Trust Assistance in Sensor-Cloud - IEEE Xplore

IEEE INFOCOM 2015 Workshop on Mobile Cloud and Virtualization

Trust Assistance in Sensor-Cloud Chunsheng Zhu∗ , Victor C. M. Leung∗ , Laurence T. Yang† , Lei Shu‡ , Joel J. P. C. Rodrigues§ , Xiuhua Li∗ ∗ Department

of Electrical and Computer Engineering, The University of British Columbia, Canada of Computer Science, St. Francis Xavier University, Canada ‡ Guangdong Provincial Key Lab. of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, China § Instituto de Telecomunicaço ˜ es, University of Beira Interior, Portugal Email: {cszhu, vleung}@ece.ubc.ca, [email protected], [email protected], [email protected], [email protected] † Department

Abstract—Incorporating 1) the ubiquitous data gathering ability of wireless sensor networks (WSNs) and 2) the powerful data storage and data processing capabilities of cloud computing (CC), Sensor-Cloud (SC) is attracting increasing attention from both academia and industry. In this paper, focusing on improving the quality of service (QoS) of SC for users to obtain sensory data from the cloud, we propose Trust-Assisted SC (TASC). In TASC, trusted sensors (i.e., sensors with trust values exceeding a threshold) collect and transmit sensory data to the cloud. Then the cloud selects the trusted data centers (i.e., data centers with trust values exceeding a threshold) to store, process the sensory data and further transmit the processed sensory data to users on demand. With extensive simulation results, we show that the TASC can substantially improve the throughput and response time for users to obtain sensory data from the cloud, compared with SC without trust assistance (SCWTA). Index Terms—Sensor-Cloud, quality of service, trust, throughput, response time

II. R ELATED WORK

I. I NTRODUCTION Incorporating 1) the ubiquitous data gathering ability of wireless sensor networks (WSNs) [1] and 2) the powerful data storage and data processing capabilities of cloud computing (CC) [2], Sensor-Cloud (SC) is receiving growing interest from both academia and industry [3] [4] [5] [6]. Particularly, as shown in Fig. 1, SC utilizes the powerful cloud consisting of multiple data centers, to store and process various sensory data (e.g., temperature, humidity, traffic, house monitoring) collected and transmitted from WSN first. Then the cloud transmits the processed sensory data to users on demand. In this whole process, from the viewpoint of user, WSNs act as the data sources, while the cloud is the data store and the user is the data buyer. In this paper, aiming at enhancing the quality of service (QoS) of SC for users to achieve the sensory data from the cloud, trust-assisted SC (TASC) is proposed. Particularly, TASC adopts the trusted sensors (i.e., sensors with trust values exceeding a threshold) to collect and transmit sensory data to the cloud. With that, trusted data centers (i.e., data centers with trust values exceeding a threshold) in cloud store, process and further transmit the processed sensory data to users in an on demand way. Compared with SC without trust assistance (SCWTA), extensive simulation results demonstrate that TASC can greatly improve the throughput and response time for users to obtain sensory data from the cloud.

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The main contributions of this paper are summarized as follows. • This paper is the first that incorporates trust into both WSN and CC to improve the QoS of a SC. This clearly demonstrates the novelty of our work and its scientific impact on current SC schemes. • This paper further proposes TASC, which is trust-assisted SC that utilizes trusted sensors to collect and transmit sensory data as well as adopts trusted cloud data centers to store and process the received sensory data. For the rest of this paper, Section II introduces the related work about SC. The system model is presented in Section III. Section IV depicts our proposed TASC. The evaluation about TASC is performed in Section V and Section VI concludes this paper.

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In this section, we review the current related work about SC. They emphasize the following three aspects: 1) improving the performance of WSN with cloud; 2) effectively integrate WSN and cloud; 3) better utilizing the sensory data of WSN with cloud. A. Improving the performance of WSN with cloud About 1) improving the performance of WSN with cloud, a collaborative computing framework which integrates cloud and wireless body sensor networks is proposed in [7], reducing 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. In addition, to effectively configure body sensor networks in an adaptive and stable manner by seeking the trade-offs among conflicting objectives (e.g., resource consumption and data yield), a cloud-integrated architecture named in Body-in-the-Cloud (BitC) is presented in [8]. For sharing the network resources among any two multimedia sensor nodes, a channel characterization scheme is shown in [9], combining a cross-layer admission control in dynamic cloud-based multimedia sensor networks. B. Effectively integrate WSN and cloud With respect to 2) effectively integrate WSN and cloud, the focus of [10] is to propose a sensory data processing framework for SC so that desirable sensory data can be transmitted


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to the mobile users in a fast, reliable, and secure manner. For prolonging the lifespan of an integrated SC, collaborative location-based sleep scheduling schemes are proposed in [11] for prolonging the lifetime of the WSN integrated with cloud, while still satisfying the data requests of mobile users. Helping cloud service users choose desirable cloud service providers and assisting cloud service providers in selecting appropriate sensor network providers during WSN and cloud integration, the authenticated trust and reputation calculation and management system designed in [12] is to authenticate cloud service providers and sensor network providers to avoid malicious impersonation attacks, as well as calculate and manage the trust and reputation regarding the service of cloud service providers and sensor network providers. Towards offering more useful data reliably to mobile cloud from WSN, a WSN and cloud integration scheme named TPSS is presented in [13], which consists of TPSDT (Time and Priority based Selective Data Transmission) and PSS (Priority-based Sleep Scheduling). To achieve shorter expected completion time of cloud tasks related to WSN during WSN and cloud integration, two job scheduling algorithms, namely priority-based two phase Min-Min (PTMM) and priority-based two phase MaxMin (PTAM), are proposed for CC integrated with WSN in [14]. In both PTMM and PTAM, cloud tasks related to WSN are executed first in phase 1, followed by the execution of other ordinary cloud tasks in phase 2. C. Better utilizing the sensory data of WSN with cloud Regarding 3) better utilizing the sensory data of WSN with cloud, a cloud-based platform (i.e., Wiki-Health) is introduced in [15] for storing, tagging, retrieving, analyzing, comparing and searching health sensor data. Similarly, the motivation of [16] is to design and develop a virtualized middleware platform based on cloud, for allowing the collection, management and integration of information generated by multiple sensors and WSNs, as well as the management of business process procedures that are supported by the sensors’ infrastructure. Moreover, considering the scenario that the cloud utilizes the sensory data to make real time alert in critical situations, an event matching algorithm based on subscriber category is

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proposed in [17], for distributing the sensor data to appropriate subscriber. To the best of our knowledge, currently there is no research work that applies trust into both WSN and CC to improve the QoS for users to obtain sensory data from the cloud in SC. Our proposed TASC is the first work that aims to improve the QoS of SC, with trust assistance in both WSN and CC. III. S YSTEM MODEL Our system model is presented as follows with the following assumptions. • There is a WSN with N normal sensor nodes (i.e., W SN = i1 , i2 , i3 , . . . , iN ). In addition, there is one source node (i.e., sr) and one sink node (i.e., si) in the WSN. sr transmits data to si. The data rate is h kbps. • There is a cloud with M data centers (i.e., C = j1 , j2 , j3 , . . . , jM ). • W SN transmits data to the cloud C. K Users (i.e., U sers = u1 , u2 , u3 , . . . , uK ) require the sensory data from C. • Time is divided into W time epochs (i.e., T ime = t1 , t2 , t3 , . . . , tW ). Each sensor node i owns a trust value vst (i) in each time epoch t. In addition, each data center j has a trust value vdt (j) for each time epoch t. IV. TASC In this section, we first present some preliminary information about trust [12] [18] [19], followed with the discussion of TASC. A. Preliminaries about trust Referring to Merriam Webster’s Dictionary, trust is defined as “assured reliance on the character, ability, strength or truth of someone or something”. Actually, trust is a concept with various definitions and evaluations in different fields (e.g., psychology, sociology, economics, philosophy, wireless networks) [12] [18]. For instance, in the context of wireless communications, “Trust of a node A in a node B is the subjective expectation of node A receiving positive outcomes from the interaction with node B in a specific context”.


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In general, for evaluating the trust from one entity (i.e., trustor) to another entity (i.e., trustee), trustor needs to gather evidences (e.g., honest, selfish, malicious behaviors) which could reflect the satisfaction about trustee, either with direct interactions or information provided by third-parties [12] [19]. Particularly, direct trust means the trustworthiness, achieved by mapping evidences coming from direct interactions, while indirect trust refers to the trustworthiness, obtained by mapping evidences coming from third-parties. In addition, recent trust reflects only the recent behaviors, while historical trust is built from the past experiences and thus reflects long-term behavioral pattern. With these evidences, trustor maps the gathered information from the evidence space to the trust space, by a predefined mapping function and an aggregation function to obtain the trustworthiness value of the trustee [12] [18]. For example, one way to calculate trustworthiness is the beta distribution shown as follows. Assume that s and f represent the collective amount of positive and negative feedbacks in the evidence space about the trustee, then the trustworthiness v about the s+1 trustee is computed as v = f +s+2 .

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B. Overview of TASC An example of TASC is presented in Fig. 2. In TASC, trusted sensors (i.e., sensors with trust values exceeding a threshold) collect and transmit sensory data to the cloud. Then the cloud selects the trusted data centers (i.e., data centers with trust values exceeding a threshold) to store, process the sensory data and further transmit the processed sensory data to users on demand. Particularly, TASC consists of 1) trust-assisted WSN and 2) trust-assisted cloud.

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In trust-assisted WSN, initially, every sensor i is with the same trust value vso . After a certain period (e.g., in the tth time epoch), the trust value vst (i) of each sensor should be changed, since the behavioral pattern (i.e., collection and transmission history) of each sensor is different. Namely, the sensors with less negative behavioral patterns should be with higher trust values, while those sensors with more negative

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behavioral patterns should be with lower trust values. In addition, as shown in Fig. 3 about the states of trustassisted WSN, whether a sensor is trusted (i.e., the trust value of the sensor exceeds a threshold) or not is dynamically changing with time. In other words, previously non-trusted sensors can become trusted, as shown from Fig. 3 (c) to Fig. 3 (d). When sensors collect and transmit data, only trusted sensors are used to collect and transmit sensory data to the cloud. In this paper, as historical trust reflects the long-term behavioral pattern, we take historical trust as the trust value vst (i) of the sensor node i. One way to calculate vst (i) is illustrated as follows [19]. Specifically, let vstn (p, q) represent the historical trust that agent p has about agent q up to n transactions in the tth time epoch. Moreover, ρ(0 ≤ ρ ≤ 1) is the forgetting factor (discounting older experiences) and vs00 (p, q) = 0. Then, vstn (p, q) =

ρ × vstn−1 (p, q) + Satstn−1 (p, q) 2

(1)

Where Satstn (p, q) represents the amount of satisfaction agent p has upon agent q based on its service up to n transactions in the tth time epoch. The satisfaction update function is defined as follows: Satstn (p, q) = α × Satscur + (1 − α) × Satstn−1 (p, q) (2) α is a weight. Satst0 (p, q) = Satst−1 last (p, q). In other words, the value of satisfaction at the start of tth time epoch is equal to the last computed satisfaction in the (t−1)th time epoch and the very initial value of satisfaction is Sats00 (p, q) = 0. Here, Satscur represents the satisfaction value for the most recent transaction. And a feedback based system is used, where an agent rates the service quality of other agent according to the following function:

Satscur

  0, if transaction is f ully unsatisf actory; = 1, if transaction is f ully satisf actory;   ∈ (0, 1), otherwise. (3)

D. Trust-assisted cloud In trust-assisted cloud, every data center j is with the same trust value vdo initially. After several time epochs (e.g., in the tth time epoch), the trust value vdt (j) of each data center should be changed, due to that the behavioral pattern (storing and processing history) of each data center is various. The data centers with less negative behavioral patterns should be treated with higher trust values, while those data centers with more negative behavioral patterns should be assigned with lower trust values. Moreover, as presented in Fig. 4 with respect to the states of trust-assisted cloud, whether a data center is trusted (i.e., the trust value of the data center exceeds a threshold) or not is also changing with time. Namely, there could be multiple (e.g., two

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trusted data centers) as shown in Fig. 4 (d). In the case of using data centers to store and process data, trusted data centers are utilized to store, process the sensory data and further transmit the processed sensory data to users on demand. Here, we also use the historical trust reflecting the longterm behavioral pattern of the data center as the trust value of vdt (j). The following shows one way to calculate vdt (j) [19]. Particularly, let vdtn (p, q) refer to the historical trust that agent p has about agent q up to n transactions in the tth time epoch. ̺(0 ≤ ̺ ≤ 1) is the forgetting factor (discounting older experiences) and vd00 (p, q) = 0. vdtn (p, q) =

̺ × vdtn−1 (p, q) + Satdtn−1 (p, q) 2

(4)

In the above, Satdtn (p, q) represents the amount of satisfaction agent p has upon agent q based on its service up to n transactions in the tth time epoch. And the satisfaction update function is shown as follows: Satdtn (p, q) = β × Satdcur + (1 − β) × Satdtn−1 (p, q) (5) Similar with Satstn (p, q), β is also a weight and 0 Satdt0 (p, q) = Satdt−1 last (p, q) as well as Satd0 (p, q) = 0. Satdcur represents the satisfaction value for the most recent transaction and the feedback based system used are the same as illustrated in Section IV-C, for an agent to rate the service quality of another agent. E. Analysis of TASC and SCWTA With respect to TASC, trusted sensor nodes with trust values exceeding a certain threshold are utilized to collect and transmit sensory data. In addition, trusted data centers with trust values exceeding a certain threshold are utilized to store and process the received sensory data. Thus, data will have higher probabilities that they can be transmitted from the WSN source node to the user and the response time for sensory data to be obtained from the WSN source node to the user is also low. About SCWTA, sensor nodes and data centers are utilized to perform their tasks without considering the trust values. Thus, some sensor nodes with very low trust values may be selected to collect and transmit sensor data and some data centers with very low trust values in cloud may be chosen to store and process the received sensory data. As a consequence, the probability that the sensory data are successfully transmitted from the source node in the WSN to the user is low and the corresponding response time is high. V. E VALUATION To judge the effectiveness of trust assistance in SC in terms of improving QoS, TASC and SCWTA are evaluated and we choose the throughput and response time as the evaluation metrics. The detailed simulation is performed in NetTopo [20] and shown as follows.


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A. Evaluation setup There is one WSN and one cloud and 10 users. The WSN is consisted of 100 normal sensor nodes, one source node and one sink node. And the data rate is 1000 kbps. The cloud is consisted of 10 data centers. The WSN transmits data to the cloud and each user requests the sensory information from the cloud on demand. Each time epoch is assumed to be 1 s. Generally, the trust values for the sensor nodes and data centers are initialized randomly between 0 and 1 in SCWTA, while the sensor nodes and data centers in TASC are with trust values exceeding a threshold. In addition, we assume that 10 sensors nodes are utilized to collect and transmit sensory data and 1 data center is utilized to store and process the

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sensory data, in both SCWTA and TASC. And the evaluation is performed in the following two scenarios. •

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Scenario 1: 100 simulations with different topologies are performed to analyze the throughput and response time of SCWTA and TASC. For TASC, the trust value threshold of both sensor node and data center is set to be 0.5. For SCWTA, the trust value of each sensor node and data center is always random between 0 and 1. Scenario 2: For a specific topology, the trust value threshold of sensor node and data center are changed in TASC 9 times (from 0.1 to 0.9), while the trust value of each sensor node and data center are still always random between 0 and 1 in SCWTA. For each time, the trust


value threshold is increased by 0.1, to analyze the effect of trust value threshold on throughput and response time. B. Evaluation results The evaluation results with respect to the throughput and response time in different topologies about SCWTA and TASC in Scenario 1 are shown in Fig. 5(a) and Fig. 5(b), respectively. Specifically, from these two figures, we can obviously observe that the throughput of TASC is almost always much higher than that of SCWTA, while the response time of TASC is nearly always greatly lower than that of SCWTA. In addition, the evaluation results in terms of the throughput and response time about SCWTA and TASC with various trust value thresholds in Scenario 2 are presented in Fig. 6(a) and Fig. 6(b), respectively. Based on these two pictures, we can achieve that for different trust value thresholds, the throughput of TASC is still higher than that of SCWTA and the response time of TASC is still lower than that of SCWTA. Moreover, the throughput of TASC can be increased by increasing the trust value threshold and the response time of TASC is nearly stable. In contrast, the throughput of SCWTA is not changed and the response time of SCWTA is irregular. VI. C ONCLUSION In this paper, focusing on the improving the QoS (e.g., throughput, response time) of SC, we have proposed the concept of TASC. In TASC, the sensory data are collected and transmitted by trusted sensors (i.e., sensors with trust values exceeding a threshold) to the cloud, followed with the storage and processing of these sensory data with the trusted data centers (i.e., data centers with trust values exceeding a threshold) in the cloud. Users then have access to these sensory data transmitted by the cloud on demand. Further evaluation results about TASC have also been presented to show the effectiveness of TASC with respect to enhancing the throughput and response time, in contrast with SCWTA. ACKNOWLEDGEMENT This work was partially supported by a Four Year Doctoral Fellowship from The University of British Columbia and funding from the Natural Sciences and Engineering Research Council of Canada under Grant CRDPJ 434659-12, the ICICS/TELUS People & Planet Friendly Home Initiative at The University of British Columbia, TELUS and other industry partners. The work of L. Shu was supported by Project from Educational Commission of Guangdong Province of China under Grant 2013KJCX0131, 2013 Special Fund of Guangdong Higher School Talent Recruitment and the National Natural Science Foundation of China under Grant 61401107. The work of J. Rodrigues has been supported by the Instituto de Telecomunicaço˜ es, Next Generation Networks and Applications Group (NetGNA), by National Funding from the FCTFundaça˜ o para a Ciência e Tecnologia in the scope of R&D Unit 50008, financed by the applicable financial framework (FCT/MEC through national funds and when applicable cofunded by FEDER - PT2020 partnership agreement).

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