun, ThaierHayajneh, Prashant Krishnamurthy, David Tipper, “SECLOUD: Source and Destination Seclusion Using Clouds for Wireless Ad hoc Networks”, IEEE Symposium on Computers and Communications, Sousse, 5-8 July 2009, pp 361-367, Print ISBN: 978-1-4244-4672-8, DOI: 10.1109/ISCC.2009.5202367. [16]Sudarsan Rao Vemuri, Dr. N Satyanarayana, V Lakshmi Prasanna, “Generic Integrated Secured WSN-Cloud Computing For U-Life Care”, International Journal of Engineering Science and Advanced Technology, Vol 2, Issue 4, pp 897-907, Aug 2012, ISSN: 2250-3676. [17]Ahmed Lounis, AbdelkrimHadjidj, AbdelmadjidBouabdallah, YacineChallal, “Secure and Scalable Cloud-based Architecture for e-Health Wireless sensor networks”, 21st International Conference on Computer Communications and Networks (ICCCN), Munich, 30 July-2 Aug 2012, pp 1-7, Print ISBN: 978-1-4673-1543-2, DOI: 10.1109/ICCCN.2012.6289252. [18]Khandakar EntenamUnayes Ahmed, Mark A Gregory, “Integrating Wireless Sensor Networks with Cloud Computing”, 7th International Conference on Mobile Ad-hoc and Sensor Networks (MSN), Beijing, 16-18 Dec 2011, pp 364-366, Print ISBN: 978-1-4577-2178-6, DOI: 10.1109/MSN.2011.86. [19]Mohamed Firdhous, Osman Ghazali, Suhaidi Hassan, “Trust Management in Cloud Computing: A Critical Review”, International Journal on Advances in ICT for Emerging Regions, Sept 2011, Vol. 2, No. 4, pp 24-36, DOI: http://arxiv.org/abs/1211.3979. [20]Rosangela de Fátima Pereira, Marcelo Risse de Andrade, ArturCarvalhoZucchi, Karen Langona, Walter Akio Goya, Nelson Mimura Gonzalez, Tereza Cristina MeloBrito de Carvalho, Jan-Erik Mangs, AzimehSefidcon, “Distributed processing from large scale sensor network using Hadoop”, IEEE International Congress on Big Data, Santa Clara, CA, 27 June-2 July 2013, pp 417-418, Print ISBN: 978-0-7695-5006-0, DOI: 10.1109/BigData.Congress.2013.64. [21]Wei Wang, Kevin Lee, David Murray, “Integrating Sensors with the cloud using dynamic proxies”, IEEE 23rd International symposium on Personal Indoor and Mobile Radio communications, Sydney, 9-12 Sept. 2012, pp 1466-1471, DOI: 10.1109/PIMRC.2012.6362579. [22]Rajesh V, O Pandithurai, S Mageshkumar, “Wireless sensor data on cloud”, IEEE International Conference on Communication, Control and Computing Technologies, Ramanathapuram, 7-10 Oct 2010, pp 476-481, DOI: 10.1109/ICCCCT.2010.5670599. [23]Wen Qiang Wang, Xiaoming Zhang, Jiangwei Zhang, Hock Beng Lim, “Smart Traffic Cloud: An Infrastructure for Traffic Applications”, IEEE 18th International Conference on Parallel and Distributed Systems, Singapore, 1719 Dec 2012, pp 822-827, DOI: http://doi. ieeecomputersociety.org/10.1109/ICPADS.2012.134. [24]Giancarlo Fortino, MukaddimPathan, Giuseppe Di Fatta, “Body Cloud: Integration of Cloud Computing and Body Sensor Networks”, IEEE 4th International Conference on Cloud computing Technology and Science, Taipei, 3-6 Dec 2012, pp 851-856, DOI: 10.1109/CloudCom.2012.6427537. [25]B. B. P. Rao, P. Saluja, N. Sharma, A. Mittal, S. V. Sharma,“Cloud computing for internet of things and sensing based applications”, 6th International Conference on Sensing Technology, Kolkata, India, 18-20 Dec 2012, pp 374– 380, DOI: 10.1109/ICSensT.2012.6461705.
AUTHOR Ms. Swathi B S is a PG Scholar in Computer Networks and Engineering at B.M.S College Of Engineering, Bangalore. My research areas are Cloud Computing and Wireless Sensor Network.
Dr. H S Guruprasad is working as Professor and Head, Computer Science Department at BMS College of Engineering, Bangalore. He has twenty four years of teaching experience. He has been awarded with Rashtriya Gaurav award in 2012. His research areas are Network Communications, algorithms, Cloud Computing and Sensor Network.
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