Towards Integration of Wireless Sensor Networks and Cloud Computing

2015 IEEE 7th International Conference on Cloud Computing Technology and Science

Towards Integration of Wireless Sensor Networks and Cloud Computing Chunsheng Zhu∗ , Xi Li† , Hong Ji† , Victor C. M. Leung∗ of Electrical and Computer Engineering, The University of British Columbia, Canada † Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, China Email: [email protected], [email protected], [email protected], [email protected]

∗ Department

service users (CSUs). Thus CSUs can obtain their required sensory data with just a simple client to access the cloud. In this new paradigm, SNPs act as the data sources for CSPs, and CSUs are the data requesters for CSPs. However, WSNCC integration is still in its infancy and a lot of research efforts are expected to emerge in this area.

Abstract—Recently, induced by incorporating the ubiquitous data gathering capability of wireless sensor networks (WSNs) as well as the powerful data storage and data processing abilities of cloud computing (CC), WSN-CC integration is attracting growing interest from both academia and industry. However, WSN-CC integration is still in its infancy and a lot of research efforts are expected to emerge in this area. Towards WSN-CC integration, this paper first presents four ignored research issues about WSN-CC integration. Further, our accomplished work and ongoing work regarding solving the identified research issues are briefly described. The analytical and experimental results conducted in our work show that the approaches proposed can effectively relieve the corresponding research problem. We hope our work can attract more researches into WSN-CC integration to make it develop faster and better.

SNP

Transmit sensory data

CSP

Reply data requests

CSU

Cloud Send data requests

Send data feedbacks SNP1

CSP1

CSU1

Cloud 1

Keywords-Wireless sensor networks; cloud computing; integration; research issues; effectiveness

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CSP2

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Cloud 2

I. I NTRODUCTION A. Research motivation

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Consisting of spatially distributed autonomous sensors, wireless sensor networks (WSNs) [1] are networks which are capable of sensing the physical or environmental conditions (e.g., temperature, humidity, pressure, vibration, motion, etc.). Enabling convenient and on-demand network access to a shared pool of configurable computing resources (e.g., servers, storage, networks, applications, services), cloud computing (CC) [2] is a widely recognized novel computing model. Integrating CC into a mobile environment, mobile cloud computing (MCC) [3] can further offload much of the data processing and storage tasks from mobile devices (e.g., smart phones, tablets, etc.) to the cloud. Motivated by complementing the ubiquitous data gathering capability of WSNs as well as the powerful data storage and data processing abilities of CC, WSN-CC integration is receiving growing attention from both academic and industrial communities recently [4] [5]. This new integration paradigm is induced by the potential application scenarios shown in Fig. 1. Specifically, sensor network providers (SNPs) offer the sensory data (e.g., traffic, video, humidity, temperature, weather) collected by the deployed WSNs to the cloud service providers (CSPs). CSPs utilize the powerful cloud to store and process the sensory data and further offer the processed sensory data on demand to the cloud 978-1-4673-9560-1/15 $31.00 © 2015 IEEE DOI 10.1109/CloudCom.2015.27

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Figure 1.

Example of application scenarios of WSN-CC integration

B. Research contribution Towards WSN-CC integration, the main contributions of this paper are summarized as follows. •

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This paper first presents four ignored research issues regarding WSN-CC integration. This advances WSN-CC integration, by demonstrating the novelty and necessity of the needed work for solving these ignored research problems. This paper further briefly describes our accomplished work and ongoing work, aiming at tackling the identified research issues. The performed analytical and experimental results demonstrate that the approaches proposed in our work can effectively mitigate the corresponding research problem. We hope our work can attract more researches into WSN-CC integration, to make it develop faster and better.

A. Research problem 1

C. Organization

1) MCC applications are usually utilized in a location specific way. For instance, the online work schedule application might be accessed when the mobile user is on the way to work, but not when the mobile user is in a restaurant in the evening. Similarly, the traffic news application may be useful for the mobile user to receive the traffic information of a certain region before the mobile user actually gets there, while it is unlikely that the traffic news application is always accessed by the mobile user regardless of his or her current location. In brief, the current locations of mobile users usually determine the specific data mobile users might request. 2) Most sensors are usually equipped with nonrechargeable batteries with limited energy. If such sensor nodes continuously transmit the gathered data to the cloud, the energy of these sensor nodes will be depleted quickly and the lifetime of the WSN will be short.

For the rest of this paper, Section II reviews the related work about WSN-CC integration and Section III presents the ignored four research problems during WSN-CC integration. Our accomplished work is briefly shown in Section IV and our ongoing work is briefly described in Section V. Section VI concludes the paper. II. R ELATED W ORK For the state of the art, current related researches about CC-WSN integration are mainly with the following two goals: 1) improving the performance of WSN with cloud; 2) better utilizing the sensory data of WSN with cloud. A. Improving the performance of WSN with cloud To reduce the transmissions and computing time of sensing data to enhance the overall performance for the services of fall events detection and 3-D motion reconstruction, a collaborative computing framework integrating cloud and wireless body sensor networks is proposed in [6]. Aiming at effectively configure body sensor networks in an adaptive and stable way via seeking the trade-offs among conflicting objectives (e.g., resource consumption and data yield), a cloud-integrated architecture named BitC (Body-in-theCloud) is shown in [7]. For sharing the network resources between any two multimedia sensor nodes, a channel characterization scheme is presented in [8], applying a crosslayer admission control in dynamic cloud-based multimedia sensor networks.

B. Research problem 2 1) Authentication of CSPs and SNPs: Malicious attackers may fake to be genuine CSPs to communicate with CSUs, or impersonate genuine SNPs to communicate with CSPs. Then CSUs and CSPs cannot eventually obtain any service from the fake CSPs and SNPs respectively. Meanwhile, the trust and reputation of the authentic CSPs and SNPs are also impaired by these fake CSPs and SNPs. 2) Trust and reputation calculation and management of CSPs and SNPs: Without trust and reputation calculation and management of CSPs and SNPs, CSU may easily select a CSP with low trust and reputation. Then the service from CSP to CSU fails to be successfully delivered quite often. What’s more, it is easy for CSP to choose an untrustworthy SNP, which delivers the requested service with an unacceptable large latency. In the meantime, the untrustworthy SNP probably might only offer the requested service for a very short time period unexpectedly. These two issues not only seriously prevent the CSU from achieving the satisfied service from the genuine CSP, but also impede the CSP from receiving the desirable service from the authentic SNP.

B. Better utilizing the sensory data of WSN with cloud About storing, tagging, retrieving, analyzing, comparing and searching health sensor data, a cloud-based platform (i.e., Wiki-Health) is proposed in [9]. Similarly, the motivation of [10] is to design and develop a cloud-based virtualized middleware platform, to collect, process and integrate the information generated by multiple sensors and WSN, as well as manage the business process procedures supported by the sensors’ infrastructure. In addition, considering the scenario that the sensory data is utilized by the cloud to perform real-time alerts in critical situations, an event matching algorithm based on subscriber category is introduced in [11], for distributing sensor data to the appropriate subscriber.

C. Research problem 3 In these potential applications (e.g., ubiquitous healthcare monitoring, earth observation, tunnel monitoring, wildlife monitoring, environmental monitoring for disaster detection, agriculture and irrigation control, transportation and vehicle real-time visualization) of WSN-MCC integration, quite a number of them actually require the WSN to reliably offer

III. R ESEARCH P ROBLEMS To the best of our knowledge, during WSN-CC integration, the following four research problems were ignored, have been identified and analyzed in our accomplished work [12] [13] [14] and our ongoing work [15].

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sensory data that are more useful to the cloud, based on the requests of the mobile users. Take smart house monitoring as an example, although various monitored house information collected by the strategically deployed sensors (e.g., video sensors, image sensors and other types of sensors) can be offloaded to the cloud to let the house owner or other authenticated and permitted person conveniently obtain their desired data with the mobile devices (e.g., smart phones, tablet computers), it is expected that videos from some locations (e.g., storage room) are of little interest, while videos from other locations (e.g., front door, back door, windows) are considered to be more important to ensure that the house is without any unexpected intrusion. Thus, not all the sensory data are useful (i.e., actually utilized) for the cloud to satisfy user requests, while transmitting these data (e.g., multimedia data) to the cloud will take substantial network bandwidth. From this point, we can observe that 1) sensory data that are more useful to the mobile users should be offered from WSN to cloud. On the other side, to perform intelligently monitoring the house, the WSN needs to successfully collect and transmit the gathered information (e.g., videos, images) to the cloud continuously, meaning that 2) the sensory data should be reliably offered from the WSN to the cloud.

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This work is the first work, considering sleep scheduling in WSNs to support location-based mobile cloud applications. This clearly distinguishes our work from current WSN-MCC integration methods. This work further proposes two novel CLSS schemes, targeted at sleep scheduling for WSNs integrated with MCC. Both the location based feature of mobile applications as well as the energy concern of WSNs are taken into account by the CLSS schemes. CLSS1 is with the focus on the energy consumption of the integrated WSN, and CLSS2 further considers the scalability and robustness of the integrated WSN.

B. Accomplished work 2 For solving research problem 2, accomplished work 2 [13] first analyzes the authentication of CSPs and SNPs as well as the trust and reputation about the services of CSPs and SNPs. Further, for WSN-CC integration, this work proposes a novel authenticated trust and reputation calculation and management (ATRCM) system. Specifically, considering (i) the authenticity of CSP and SNP; (ii) the attribute requirement of CSU and CSP; (iii) the cost, trust and reputation of the service of CSP and SNP, the proposed ATRCM system performs the following three functions: 1) Authenticating CSP and SNP to avoid malicious impersonation attacks; 2) Calculating and managing trust and reputation about the service of CSP and SNP; 3) Helping CSU select appropriate CSP and assisting CSP in choosing desirable SNP. The following presents main contributions of this work. • This work is the first work, exploring the trust and reputation calculation and management system with authentication for the CC-WSN integration, which clearly distinguishes the novelty of our work and its scientific impact on existing schemes integrating WSNs and CC. • This work further proposes an ATRCM system for CCWSN integration. It incorporates authenticating CSP and SNP, then considers the attribute requirement of CSU and CSP as well as cost, trust and reputation of the service of CSP and SNP, for enabling CSU to select genuine and appropriate CSP and assisting CSP in choosing authentic and desirable SNP.

D. Research problem 4 Improving quality of service (QoS) of WSN-CC integration with trust assistance: QoS is always a fascinating and valuable topic, as QoS (e.g., throughput, response time) always plays a vital role for users to eventually use the service (e.g., service provided by WSN-CC integration). Trust is supposed to enhance the performance of an existing system, by assigning trust value to the entity of the system. Potential way (e.g., with trust assistance) to improve the QoS of WSN-CC integration is always worth to be explored. IV. ACCOMPLISHED W ORK A. Accomplished work 1 To solve research problem 1, in accomplished work 1 [12], we propose two collaborative location-based sleep scheduling (CLSS) schemes for WSNs integrated with MCC. Considering the locations of mobile users, CLSS schemes dynamically change the awake or asleep status of sensor nodes in the integrated WSN to reduce the energy consumption. Particularly, CLSS1 aims at maximizing energy consumption savings of the integrated WSN, while CLSS2 considers also the scalability and robustness of the integrated WSN. Further, CLSS schemes are evaluated analytically and by simulations, to demonstrate that they can prolong the lifetime of the integrated WSN while satisfying the data requests of the mobile users. The below summarizes main contributions of this work.

C. Accomplished work 3 About solving research problem 3, our accomplished work 3 [14] first identifies the critical issues which affect the usefulness of sensory data and reliability of WSN. Then it proposes a novel WSN-MCC integration scheme named TPSS. Particularly, TPSS consists of two parts: 1) TPSDT (Time and Priority based Selective Data Transmission) for WSN gateway to selectively transmit sensory data that are more useful to the cloud, considering the time and priority features of the data requested by the mobile user; 2) PSS

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demonstrate the effectiveness of the proposed approaches in mitigating the corresponding research problem. We hope our work can attract more researches into WSN-CC integration for making it develop faster and better.

(Priority-based Sleep Scheduling) algorithm for WSN to save energy consumption, so that it can gather and transmit data in a more reliable manner. Analytical and experimental results show the effectiveness of TPSS, regarding improving the usefulness of sensory data and reliability of WSN for WSN-MCC integration. Main contributions of this work are demonstrated as follows. • This work is the first work, investigating jointly the issues about usefulness of sensory data and reliability of WSN, from the view of WSN-MCC integration. • This work further proposes a novel TPSS scheme including TPSDT and PSS for WSN-MCC integration, targeted respectively at enhancing the usefulness of sensory data and reliability of WSN. In particular, TPSDT is used by WSN gateway, to selectively transmit sensory data that are more useful to the cloud. Moreover, PSS is utilized by WSN to save energy consumption for collecting and transmitting data, leading to a more reliable operation. Both TPSDT and PSS take into account the time and priority characteristics of the data requested by the mobile user.

ACKNOWLEDGEMENT This work was supported by a University of British Columbia Four Year Doctoral Fellowship, the Canadian Natural Sciences and Engineering Research Council under Grant CRDPJ 434659-12, the ICICS/TELUS People & Planet Friendly Home Initiative at UBC, and the National Science Foundation for Young Scientists of China under Grant 61302080. R EFERENCES [1] C. Zhu, L. Shu, T. Hara, L. Wang, S. Nishio, and L. T. Yang, “A survey on communication and data management issues in mobile sensor networks,” Wireless Communications and Mobile Computing, vol. 14, no. 1, pp. 19–36, Jan. 2014. [2] R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, “Cloud computing and emerging it platforms: Vision, hype, and reality for delivering computing as the 5th utility,” Future Generation Computer Systems, vol. 25, no. 6, pp. 599–616, Jun. 2009. [3] C. Zhu, V. C. M. Leung, X. Hu, L. Shu, and L. T. Yang, “A review of key issues that concern the feasibility of mobile cloud computing,” in Proc. IEEE CPSCom, 2013, pp. 769–776. [4] C. Zhu, H. Wang, X. Liu, L. Shu, L. T. Yang, and V. C. M. Leung, “A novel sensory data processing framework to integrate sensor networks with mobile cloud,” IEEE Systems Journal, pp. 1–12, 2014. [5] C. Zhu, X. Li, V. C. M. Leung, X. Hu, and L. T. Yang, “Job scheduling for cloud computing integrated with wireless sensor network,” in Proc. IEEE CloudCom, 2014, pp. 62–69. [6] C.-F. Lai, M. Chen, J.-S. Pan, C.-H. Youn, and H.-C. Chao, “A collaborative computing framework of cloud network and wbsn applied to fall detection and 3-d motion reconstruction,” IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 2, pp. 457–466, Mar. 2014. [7] C.-R. Yi, J. Suzuki, D. H. Phan, S. Omura, and R. Hosoya, “An evolutionary game theoretic approach for configuring cloud-integrated body sensor networks,” in Proc. IEEE NCA, 2014, pp. 277–281. [8] L. D. P. Mendes, J. J. P. C. Rodrigues, J. Lloret, and S. Sendra, “Cross-layer dynamic admission control for cloud-based multimedia sensor networks,” IEEE Systems Journal, vol. 8, no. 1, pp. 235–246, Mar. 2014. [9] Y. Li, L. Guo, C. Wu, C.-H. Lee, and Y. Guo, “Building a cloud-based platform for personal health sensor data management,” in Proc. IEEEEMBS BHI, 2014, pp. 223–226. [10] D. Vouyioukas, A. Moralis, M. Sardis, D. Drakoulis, G. Labropoulos, S. Kyriazakos, and D. Dres, “Epikouros - virtualized platforms using heterogeneous sensor services in cloud computing environment,” in Proc. VITAE, 2014, pp. 1–5. [11] S. S. Grace and M. R. Sumalatha, “Event matching based on subscriber category in sensor cloud,” in Proc. ICRTIT, 2014, pp. 1–5. [12] C. Zhu, V. C. M. Leung, L. T. Yang, and L. Shu, “Collaborative location-based sleep scheduling for wireless sensor networks integrated with mobile cloud computing,” IEEE Transactions on Computers, vol. 64, no. 7, pp. 1844–1856, Jul. 2015. [13] C. Zhu, H. Nicanfar, V. C. M. Leung, and L. T. Yang, “An authenticated trust and reputation calculation and management system for cloud and sensor networks integration,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 1, pp. 118–131, Jan. 2015. [14] C. Zhu, Z. Sheng, V. C. M. Leung, L. Shu, and L. T. Yang, “Towards offering more useful data reliably to mobile cloud from wireless sensor network,” IEEE Transactions on Emerging Topics in Computing, vol. 3, no. 1, pp. 84–94, Mar. 2015. [15] C. Zhu, V. C. M. Leung, L. T. Yang, L. Shu, J. J. P. C. Rodrigues, and X. Li, “Trust assistance in sensor-cloud,” in Proc. INFOCOM Workshops, 2015, pp. 342–347.

V. O NGOING W ORK With respect to research problem 4, the basic idea is to incorporate trust into both WSN and CC to enhance the QoS of a WSN-CC integration. Specifically, we can propose the trust-assisted Sensor-Cloud (TASC), in which trusted sensors (i.e., sensors with trust values exceeding a threshold) gather and transmit sensory data to the cloud first. Then the cloud chooses the trusted data centers (i.e., data centers with trust values exceeding a threshold), to store, process the sensory data and further deliver the processed sensory data to users on demand. The novelty of this work is that currently there is no research work, applying trust into both WSN and CC to enhance the QoS for users to obtain sensory data from the cloud in a WSN-CC integration. The importance of this work is to improve the QoS (e.g., throughput, response time) for users to achieve sensory data from the cloud, with trust assistance. A preliminary version of this work is presented in [15]. The simulation results present that TASC can substantially enhance the throughput and response time for users to receive sensory data from the cloud, compared with SC without trust assistance (SCWTA). VI. C ONCLUSION This paper focuses on the integration of WSNs and CC, which is a valuable and challenging topic. Particularly, towards WSN-CC integration, four ignored research problems have been identified in our work. Further, regarding solving those identified research problems, our accomplished work and ongoing work have been briefly shown. Extensive analytical and experimental results performed in these work

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