A Trust and Reputation Management System for Cloud ... - IEEE Xplore

IEEE ICC 2014 - Ad-hoc and Sensor Networking Symposium

A Trust and Reputation Management System for Cloud and Sensor Networks Integration Chunsheng Zhu∗ , Hasen Nicanfar∗ , Victor C. M. Leung∗ , Wenxiang Li†‡∗ , Laurence T. Yang§ ∗ Department

of Electrical and Computer Engineering, The University of British Columbia, Canada Email: {cszhu, hasennic, vleung}@ece.ubc.ca † School of Information Science and Engineering, Wuhan University of Science and Technology, China ‡ Hubei Province Key Laboratory of Systems Science in Metallurgical Process, China Email: [email protected] § Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, Canada Email: [email protected] Abstract—By incorporating the advantages of cloud computing (CC) and wireless sensor networks (WSNs), the integration of CC and WSNs attracts a lot of attention from both academia and industry. However, trust and reputation management for CC and WSNs integration is a critical and barely explored issue, which could strongly prevent the cloud service users (CSUs) from choosing the desirable cloud service providers (CSPs) or hinder the CSP from selecting appropriate sensor network providers (SNPs). To fill the gap, this paper proposes a novel trust and reputation management system for CC and WSNs integration. Considering the attribute requirement of CSU and CSP as well as the cost, trust and reputation of the service of CSP and SNP, the proposed system achieves the following two goals: 1) calculate and manage the trust and reputation regarding the service of CSP and SNP; 2) help CSU choose CSP and assist CSP in choosing SNP. Evaluation results are also shown to verify effectiveness of the proposed system. Index Terms—Cloud; sensor networks; integration; trust; reputation

I. I NTRODUCTION Cloud computing (CC) is a model, enabling convenient and on-demand network access for a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) which could be rapidly provisioned and released with minimal management effort or service provider interaction [1] [2] [3] [4]. One distinctive feature of CC is that users can elastically utilize the infrastructure (e.g., servers, networks, and storages), platforms (e.g., middleware services and operating systems), and softwares (e.g., application programs) provided by cloud providers in an on-demand fashion. Benefiting from the powerful CC, not only the operating cost and business risks as well as maintenance expenses of service providers can be greatly lowered, but also the service scale can be expanded on demand and web-based easy access for clients could be offered. Furthermore, wireless sensor networks (WSNs) consist of spatially distributed autonomous sensors sensing the physical or environmental conditions (e.g., temperature, sound, vibration, pressure, motion, etc.) [5] [6] [7]. By changing the traditional way for people to interact with the physical world, WSNs are widely focused with their great potential in military, industry, and civilian applications (e.g., battlefield surveillance,

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industrial process monitoring, forest fire detection, health monitoring, etc.). For example, about forest fire detection, by strategically, randomly and densely deploying the sensor nodes into a forest, the exact origin of a forest fire could be relayed to the end users before the forest fire becomes uncontrollable without the vision of physical fire. By incorporating the advantages of CC and WSNs, the integration of CC and WSNs is receiving much attention from both academic and industrial community (e.g., [8] [9] [10] [11] [12] [13]). This integration trend is driven by the potential application scenarios demonstrated in Fig. 1. Specifically, sensor network providers (SNPs) offer the sensory data (e.g., traffic, weather, video, temperature) gathered by the deployed WSNs to the cloud service providers (CSPs). CSPs utilize the powerful cloud to store and process the sensory data and then further provide the processed sensory data to the cloud service users (CSUs) on demand. SNPs act as the data source for CSPs. And CSUs are the data requesters for CSPs. During the integration of CC and WSNs, one very critical and barely explored issue is the management of the trust and reputation of CSPs and SNPs. Without acquaintance of the trust and reputation of CSPs and SNPs, a CSU may easily choose a CSP with low trust and reputation. Then the service from CSP to CSU fails to be successfully delivered quite often. In addition, it is very likely for a CSP to choose an untrustworthy SNP which delivers the service the CSP requests with unacceptable latency. All these would strongly prevent the CSU from choosing the desirable CSP or hinder the CSP from selecting the appropriate SNP. To the best of our knowledge, there is no research discussing and analyzing the management of both trust and reputation for CC and WSNs integration. The purpose of our work is to propose a novel trust and reputation management system for CC and WSNs integration. Particularly, the proposed system owns the following two functions: 1) calculate and manage the trust and reputation with respect to the service of CSP and SNP 2) help CSU choose CSP and assist CSP in selecting SNP, based on the attribute requirement of CSU and CSP as well as the cost, trust and reputation of the service of CSP and SNP.


CSU

Send data requests Reply data requests

CSP Cloud

Transmit sensory data Send data feedbacks

CSP1

CSU1

SNP

SNP1

Cloud 1

SNP2

CSP2

CSU2

Cloud 2

SNP3

CSP3

CSU3

Cloud 3

Fig. 1.

Example of application scenarios of CC and WSN integration

The main contributions of this paper are twofold. • This paper is the first research work proposing the trust and reputation management system for CC and WSNs integration, which clearly distinguishes the novelty of our work and its scientific impact on current CC and WSNs integration schemes. • This paper further considers the attribute requirement of CSU and CSP and proposes a trust and reputation management system for CC and WSNs integration. The attribute requirement of CSU and CSP as well as the cost, trust and reputation of the service of CSP and SNP, are incorporated for the CSU to select CSP as well as for the CSP to choose SNP. For the rest part of paper, section II reviews the related work and section III introduces the system model. Details about the proposed trust and reputation management system for CC and WSNs integration are illustrated in section IV. Evaluation of the proposed system is performed in section V and this paper is concluded in section VI. II. R ELATED WORK There is a lot of research work with respect to trust or reputation in cloud. For instance, the focus of [14] [15] [16] is to analyze various factors (e.g., accountability, compromise servers, recovery, malicious insiders, data location, investigation, data segregation, availability, long-term viability, regulatory compliance, backup and recovery as well as privileged user access), which may affect the cloud user to trust the cloud. To evaluate the trustworthiness of the cloud resources, a cloud

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resource trustworthiness evaluation framework is proposed in [17], which utilizes an amor to constantly monitor and assess the cloud environment as well as check the resources the armor protects. For efficient reconfiguration and allocation of cloud resources to meet various user requests, a trust model is presented in [18], which collects and analyzes the reliability of cloud resources based on the historical information of servers so that the best available cloud resources to fulfill the user requests can be prepared in advance. Determining the credibility of trust feedbacks as well as managing trust feedbacks for cloud environments in [19], it shows a trust as service framework to improve current trust managements, by introducing an adaptive credibility model to distinguish the credible and malicious feedbacks. Discussing the cloud accountability issue in [20], it first uses detective controls to analyze the key issues to establish a trusted cloud and then gives a trustcloud framework consisted of five abstraction layers, where technical and policy-based approaches are applied to address accountability. Related to trust or reputation in cloud and sensor network integration, the only work is [21], which discusses how trust management could be effectively used to enhance the security of a cloud-integrated sensor network. Particularly, the main focus is to demonstrate that trust can be employed to perform trust-aware data transmission and trustaware data processing of the integrated WSN. To the best of our knowledge, there is no trust and reputation management system discussing CC and WSNs integration. Our work is the first system managing the trust and reputation in the scenario of integrating CC and WSNs.


TABLE I M AIN N OTATION D EFINITIONS

III. S YSTEM MODEL

•

•

•

•

• • •

•

There are multiple CSUs, CSPs as well as SNPs. The number of CSU, CSP and SNP are Nu , Nc and Nk , respectively. CSU = {CSU1 , CSU2 , . . . , CSUNu }. CSP = {CSP1 , CSP2 , . . . , CSPNc }. SN P = {SN P1 , SN P2 , . . . , SN PNk }. Each CSU, CPS as well as SNP owns several attributes. Specifically, the data service requested and required by the CSU has the following attributes: data service pay (DSP); data type (DT); data size (DS); data request speed (DRS); data service time (DST). The cloud service provided and managed by each CSP is with the following characteristics: cloud service charge (CSC); cloud operation cost (COC); network service pay (NSP); cloud service type (CST); cloud storage size (CSS); cloud processing speed (CPS); cloud operation time (COT). The sensor network offered and managed by each SNP owns the following properties: network service charge (NSC); network operation cost (NOC); sensor type (ST); network coverage (NC); network throughput (NT); network lifetime (NL). There is also an authorized trusted center entity (TCE), which stores the direct trust value (i.e., Tcu ) of each service from each CSP to each CSU as well as the direct trust value (i.e., Tkc ) of each service from each SNP to each CSP. TCE also stores the reputation value (i.e., Rc ) of each service provided by each CSP and the reputation value (i.e., Rk ) of each service provided by each SNP. Each CSU has a minimum acceptable trust value (i.e., Tscu ) of each service from each CSP to the CSU. Moreover, each CSP owns a minimum acceptable trust value (i.e., Tskc ) of each service from each SNP to the CSP. Similarly, each CSU has a minimum acceptable reputation value (i.e., Rsc ) regarding each service of each CSP. And each CSP owns a minimum acceptable reputation value (i.e., Rsk ) with respect to each service of each SNP. There is a cost difference (i.e., Cc ) between the CSC of CSP and DSP of CSU of each service, i.e., Cc =CSC-DSP. There is a cost difference (i.e., Ck ) between the NSC of SNP and NSP of CSP of each service, i.e., Ck =NSC-NSP. Each CSU has an acceptable range (i.e., Cbc ) of the difference between the CSC of CSP and DSP of CSU of each service. In addition, each CSP has an acceptable range (i.e., Cbk ) of the difference between the NSP of CSP and NSC of SNP of each service. The interval of Cbc and Cbk are |Cbc | and |Cbk |, respectively. Each CSU owns three weights (i.e., αc , βc and γc ) with respect to the importance of Cc , Tcu and Rc . αc + βc + γc = 1. Similarly, each CSP has three weights (i.e., αk , βk , γk ) regarding the importance of Ck , Tkc and Rk . αk + βk + γk = 1. The main notations in this paper are summarized in Table I.

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Symbol CSU CSP SNP DSP DT DS DRS DST CSC COC NSP CST CSS CPS COT NSC NOC ST NC NT NL TCE Tcu Tkc Rc Rk Tscu Tskc Rsc Rsk Cc Ck Cbc Cbk |Cbc | |Cbk | αc βc γc αk βk γk

Definition cloud service user cloud service provider sensor network provider data service pay data type data size data request speed data service time cloud service charge cloud operation cost network service pay cloud service type cloud storage size cloud processing speed cloud operation time network service charge network operation cost sensor type network coverage network throughput network lifetime trusted center entity direct trust value of service from CSP to CSU direct trust value of service from SNP to CSP reputation value of service provided by CSP reputation value of service provided by SNP minimum acceptable trust value of service from CSP to CSU minimum acceptable trust value of service from SNP to CSP minimum acceptable reputation value for service of CSP minimum acceptable reputation value for service of SNP CSC-DSP NSC-NSP acceptable range for Cc acceptable range for Ck interval of Cbc interval of Cbk weight with respect to the importance of Cc weight with respect to the importance of Tcu weight with respect to the importance of Rc weight with respect to the importance of Ck weight with respect to the importance of Tkc weight with respect to the importance of Rk

IV. P ROPOSED TRUST AND REPUTATION SYSTEM A. System flowchart between the CSU and CSPs •

Step 1: CSU checks whether the characteristics of CSPs satisfy the attributes of CSU. Filter the CSPs that are not satisfied. ⎧ CST ⊇ DT ⎪ ⎪ ⎪ ⎨CSS ≥ DS (1) ⎪ CP S ≥ DRS ⎪ ⎪ ⎩ COT ≥ DST

•

Step 2: CSU issues requests to TCE and obtains the Tcu value of the service from CSP to the CSU. CSU checks whether the Tcu value exceeds the Tscu value. Filter the CSPs that are not satisfied. Tcu ≥ Tscu

•

(2)

Step 3: CSU issues requests to TCE and obtains the Rc value of the service provided by the CSP. CSU checks


Step Start 1 2 3 4 5 6 End

CSPs

CSU

Provide attributes

Checks CSPs attributes and filters CSPs Issues requests to TCE Issues requests to TCE Calculates Cc value Chooses the service of CSP

CSU

Replies the Tcu value Replies the Rc value

Checks Tcu value and filters CSPs Checks Rc value and filters CSPs Checks Cc value and filters CSPs

Updates Tcu and Rc value

Fig. 2.

Step Start 1 2 3 4 5 6 End

TCE

Trust and reputation management system flowchart between CSU and CSPs

SNPs

CSP

Provide attributes

Checks SNPs attributes and filters SNPs Issues requests to TCE Issues requests to TCE Calculates Ck value Chooses the service of SNP

TCE

CSP

Replies the Tkc value Replies the Rk value

Checks Tkc value and filters SNPs Checks Rk value and filters SNPs Checks Ck value and filters SNPs

Updates Tkc and Rk value

Fig. 3.

Trust and reputation management system flowchart between CSP and SNPs

whether the Rc value exceeds the Rsc value. Filter the CSPs that are not satisfied. Rc ≥ Rsc •

•

Rc =

•

(4)

Step 6: TCE updates the Tcu value as well as the Rc value with the following processes. Tcu value update: TCE establishes a database which dynamically stores the success number (i.e., Scu ) and failure number (i.e., Fcu ) of each service issued from CSP to the CSU in the history. The trust value Tcu of each service from CSP to CSU is calculated as follows. Scu + 1 (6) Fcu + Scu + 2 Rc value update: TCE stores all the connection numbers between CSUs and CSP in the history. If the CSU chose the service of the CSP, then there is a connection between the CSU and the CSP with respect to the chosen service. Assume the number of connections between different

560

(7)

Step 1: CSP checks whether the characteristics of SNPs satisfy the attributes of CSP. CSP also checks whether the characteristics of SNP satisfy the attributes of CSU. Filter the SNPs that are not satisfied. ⎧ ST ⊇ DT ⎪ ⎪ ⎪ ⎨N C ⊇ DS ⎪ N T ≥ DRS ⎪ ⎪ ⎩ N L ≥ DST

(5)

Tcu =

CNc Nu + 1

B. System flowchart between the CSP and SNPs

Step 5: CSU chooses the service provided by the CSP with the maximum Mc . Cc + βc · Tcu + γc · Rc Mc = −αc · |Cbc |

•

(3)

Step 4: CSU calculates the Cc value between CSC of CSP and DSP of CSU and checks whether the Cc value is within the Cbc range. Filter the CSPs that are not satisfied. Cc ∈ Cbc

CSUs and the CSP for the chosen service is CNc . The reputation value of the service of the CSP is calculated as follows.

⎧ CST ⊇ ST ⎪ ⎪ ⎪ ⎨CSS ≥ N C ⎪ CP S ≥ N T ⎪ ⎪ ⎩ COT ≥ N L •

(8)

(9)

Step 2: CSP issues requests to TCE and obtains the Tkc value of the service from SNP to the CSP. CSP checks whether the Tkc value exceeds the Tskc value. Filter the SNPs that are not satisfied. Tkc ≥ Tskc

(10)


•

Step 3: CSP issues requests to TCE and obtains the Rk value of the service provided by the SNP. CSP checks whether the Rk value exceeds the Rsk value. Filter the SNPs that are not satisfied. Rk ≥ Rsk

•

Step 4: CSP calculates the Ck value between NSC of SNP and NSP of CSP and checks whether the Ck value is within the Cbk range. Filter the SNPs that are not satisfied. Ck ∈ Cbk

•

Ck + βk · Tkc + γk · Rk |Cbk |

(13)

Step 6: TCE updates the Tkc value and the Rk value as follows. Tkc value update: TCE establishes a database which dynamically stores the success number (i.e., Skc ) and failure number (i.e., Fkc ) of service sent from SNP to the CSP in the history. The trust value Tkc of the service from CSP to SNP is calculated as follows. Tkc =

Skc + 1 Fkc + Skc + 2

(14)

Rk value update: TCE stores all the connection numbers between CSPs and SNP in the history. If the CSP chose the SNP, then there is connection between the CSP and SNP regarding the chosen service. Assume the number of connections between different CSPs and the SNP for the chosen service is CNk . The reputation value of the service of the SNP is calculated as follows. Rk =

CNk Nc + 1

CSU1 CSU1 CSU1 CSU2 CSU2 CSU2 CSU3 CSU3 CSU3

↔ CSP1 ↔ CSP2 ↔ CSP3 ↔ CSP1 ↔ CSP2 ↔ CSP3 ↔ CSP1 ↔ CSP2 ↔ CSP3

(15)

V. E VALUATION A. Evaluation setup To perform the evaluation, we utilize the following case study. There are three CSUs, three CSPs and three SNPs. In addition, all characteristics of CSP satisfy the attributes of CSU and all characteristics of SNP satisfy the attributes of CSP. Detailed parameters with respect to CSU and CSP as well as detailed parameters regarding CSP and SNP are shown in Table II and Table III, respectively. For CSUs, CSPs and SNPs with other parameters, the evaluation process is similar. We analyze whether our proposed system can fulfill the predetermined goals: 1) calculation and management of trust and reputation with respect to the service of CSP and SNP 2) help the CSU choose CSP and assist CSP in selecting SNP based on the attribute requirement of CSU and CSP as well as cost, trust and reputation of the service of CSP and SNP.

561

Cc -10 -15 -20 -15 -20 -25 -20 -25 -30

Tcu 0.7 0.8 0.9 0.7 0.8 0.9 0.7 0.8 0.9

Rc 0.8 0.7 0.6 0.8 0.7 0.6 0.8 0.7 0.6

Cbc [-30, 30] [-30, 30] [-30, 30] [-30, 30] [-30, 30] [-30, 30] [-30, 30] [-30, 30] [-30, 30]

Tscu 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

Rsc 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

Tskc 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

Rsk 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

TABLE III PARAMETERS OF CSP AND SNP

(12)

Step 5: CSP chooses the service provided by the SNP with the maximum Mk . Mk = −αk ·

•

(11)

TABLE II PARAMETERS OF CSU AND CSP

CSP1 CSP1 CSP1 CSP2 CSP2 CSP2 CSP3 CSP3 CSP3

↔ SN P1 ↔ SN P2 ↔ SN P3 ↔ SN P1 ↔ SN P2 ↔ SN P3 ↔ SN P1 ↔ SN P2 ↔ SN P3

Ck -10 -15 -20 -15 -20 -25 -20 -25 -30

Tkc 0.8 0.7 0.6 0.8 0.7 0.6 0.8 0.7 0.6

Rk 0.7 0.6 0.5 0.7 0.6 0.5 0.7 0.6 0.5

Cbk [-30, 30] [-30, 30] [-30, 30] [-30, 30] [-30, 30] [-30, 30] [-30, 30] [-30, 30] [-30, 30]

B. Evaluation results Regarding the calculation and management of trust and reputation with respect to the service of CSP and SNP, the process is illustrated in Step 6 of the system flowchart between the CSU and CSPs as well as Step 6 of the system flowchart between the CSP and SNPs, which are shown in section IV. The weight set 1 as well as the corresponding results with respect to choosing CSPs and SNPs are shown in Table IV and Table V, respectively. In weight set 1, CSUs and CSPs take Cc , Tcu and Rc all into account. With equation (5) and equation (13), we can get that CSU1 , CSU2 and CSU3 all choose CSP3 with weight set 1. In addition, CSP1 , CSP2 and CSP3 all choose SN P1 . Furthermore, Table VI and Table VII present weight set 2 as well as the corresponding results regarding selecting CSPs and SNPs, respectively. For weight set 2, CSUs and CSPs only consider one of Ck , Tkc and Rk . Similarly, based on equation (5) and equation (13), we can obtain that CSU1 and CSU2 chooses CSP3 while CSU3 selects CSP1 . Meanwhile, CSP1 selects SN P3 while CSP2 and CSP3 both choose SN P1 . TABLE IV W EIGHT SET 1 OF CSU AND CORRESPONDING RESULTS CSU1 CSU2 CSU3

αc 1/3 1/2 1/5

βc 1/3 1/4 2/5

γc 1/3 1/4 2/5

Result CSP3 CSP3 CSP3

TABLE V W EIGHT SET 1 OF CSP AND CORRESPONDING RESULTS CSP1 CSP2 CSP3

αk 1/3 1/2 1/5

βk 1/3 1/4 2/5

γk 1/3 1/4 2/5

Result SN P1 SN P1 SN P1


TABLE VI W EIGHT SET 2 OF CSU AND CORRESPONDING RESULTS CSU1 CSU2 CSU3

αc 1 0 0

βc 0 1 0

γc 0 0 1

Result CSP3 CSP3 CSP1

TABLE VII W EIGHT SET 2 OF CSP AND CORRESPONDING RESULTS CSP1 CSP2 CSP3

αk 1 0 0

βk 0 1 0

γk 0 0 1

Result SN P3 SN P1 SN P1

From the above evaluation results, we can observe that our proposed system is indeed able to assist the CSU in selecting CSP as well as help CSP choose SNP, considering the attribute requirement of CSU and CSP as well as cost, trust and reputation of the service of CSP and SNP. Moreover, comparing the weights and corresponding results in Table IV with that in Table VI, we can deduce that different weights cannot always change the corresponding results for CSU to choose CSP. Similarly, the corresponding results for CSP to select SNP cannot always be affected by changing weights, comparing the weights and corresponding results in Table V with that in Table VII. VI. C ONCLUSION In this paper, we propose a novel trust and reputation management system to integrate cloud computing (CC) and wireless sensor networks (WSNs), focusing on trust and reputation management which is a critical and barely explored issue with respect to CC and WSNs integration. The design and evaluation about the proposed system are presented. They demonstrate that the proposed CC and WSN integration trust and reputation system owns the following two functions: 1) calculate and manage the trust and reputation with respect to the service of cloud service provider (CSP) and sensor network provider (SNP) 2) help the cloud service user (CSU) choose CSP and assist CSP in selecting SNP, based on the attribute requirement of CSU and CSP as well as cost, trust and reputation of the service of CSP and SNP. ACKNOWLEDGEMENT This work is supported by a Four Year Doctoral Fellowship from The University of British Columbia and by funding from the Natural Sciences and Engineering Research Council of Canada, TELUS and other industry partners. This work is also supported by the grant from Hubei Province Key Laboratory of Systems Science in Metallurgical Process (Wuhan University of Science and Technology) (Y201322). R EFERENCES [1] Q. Zhang, L. Cheng, and R. Boutaba, “Cloud computing: state-of-the-art and research challenges,” Journal of Ineternet Services and Applications, vol. 1, pp. 7–18, 2010.

562

[2] H. T. Dinh, C. Lee, D. Niyato, and P. Wang, “A survey of mobile cloud computing: Architecture, applications, and approaches,” Wireless Communications and Mobile Computing, vol. 13, pp. 1587–1611, 2013. [3] S. Wang and S. Dey, “Adaptive mobile cloud computing to enable rich mobile multimedia applications,” IEEE Transactions on Multimedia, vol. 15, pp. 870–883, 2013. [4] 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 International Conference on Cyber, Physical and Social Computing, 2013, pp. 769–776. [5] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: a survey,” Computer Networks, vol. 38, pp. 393–422, 2002. [6] 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, pp. 19–36, 2014. [7] W. Fang, F. Liu, F. Yang, L. Shu, and S. Nishio, “Energy-efficient cooperative communication for data transmission in wireless sensor networks,” IEEE Transactions on Consumer Electronics, vol. 56, pp. 2185–2192, 2010. [8] G. Fortino, M. Pathan, and G. D. Fatta, “Bodycloud: Integration of cloud computing and body sensor networks,” in Proc. IEEE 4th International Conference on Cloud Computing Technology and Science, 2012, pp. 851–856. [9] Y. Takabe, K. Matsumoto, M. Yamagiwa, and M. Uehara, “Proposed sensor network for living environments using cloud computing,” in Proc. 15th International Conference on Network-Based Information Systems, 2012, pp. 838–843. [10] R. Hummen, M. Henze, D. Catrein, and K. Wehrle, “A cloud design for user-controlled storage and processing of sensor data,” in Proc. IEEE 4th International Conference on Cloud Computing Technology and Science, 2012, pp. 232–240. [11] C. Zhu, V. C. M. Leung, L. T. Yang, X. Hu, and L. Shu, “Collaborative location-based sleep scheduling to integrate wireless sensor networks with mobile cloud computing,” in Proc. IEEE Globecom 2013 Workshop on Cloud Computing Systems, Networks, and Applications, 2013, pp. 451–456. [12] C. Zhu, V. C. M. Leung, H. Wang, W. Chen, and X. Liu, “Providing desirable data to users when integrating wireless sensor networks with mobile cloud,” in Proc. IEEE 5th International Conference on Cloud Computing Technology and Science, 2013. [13] A. Alamri, W. S. Ansari, M. M. Hassan, M. S. Hossain, A. Alelaiwi, and M. A. Hossain, “A survey on sensor-cloud: Architecture, applications, and approaches,” International Journal of Distributed Sensor Networks, pp. 1–18, 2013. [14] A. Sangroya, S. Kumar, J. Dhok, and V. Varma, “Towards analyzing data security risks in cloud computing environments,” in Proc. International Conference on Information Systems, Technology and Management, 2010, pp. 255–265. [15] “Top threats to cloud computing v1.0,” Cloud Security Alliance, March 2010. [16] A. Rashidi and N. Movahhedinia, “A model for user trust in cloud computing,” International Journal on Cloud Computing: Services and Architecture, vol. 2, pp. 1–8, 2012. [17] M. Kuehnhausen, V. S. Frost, and G. J. Minden, “Framework for assessing the trustworthiness of cloud resources,” in Proc. IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support, 2012, pp. 142–145. [18] H. Kim, H. Lee, W. Kim, and Y. Kim, “A trust evaluation model for qos guarantee in cloud systems,” International Journal of Grid and Distributed Computing, vol. 3, pp. 1–10, 2010. [19] T. H. Noor and Q. Z. Sheng, “Trust as a service: A framework for trust management in cloud environments,” in Proc. 12th international conference on Web information system engineering, 2011, pp. 314–321. [20] R. K. L. Ko, P. Jagadpramana, M. Mowbray, S. Pearson, M. Kirchberg, Q. Liang, and B. S. Lee, “Trustcloud: A framework for accountability and trust in cloud computing,” in Proc. IEEE World Congress on Services, 2011, pp. 584–588. [21] O. Savas, G. Jin, and J. Deng, “Trust management in cloud-integrated wireless sensor networks,” in Proc. International Conference on Collaboration Technologies and Systems, 2013, pp. 334–341.