IEEE COMMUNICATIONS MAGAZINE, VOL. XX, NO. XX, AUGUST 2016
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Multi-Method Data Delivery for Sensor-Cloud Users Chunsheng Zhu, Victor C. M. Leung, Fellow, IEEE, Kun Wang, Member, IEEE, Yan Zhang, Senior Member, IEEE, and Laurence T. Yang, Senior Member, IEEE
Abstract—Delivering sensory data to users anytime and anywhere if there is network connection, Sensor-Cloud (SC) which integrates wireless sensor networks (WSNs) and cloud computing (CC) is attracting growing interest from both academia and industry. This article discusses the potential applications and recent work about SC and observes two issues regarding data delivery to SC users. Further, motivated by solving these two issues, this article proposes a Multi-Method Data Delivery (MMDD) scheme for SC users. MMDD strategically incorporates four kinds of delivery: 1) delivery from cloud to SC users; 2) delivery from WSN to SC users; 3) delivery from SC users to SC users; 4) delivery from cloudlet to SC users. Compared with exclusive data delivery (EDD) from cloud to SC users, evaluation results show that MMDD could achieve lower delivery cost or less delivery time for SC users.
I. I NTRODUCTION
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ECENTLY, motivated by incorporating the ubiquitous data gathering ability of wireless sensor networks (WSNs) as well as the powerful data storage and data processing capabilities of cloud computing (CC), Sensor-Cloud (SC) [1] is receiving growing attention from both academic and industrial communities. Basically, integrating WSNs and CC, as shown in Fig. 1, the sensory data is gathered by the ubiquitous sensor nodes (e.g., temperature sensor nodes, humidity sensor nodes, pressure sensor nodes, etc.) in WSN and transmitted to the powerful data centers in cloud. Then the sensory data is stored and processed by the cloud and further delivered to SC users on demand. With such integration, 1) from the perspective of users, SC makes them obtain their required sensory data anytime and anywhere if there is network connection. 2) From the view of WSN and cloud, SC complements them. For example, about cloud, the service cloud offers can be greatly enriched, by providing the services (e.g., environmental monitoring, healthcare monitoring, landslide detection, forest fire detection, etc.) that WSN provides. Regarding WSN, the utility of WSN could be enhanced, by being able to serve multiple applications via 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, the ICICS/TELUS People & Planet Friendly Home Initiative at The University of British Columbia, TELUS and other industry partners. C. Zhu and V. C. M. Leung are with the Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada (e-mail: {cszhu, vleung}@ece.ubc.ca). K. Wang is with the School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210003, China (e-mail:
[email protected]). Y. Zhang is with the Simula Research Laboratory, 1364 Fornebu, Norway, and also with the Department of Informatics, The Faculty of Mathematics and Natural Sciences, University of Oslo, 0373 Oslo, Norway (e-mail:
[email protected]). L. T. Yang is with the Department of Computer Science, St. Francis Xavier University, Antigonish, NS B2G 2W5, Canada (e-mail:
[email protected]).
cloud. Generally, in such integration, there are three main entities (i.e., sensor network provider (SNP) which enables the WSN, cloud service provider (CSP) which offers the cloud, SC user). Discussing the potential applications and recent work regarding SC, this article observes two issues concerning data delivery to SC users. Then, triggered by solving these two issues, this article proposes a Multi-Method Data Delivery (MMDD) mechanism for SC users. Particularly, MMDD strategically combines four kinds of delivery: 1) delivery from cloud to SC users; 2) delivery from WSN to SC users; 3) delivery from SC users to SC users; 4) delivery from cloudlet to SC users. In contrast to exclusive data delivery (EDD) from cloud to SC users, evaluation results present that MMDD could obtain lower delivery lost or less delivery time for SC users. For the rest part of this article, the potential applications of SC are presented in Section II. Section III reviews the recent work about SC and presents the two issues regarding data delivery to SC users. The proposed MMDD scheme is introduced in Section IV. Section V performs the evaluation with respect to MMDD and EDD. This article is concluded in Section VI.
II. P OTENTIAL APPLICATIONS OF SC SC owns a lot of exciting potential applications [2]. For instance, concerning real-time agriculture monitoring, WSNs comprised of a variety of sensor nodes (e.g., soil moisture sensor nodes, air sensor nodes, temperature sensor nodes, CO2 concentration sensor nodes and camera sensor nodes, etc.) can be arranged to collect various information about the crops in the farm. These data can be further analyzed by the cloud real-timely in order to track the health of the crops. With respect to real-time transportation monitoring, WSNs that include different kinds of sensor nodes (e.g., pressure sensor nodes, image sensor nodes, video sensor nodes, alcohol gas sensor nodes, etc.) can be used for gathering the vehicle and driver information. After the cloud real-timely incorporates the collected information, the level of fuel and the vehicle arrival time as well as the status of driver can be tracked, predicted, and observed, respectively. Regarding real-time tunnel monitoring, WSNs including light sensor nodes can be utilized to sense the light levels inside the tunnel. Meanwhile, the cloud can analyze the sensed light levels in real-time, so that the light intensity can be automatically adjusted to save the energy spent unnecessarily for lightening throughout the day. About real-time wildlife monitoring, WSNs consisting of various types of sensor nodes (e.g., video sensor nodes) can be deployed into a wide filed, collecting the information about
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the wildlife sanctuaries and activities, etc. With the powerful cloud which stores and processes the gathered information, real-timely monitoring and further protecting the wildlife (e.g., the endangered species) can be achieved. III. R ECENT WORK ABOUT SC Focusing on cloud-based WSN, the aim of [3] is enhancing the lifetime of the WSN integrated with cloud. Specifically, two CLSS (collaborative location-based sleep scheduling) approaches are introduced for WSNs. The strategy is dynamically determining the awake or asleep state of each sensor node to decrease energy consumption of the integrated WSN, considering the locations of mobile cloud users. With respect to WSN-based cloud, the purpose of [4] is reducing the expected completion time for the CC integrated with WSN. Particularly, two job scheduling schemes (i.e., PTMM (priority-based two phase Min-Min) and PTAM (priority-based two phase Max-Min)) are described for CC. The technique is executing WSN related cloud tasks in phase 1, while executing other ordinary cloud tasks in phase 2. Concerning SC integration, a sensory data processing framework is shown in [5], aiming at transmitting desirable sensory data to the mobile cloud users in a fast, reliable, and secure manner. The mechanism is incorporating the WSN gateway, the cloud gateway, the cloud and the mobile users to perform various functions (e.g., data traffic monitoring, data filtering, data prediction, data recommendation, data compression, data decompression, data security). Another sensory data delivery scheme is presented in [6], towards offering more useful data reliably to mobile cloud from WSN. The idea is making the WSN gateway selectively transmit the sensory data to the cloud based on the time and priority features of the data requested by the mobile user, while utilizing the prioritybased sleep scheduling to save the energy consumption of the WSN. An authenticated trust and reputation calculation and management system is exhibited in [7], targeting at helping CSU choose desirable CSP and assisting CSP in selecting appropriate SNP. The scheme is incorporating the authenticity of CSP and SNP; the attribute requirement of cloud service user (CSU) and CSP; the cost, trust, and reputation of the service of CSP and SNP. A trust-assisted SC is designed in
[8], devoting effort to improve the quality of service (QoS) that the sensory data is achieved by SC users. The method is utilizing the trusted sensors (i.e., sensors with trust values surpassing a threshold) in WSN for gathering and transmitting the sensory data, while using the trusted data centers (i.e., data centers with trust values surpassing a threshold) in cloud for storing, processing and delivering the sensory data to users. Five pricing models are devised in [9], induced by offering a guidance for future research regarding SC pricing. The pricing designs consider the following factors: the lease period of the SC user; the required working time of SC; the SC resources utilized by the SC user; the volume of sensory data obtained by the SC user; the SC path that transmits the sensory data from the WSN to the SC user, respectively. To the best of our knowledge, the sensory data delivery of SC in all the above work is exclusive data delivery (EDD) from cloud to SC users. With such delivery, the following two issues could exist.
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Issue 1: Since SC users might request the same data from the cloud, cloud might deliver large amounts of same data to SC users. With a large number of repeated data transmissions from cloud to SC users exclusively, it increases the demand regarding the energy and resources as well as bandwidth of SC. Issue 2: When multiple SC users request the data from the cloud simultaneously, a lot of data needs to be delivered from the cloud to multiple SC users at the same time. With substantial data delivery from cloud to multiple SC users exclusively, it also increases the requirement with respect to the energy and resources as well as bandwidth of SC.
In both of the above two cases, we can observe that the delivery cost (e.g., utilized energy and resources as well as bandwidth) for providing data to SC users will be increased. Furthermore, in terms of a SC with certain energy and resources as well as bandwidth, the delivery time for offering data to SC users will also be increased.
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IV. P ROPOSED MMDD SCHEME A. Overview Motivated by solving the above observed two issues, the Multi-Method Data Delivery (MMDD) scheme is proposed. Particularly, as shown in Fig. 2, the following four methods are incorporated by MMDD for delivering the sensory data to SC users: 1) MMDD1 (delivery from cloud to SC users); 2) MMDD2 (delivery from WSN to SC users); 3) MMDD3 (delivery from SC users to SC users); 4) MMDD4 (delivery from cloudlet to SC users). Regarding 1) MMDD1 (delivery from cloud to SC users), the sensory data is delivered via the network communication (e.g., WCDMA, LTE, WiMax). About 2) MMDD2 (delivery from WSN to SC users), the sensory data is delivered via the base station communication. With respect to 3) MMDD3 (delivery from SC users to SC users), the sensory data is delivered via the device to device communication. For 4) MMDD4 (delivery from cloudlet to SC users), the sensory data is delivered via the local area network communication. Here, available for use by nearby mobile devices, a cloudlet [10] is a resource-rich and trusted computer or cluster of computers well-connected to the Internet. B. Delivery rules The delivery rules consider the following elements. 1) The delivery methods which are available for the SC user might be different, when the SC user is in various locations. 2) The sensory data requested by the SC user needs to be delivered, with the delivery method (s). 3) For each delivery method in MMDD, there is an associated delivery cost and delivery time. Particularly, regarding different delivery methods in MMDD,
the delivery cost and delivery time probably are various in different conditions. In terms of a certain delivery method in MMDD, the delivery cost and delivery time probably are also various in different situations. Thus, the appropriate delivery method (s) in different conditions need (s) to be used, to better satisfy the SC user’s requirement regarding delivery cost or delivery time. Specifically, the rules to utilize the delivery method (s) are shown as follows. Rule 1): based on the location of the SC user, the delivery method (s) which is (are) available to the SC user is (are) determined by the SC. Rule 2): taking into account the data required by the SC user, the delivery method (s) which can deliver the needed sensory data is (are) decided by the SC. Rule 3): considering the delivery cost and delivery time of the delivery method (s), the specific delivery method (s) is (are) utilized by the SC, based on the service level agreement of the SC user. Regarding Rule 1), the locations of SC users are obtained by SC, with a mobile application which dynamically uploads the location of the SC user to SC [11]. About Rule 2), if the sensory data required by the SC user needs to be delivered by multiple delivery methods, multiple delivery methods become the candidates. Otherwise, only one delivery method is used. With respect to Rule 3), if the SC user wants to obtain the data with minimum delivery time, then the delivery method (s) with the minimum delivery time is (are) utilized. If the SC user wants to achieve the data with a minimum delivery cost, then the delivery method (s) with the minimum delivery cost is (are) used. Otherwise, the delivery method (s) with a satisfactory delivery time (i.e., the delivery time is less than a threshold) and a minimum delivery cost is (are) utilized, or the delivery method (s) with a satisfactory delivery cost (i.e., the
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delivery cost is less than a threshold) and a minimum delivery time is (are) used. C. Delivery and application analysis The purpose of MMDD is to better cater for the SC user’s delivery requirement, since the delivery cost or delivery time with EDD might not satisfy the SC user’s delivery requirement in some cases. For example, for Issue 1 and Issue 2 discussed in Section III, in the case that SC users request the same data from the cloud or multiple SC users request the data from the cloud simultaneously, the data could be offered to the SC users by intelligently utilizing different delivery method (s) in MMDD, instead of EDD from cloud to SC users. Since different delivery methods in MMDD own various delivery cost and delivery time in different conditions, the delivery cost for providing data to SC users with MMDD might be lower than that with EDD. Similarly, the delivery time for offering data to SC users with MMDD might be less than that with EDD. In other words, the aim of MMDD is to strategically incorporate the four delivery methods (i.e., MMDD1, MMDD2, MMDD3, and MMDD4) to generate lower delivery cost or less delivery time for the SC user, while offering the needed data to the SC user. As a result, different MMDD (s) might be utilized for delivering the sensory data to the SC user in different conditions. For instance, MMDD4 might be used when the SC user is in a coffee shop in which a cloudlet could offer the sensory data, while MMDD3 probably could be used when the SC user is in a classroom where other SC users could provide the sensory data. MMDD2 might be utilized when the SC user is very close to the WSN offering the sensory data, while MMDD1 probably could be utilized when the SC user is very far from home and the cloud could provide the sensory data. Moreover, multiple delivery methods in MMDD
could be used, if one delivery method (e.g., MMDD4) is not sufficient to offer all the sensory data that the SC user requests. Furthermore, regarding MMDD, it could also be utilized in the future Social-Sensor-Cloud (SSC) [12]. As shown in Fig. 3 about SSC, the SC users form social networks (SNs) [13], which connect and complement the WSNs, cloud and cloudlet. In particular, with SNs, the resources and services of the WSNs, cloud and cloudlet could be shared leveraging the relationships established between members of a SN. In such a manner, the resources and services requested by the SC users could be substantially reduced. Then the delivery cost and delivery time with MMDD might be further decreased. With further decreased delivery cost or delivery time, the SC user’s delivery requirement could be further better satisfied. V. E VALUATION A. Evaluation setup To evaluate the delivery time and delivery cost of EDD and MMDD for SC, the following two evaluations are performed as case studies. In both evaluations, there is a SC consisting of a WSN, a cloud, a cloudlet and a number of SC users. In terms of EDD, the sensory data is delivered to the SC users from the cloud exclusively. For MMDD, the sensory data is delivered to the SC users cooperatively with MMDD1, MMDD2, MMDD3 and MMDD4. For evaluation 1, it is assumed that the delivery time to a SC user with MMDD1, MMDD2, MMDD3 and MMDD4 are 0.4 seconds, 0.3 seconds, 0.2 seconds and 0.1 seconds, respectively. The delivery cost to a SC user with MMDD1, MMDD2, MMDD3 and MMDD4 are 0.8 $, 0.6 $, 0.4 $, 0.2 $, respectively. Evaluation 1 is conducted in the two scenarios below. • Scenario 1: MMDD1, MMDD2, MMDD3, and MMDD4 are used to serve 25% SC users, respectively. The num-
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ber of SC users ranges from 100 to 1000 (every time increased by 100). This scenario is for evaluating the impacts of the number of SC users on SC’s delivery time and SC’s delivery cost, with EDD and MMDD. • Scenario 2: There are 500 SC users. The percentage of SC users MMDD1 serves, ranges from 10% to 90% (every time increased by 10%). MMDD2, MMDD3, and MMDD4 are utilized to serve the remaining users, equally. This scenario is to evaluate the impacts of the utilization rate of MMDD1 on SC’s delivery time and SC’s delivery cost, with EDD and MMDD. Concerning evaluation 2, there are 500 SC users, served by MMDD1, MMDD2, MMDD3 and MMDD4 equally. Moreover, it is given that the delivery time to a SC user with MMDD3 and MMDD4 are 0.4 seconds and 0.2 seconds, respectively. The delivery cost to a SC user with MMDD3 and MMDD4 are 0.8 $ and 0.4 $, respectively. Evaluation 2 is implemented in the following two scenarios. • Scenario 3: The delivery time and delivery cost to a SC user with MMDD1 are 0.8 seconds and 1.6 $, respectively. The delivery time to a SC user with MMDD2 ranges from 0.6 seconds to 1.4 seconds (every time increased by 0.1 seconds). The delivery cost to a SC user with MMDD2 ranges from 1.2 $ to 2.0 $ (every time increased by 0.1 $). This scenario is for evaluating the impact of MMDD2’s delivery time on SC’s delivery time and the impact of MMDD2’s delivery cost on SC’s delivery cost, with EDD and MMDD. • Scenario 4: The delivery time and delivery cost to a SC user with MMDD2 are 0.6 seconds and 1.2 $, respectively. The delivery time to a SC user with MMDD1 ranges from 0.8 seconds to 1.6 seconds (every time increased by 0.1 seconds). The delivery cost to a SC user with MMDD1 ranges from 1.6 $ to 2.4 $ (every time increased by 0.1 $). This scenario is to evaluate the impact of MMDD1’s delivery time on SC’s delivery time and the impact of MMDD1’s delivery cost on SC’s delivery cost, with EDD and MMDD. B. Evaluation results Fig. 4 and Fig. 5 present the evaluation 1 results and evaluation 2 results about the delivery time and delivery cost with EDD and MMDD for SC in the two different scenarios, respectively. As shown in these figures, it can be clearly obtained that the delivery time or delivery cost of MMDD is better than that of EDD, in terms of the above case studies. Particularly, from Fig. 4(a), Fig. 4(c), Fig. 5(a) and Fig. 5(c), it can be observed that the SC’s delivery time with MMDD are always less than that with EDD in Scenario 1, Scenario 2, Scenario 3 and Scenario 4. In addition, based on Fig. 4(b), Fig. 4(d), Fig. 5(b) and Fig. 5(d), it can be achieved that the SC’s delivery cost with MMDD are also always lower than that with EDD in Scenario 1, Scenario 2, Scenario 3 and Scenario 4. VI. C ONCLUSION Attracting growing interest from both academic and industrial communities by integrating WSNs and CC, SC delivers
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sensory data to users anytime and anywhere if there is network connection. In this article, the potential applications and recent work with respect to SC have been discussed and two research issues about data delivery to SC users have been identified. Further, the MMDD scheme has been proposed, induced by solving the observed two research issues. Specifically, the following four methods that deliver sensory data to SC users are strategically incorporated in MMDD: 1) delivery from cloud to SC users; 2) delivery from WSN to SC users; 3) delivery from SC users to SC users; 4) delivery from cloudlet to SC users. Evaluation results has also been presented about MMDD and EDD, demonstrating that MMDD could achieve lower delivery cost or less delivery time for SC users. R EFERENCES [1] C. Zhu, X. Li, H. Ji, and V. C. M. Leung, “Towards integration of wireless sensor networks and cloud computing,” in Proc. 7th IEEE Int. Conf. Cloud Comput. Technol. Sci., 2015, pp. 491–494. [2] 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,” Int. J. Distrib. Sensor Netw., vol. 2013, pp. 1–18, 2013. [3] C. Zhu, V. C. M. Leung, L. T. Yang, and L. Shu, “Collaborative locationbased sleep scheduling for wireless sensor networks integrated with mobile cloud computing,” IEEE Trans. Comput., vol. 64, no. 7, pp. 1844–1856, Jul. 2015. [4] 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. 6th IEEE Int. Conf. Cloud Comput. Technol. Sci., 2014, pp. 62–69. [5] 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 Syst. J., vol. PP, no. 99, pp. 1–12, Jan. 2014. [6] 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 Trans. Emerg. Topics Comput., vol. 3, no. 1, pp. 84–94, Mar. 2015. [7] 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 Trans. Inf. Forensics Security, vol. 10, no. 1, pp. 118–131, Jan. 2015. [8] 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. IEEE Conf. Comput. Commun. Workshops, 2015, pp. 342–347. [9] C. Zhu, V. C. M. Leung, E. C.-H. Ngai, L. T. Yang, L. Shu, and X. Li, “Pricing models for sensor-cloud,” in Proc. 7th IEEE Int. Conf. Cloud Comput. Technol. Sci., 2015, pp. 454–457. [10] M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, “The case for vm-based cloudlets in mobile computing,” IEEE Pervasive Computing, vol. 8, no. 4, pp. 14–23, Oct.-Dec. 2009. [11] G. Ananthanarayanan, M. Haridasan, I. Mohomed, D. Terry, and C. A. Thekkath, “Startrack: a framework for enabling track-based applications,” in Proc. 7th Int. Conf. Mob. Syst., Appl., Serv., 2009, pp. 207–220. [12] C. Zhu, V. C. M. Leung, L. Shu, and E. C.-H. Ngai, “Green internet of things for smart world,” IEEE Access, vol. 3, pp. 2151–2162, Nov. 2015. [13] Y. Jiang and J. C. Jiang, “Understanding social networks from a multiagent perspective,” IEEE Trans. Parallel Distrib. Syst., vol. 25, no. 10, pp. 2743–2759, Oct. 2014.
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