Sensor Cloud Computing for Vehicular Applications: from Analysis to Practical Implementation Zhengguo Sheng
∗
Xiping Hu
Peyman TalebiFard
University of British Columbia Vancouver, Canada
University of British Columbia Vancouver, Canada
University of British Columbia Vancouver, Canada
University of British Columbia Vancouver, Canada
Beijing Jiaotong University Beijing, China
Sichuan University Chengdu, China
[email protected] Victor C.M. Leung
[email protected]
[email protected] Ruifeng Chen
[email protected]
ABSTRACT
[email protected]
tructures and services for running mobile applications. The key idea of cloud computing is to create a pool of visualized, dynamically configurable and manageable resources across computing networks, which can deliver on demand services to users over the Internet. In a simple, topological sense, a cloud computing solution is made up of several components, such as clients, data center and distributed servers. Clients can subscribe services via the cloud computing platform which can offer diverse storage and computation capabilities from both data center and distributed servers, respectively. Today, with the development of wireless technologies and powerful computing hardware, the cloud computing capability has been largely extended to a broad range of MWSN, such as wireless sensor networks [3, 4] and Internet-of-Things (IoT) [5], to support flexible mobile applications. In MWSN, sensor devices are commonly with a radio transceiver and a microcontroller powered by a battery, as well as diverse sensors for detecting light, heat, humidity, temperature, etc. Examples of mobile sensor devices include most current mobile phones (such as iPhone, Samsung’s Android phones, etc.) which are equipped with a rich set of embedded sensors such as camera, GPS, WiFi/3G/ 4G radios, accelerometer, digital compass, gyroscope, microphone and so on. Moreover, the recent developed sensor platforms, such as WRTnode1 and Arduino2 , are also capable of connecting external sensors (such camera sensor, thermal sensor, heartbeat sensor, air pollution sensor, etc.) to enable attractive mobile sensing services in various domains such as environmental monitoring, social networking, healthcare and transportation, etc. Different to the static sensor networks, MWSN are much more versatile as they can be deployed in any scenario and cope with rapid topology changes. The advantage of allowing the sensor devices to be mobile increases the number of applications beyond those for which static WSNs are used. This particularly promotes the development of intelligent transportation systems (ITS) for reducing the traffic congestions, the high number of traffic road accidents, etc. Indeed ITS can support a large number of applications including safety traffic applications (e.g., collision avoidance, road obstacle warning, safety message disseminations, etc.), traffic information and infotainment services (e.g., games, multimedia streaming, etc.). For example, a car attached with a mobile sensing device can actively collect on-board diagnostics (OBD) information and nearby messages via vehicle-to-roadside (V2R) and vehicle-to-vehicle (V2V) communications, and inform drivers and
Advances in sensor cloud computing to support vehicular applications are becoming more important as the need to better utilize computation and communication resources and make them energy efficient. In this paper, we propose a novel approach to minimize energy consumption of processing a vehicular application within mobile wireless sensor networks (MWSN) while satisfying a certain completion time requirement. Specifically, the application can be optimally partitioned, offloaded and executed with helps of peer sensor devices, e.g., a smart phone, thus the proposed solution can be treated as a joint optimization of computing and networking resources. Our theoretical analysis is supplemented by simulation results to show the significance of energy saving by 63% compared to the traditional cloud computing methods. Moreover, a prototype cloud system has been developing to validate the efficiency of sensor cloud strategies in dealing with diverse vehicular applications.
Categories and Subject Descriptors G.1 [Mathematics of Computing]: NUMERICAL ANALYSIS; C.2 [COMPUTER-COMMUNICATION NETWORKS]: Network Architecture and Design
Keywords Mobile wireless sensor networks; cloud computing; vehicular applications
1.
[email protected] Yingjie Zhou
INTRODUCTION
Cloud computing [1, 2] has been proposed as an efficient and cost effective way of providing highly scalable and reliable infras∗This work was supported in part by the Canadian Natural Sciences and Engineering Research (NSERC), the NSERC DIVA Strategic Research Network, and various industry partners. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from
[email protected]. DIVANet’14, September 21–26, 2014, Montreal, QC, Canada. Copyright 2014 ACM 978-1-4503-3028-2/14/09 ...$15.00. http://dx.doi.org/10.1145/2656346.2656350.
1 2
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http://wrtnode.com/. http://www.arduino.cc/.
2.
Mobile wireless sensor networks
Cooperative allocation of resources and offloading is a prominent feature in cloud computing that can be leveraged by means of chaining virtualized service instances. When a mobile application is running, auxiliary services may be instantiated either locally at the device level or at the edge of cloud or near a data center based on the nature of the service. Services can be composed to run a new service or application and these services can be instantiated at different locations. In this model, service instances and applications are agnostic to the underlying infrastructure and demand that a certain Service Level Agreement (SLA) is met while the virtual compute, networking and storage resources are allocated optimally. Intelligent applications and context-aware services can leverage contextual interactions among the objects of IoT through a content-aware networking approach. On the other hand, interaction of elements and devices within the ITS and vehicular clouds raise the new challenge of interconnecting massive amount of heterogeneous applications, services, sensors and devices. On the approach towards an edge cloud platform that supports services pertaining to the advancement of applications in IoT, a generic platform is demonstrated in [9] and a top down service chaining approach from abstraction to composition and virtual infrastructure embedding is proposed. Authors in [10, 11] tackle the challenge of IoT by promoting the information centric networking paradigm to leverage higher order connectivity among the objects.
Request
Eth
ern e
t
o offloading
-Fi Wi
MOTIVATION
Response Gateway
Figure 1: An illustration of sensor cloud computing
nearby vehicles of the emerging situation. A comprehensive survey of the vehicular applications is presented in [6]. In essence, with these sensor devices which basically consist of powerful sensing, processing and communicating capabilities, the emerging dissemination of MWSN and cloud computing can bring new opportunities of sensor and cloud integration, which will facilitate clients not only to monitor and collect data from the environment but also to execute and output sensor services using its own processing capabilities. Although various sensor cloud schemes have been developed to increase bandwidth efficiency [7, 8], the sensor device is usually assumed as data collecting point and there is lack of understanding of its processing capability and the potential benefits of being as a computing cloud. Thanks to the recent developments in microelectromechanical systems (MEMS) and software platforms, the sensor devices are shown to be promising to host lightweight applications as a web server. Moreover, the latest radio frequency (RF) technologies and lightweight web services, e.g., (REST) Representational State Transfer (REST)3 , for accessing applications and services on MWSN has enabled the newly emerging Sensor-as-aService (SaaS) paradigm. In this paper, we investigate fundamental characteristics of cloud computing in MWSN in terms of energy efficiency and propose a novel approach to optimize total energy consumption of processing a mobile application requested by a client, while satisfying a certain delay requirement. Specifically, by introducing the concept of cooperation which encourages single devices to share their resources cooperatively, the proposed solution can jointly consider computation and communication costs as a whole, and optimally partition, offload and execute tasks between mobile sensor devices to boost energy efficiency. Moreover, a prototype cloud system has been developing to validate the efficiency of sensor cloud strategies in dealing with diverse vehicular applications. Fig. 1 gives an example of the sensor cloud computing where mobile wireless sensor networks form a cooperative cloud and can serve clients’ service requests via IP networks. To the best of our knowledge, this is the first work that considers mobile sensor device as a service and realizes cooperative sensor cloud computing. This paper is organized as follows. The motivation is highlighted in Section 2. The system model and problem formulation are introduced and derived in Section 3. The optimal sensor cloud computing scheme is presented and analyzed in Section 4. Analytical results are provided in Section 5. Prototype system is demonstrated in Section 6. Finally, concluding remarks are given in Section 7.
3.
SYSTEM MODEL AND PROBLEM FORMULATION
3.1
Mobile Application Model
We consider MWSN where sensor devices can execute lightweight mobile applications with helps of peer sensor devices. In order to characterize the mobile application, we consider a canonical model [12] that captures the essentials of a typical mobile application. Specifically, a mobile application can be abstracted into the following two parameters: • Processing data size L: the total number of data bits for executing a mobile application. We also assume that such processing data can be partitioned from the main code and offloaded to a peer sensor for remote execution [13]. • Application completion deadline T : the maximum number of time slots that a mobile application must be completed. t is discrete time index ranging from t = 1...T . In the following, we denote an application as A(L, T ) and use it to characterize energy behaviors.
3.2
Computation Energy Consumption
The energy consumption of computation is directly determined by the CPU workload of a sensor node. According to [14], the workload can be measured by the number of CPU cycles required by an application, which is related to the data size and computation complexity, and can be defined as W = LX ,
(1)
where W is the number of CPU cycles, L is the processing data size and X is the computation algorithm which can be characterized as a random variable with Gamma distribution. Although a number of factors consume CPU power, such as short circuit power and dynamic power, etc., the energy consumption is dominated by dynamic power which can be minimized by configuring the clock frequency of the chip via the dynamic voltage scaling
3 REST, a lightweight web service implementation, is a design concept that all the objects in the Internet are abstracted as resources. REST style can make applications as sharable, reusable and loose coupling services.
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bits within a time slot is governed by a convex monomial function5
technology [15]. In CMOS circuits [16], the computation energy per operation cycle c is proportional to V 2 , where V is the supply voltage to the chip. When operation is at low voltage, likes in wireless sensor networks, the clock frequency, f , can be treated as a linear function of the voltage supply. As a result, the total energy consumption of computation can be expressed as Ec =
W X
c (w) =
w=1
W X
κfw2 ,
Et = ρ
(2)
w=1
Wp X
c FW (w)fw2 ,
4.
w=1
is the complementary cumulative distribution funcwhere tion (CCDF) that the application has not completed after w CPU cycles. Since the Gamma distribution is exponentially tailed, the c (w) ∼ µe−νw for some constants CCDF can be assumed as FW µ > 0 and ν > 0. It is noted that with w → ∞, the probability goes to 0, which means it is unlikely that an application cannot be completed with a large CPU cycles. According to [12], by optimizing the clock-frequency scheduling for each CPU cycle fw and ensure the application completion time PWp is less than the deadline ( w=1 1/fw ≤ T ), we can derive the minimum value of (3) as KL3 . T2
min
Ecl (ll , t) + Et (lr , gl,r ) + Er (lr , gr,l ) + Ecr (lr , t) {z } | {z } |
s.t.
l l + lr = L ,
ll ,lr
remote energy cost
local energy cost
t≤T.
(6)
• Ecl and Ecr denote the local and remote nodes computation energy consumption, and Et and Er denote transmission and reception energy consumption, respectively. • ll and lr are partitioned data size for local and remote processing. A symmetric channel is assumed between local and remote sensor nodes and has channel gain gl,r = gr,l . A delay deadline T is considered to ensure Quality-as-Service. Theorem 1: The optimal data partition to minimize the total energy consumption of processing a mobile application A(L, T ), is given by
(4)
where K is a constant factor determined by κ and p.
3.3
ENERGY OPTIMIZATION FOR SENSOR CLOUD COMPUTING
Our interest in this section is to find an optimal mobile application partition solution to minimize the total energy consumption of processing a mobile application given that a target completing deadline T is satisfied with the help of a peer sensor device and can be formulated as
(3)
c (w) FW
Ec =
(5)
where ρ denotes the energy coefficient, g denotes channel state and n denotes the order of monomial with value 1 ≤ n ≤ 5. The choice of n depends on the bit scheduler policy, with a large value of n, the scheduler will transmit equal number of bits at every time slot regardless of the channel state [20]. In this paper, we consider the optimal case for n = 1 which is called one-shot policy, in which the transmission only depends on the channel state and is completed in one time slot. There are several reasons for applying this scenario: First, for energy constrained sensor device, it may not be desirable to split a single data across multiple time slots because of extra energy consumed by large overhead associated with each slot. Second, since we impose a delay deadline for completing a mobile application, the transmission time should be relatively small compared to T , such that the time offset between local and remote executions can be negligible. Third, as a mobile application, the transmission air time should be minimized to avoid channel fluctuation. In order to ensure the optimal performance of such policy, the scheduler should be opportunistic, in the sense of offloading tasks to a peer node with good channel quality.
where κ is the effective switched capacity determined by the chip architecture and fw is the clock-frequency which is scheduled in the next CPU cycle given the number of w CPU cycles have been completed. Intuitively, the CPU can reduce its energy consumption by scheduling low clock frequency. However, as a practical implementation, the application has to meet a delay deadline. We adopt the statistical CPU scheduling model [17] which assumes the application should satisfy the soft real-time requirement, in which the application completion needs to meet its deadline with the probability p by allocating Wp CPU cycles. The parameter p is the application completing probability (ACP). In other words, the probability of an application requires no more than the allocated Wp should satisfy FW (Wp ) = P r[W ≤ Wp ] ≥ p. According to (1), since −1 (p), W is a linear function of X, we can obtain Wp = LFX −1 (p) is the inverse cumulative distribution function of where FX X. Hence, the total energy consumption can be derived as Ec = κ
Ln . g
Communication Energy Consumption
β L + , 2 6αL
L β − . 2 6αL
The power consumption of communication is determined by the number of bits being transmitted and the current draw of the electrical circuits that implement the physical communication layer which includes idle, transmit and receive modes. According to the data sheet of a typical low energy radio transceiver, e.g., IEEE 802.11n [18] or 802.15.4 [19], the power consumption is dominated by the transmit or receive modes and their costs are approximately the same. So in this paper, we assume the communication energy includes both transmission and reception of processing data, and do not consider the small output results4 from the cloud. We use an empirical transmission energy model [20] to characterize communication cost. The required energy Et to transmit L
, K denotes the computation coefficient, where α = TK2 , β = g2ρ l,r ρ denotes communication coefficient of wireless channel and gl,r is the channel gain. Proof : See Appendix A. 2 In general, we find that the minimum total energy consumption can be achieved by optimally partitioning, offloading and executing the data via the sensor cloud computing, which can be determined by the application profile, hardware configurations of sensor devices and wireless channel conditions.
4
5
ll∗ =
This is a reasonable assumption for sensor cloud computing where most of sensor based applications come with simple results of warning or image detection indication, etc.
lr∗ =
(7)
Although the monomial cost does not hold for operation at capacity in AWGN channel, there is a practical modulation scheme to well approximate by a monomial [20].
55
diff(L, T, K, ρ, gl,r ) =
2ρT 2 . 3gl,r KL
Optimal data size of local execution (bits)
Property 2: The size difference of the optimal processing data between local and remote executions is (8)
In essence, we can observe that the optimal partition is highly depending on system parameters. Specifically, the local execution is preferable when (8) tends to increase (i.e., small data size L, long delay deadline T , large transmission cost ρ, small computation cost K or high channel loss gl,r ). Otherwise, the remote execution is preferable. Property 3: By defining the application processing speed as υ = L , we have the equivalent offload decision rules T q 2ρ Never offload, if υ ≤ q 3Kgl,r (9) 2ρ Offload, if υ > 3Kg l,r
1100 1000
800
500 10
>
1 , 6
Er
. (10) T 3Kgl,r
Ecr
T=15ms
700
β 6αL
Ecl
T=20ms
900
of the offloading decision rules. With the information of application profile and system coefficients, we can quickly decide the best strategy for processing a mobile application. Fig. 4 shows the total energy consumption of the optimal solution and compare it with that of non-cooperative case where the cloud computing is purely executed locally. By setting K = 10−10 , ρ = 0.006 and T = 20 ms, we observe that under the same communication coefficient, the energy performance improves with better channel quality. Even with severe channel quality and high communication cost (gl,r = 0.1, ρ = 0.01), the performance of the proposed solution is closed to the non-cooperative case when the application processing requirement is not stringent (small L, large T ). As the data size increases, the cooperative sensor cloud computing can ensure optimal with better energy efficiency than the non-cooperative case. Given the worst channel scenario with gl,r = 0.1, an average of 63% of energy can be saved by using the proposed cooperative cloud computing.
ANALYTICAL RESULTS
In this section, we provide simulation results to validate the performance of the proposed method. To be consistent with the real energy measurements [14], we set the computation coefficient in the order of 10−11 , the communication coefficient in the order of 10−2 , a time slot t = 2 ms and channel gain 0 < g < 1. Fig. 2 shows the local processing data size partitioned by the proposed optimal solution. The processing data size is assumed as L = 1024 bits and channel gain is gl,r = 0.5. It is clear that the optimal partition is significantly affected by the system coefficients. With better computation efficiency (smaller K) and higher communication cost (larger ρ), the optimal partition tends to allocate more processing task locally. Moreover, with a relaxed delay deadline (large T ), the local execution is more preferable to save energy by reducing processing speed. Fig. 3 gives an illustration
6.
PROTOTYPE CLOUD SYSTEM FOR VEHICULAR APPLICATIONS
As shown in Fig. 5, our cloud platform adopts a hierarchical architecture which consists of the following four layers.
56
have implicit but disparate assumptions of interpretation. For example, data standard about distance collected from a sensor device in the US (mile) and the same concept of data comes from Europe (kilometer) are different. Such implicit assumptions of data interpretation have to be addressed before the services can be dynamically composed and delivered. Thus, to make the raw sensing data from different sources be context-aware, one possible way is to require service providers to pre-specify the context definition for their sensor devices and register them to the cloud [22]. Further, as introduced in our earlier works, we use a lightweight ontology which contains a modifier using to capture additional information that affects the interpretations of generic concepts [22–24]. Specifically, the generic concept in the ontology can have multiple modifiers, each of which indicates an orthogonal dimension of the variations in data interpretation. The data analysis engine can understand the context of data sources and therefore know how to interpret the data based on the values of the modifiers associated with the corresponding context, which is more flexible and adaptable to the dynamic service environment.
3
Total energy comsuption (µJ)
10
2
10
Non−cooperative cloud Cooperative cloud g=0.1,ρ=0.006 Cooperative cloud g=0.3,ρ=0.006 Cooperative cloud g=0.9,ρ=0.006 Cooperative cloud g=0.1,ρ=0.01
1
10 500
1000 Data size L (bits)
1500
Figure 4: Total energy consumption vs. processing data size Partners
Customers
Applications
Developers
Runtime|Services
API
API
ManagedInterface
API
6.4
Employees API
APIs|Dev.Environment API
ProcessMgmt
MoreThings
Real-timeBigDataAnalysis Dataanalytics|Eventanalytics|Servicecomposition
Datastorageservices Historian|Cache|Search
Visualization
Failurehandling
DeviceRegistration&Connectivity Naming|Addressing|Protocoladaptation|Security&Privacy|Eventmgmt
Figure 5: Architecture of the sensor cloud platform
6.1
Sensor cloud layer
According to Section 4, this is the layer where sensor devices, e.g., smartphone, form a sensor cloud and can process lightweight vehicular applications using their own processing and communication capabilities. The output results can be uploaded via wireless, wired or hybrid networks to the upper management and services layers.
6.2
6.5
Cloud gateway layer
System evaluation
We develop a prototype cloud system to connect senor devices via the cloud platform using the proposed sensor cloud method. The snapshot of the web portal is shown in Fig. 6 (a). Through the pre-defined APIs, interactions with application data can be easily managed and retrieved in a unified manner. We evaluate the system performance of the sensor cloud in terms of time efficiency by setting up a test environment in which a user request environmental data from his car and 5 sensor devices are used to upload computing tasks to the cloud platform with a total average rate of E = 5/min. The ε£-GALEN ontology [25] is adopted as benchmark, and the computing tasks are to index and calculate the similarities of concepts on this ontology under the condition of four different size assertions (1000, 1500, 2000, 36000). We take 5 tests and each lasts for 30 minutes. The average results are shown in Fig. 6 (b). The time delay when performing
This layer works as a bridge between the sensor device and the cloud platform, so as to form a seamless management platform. Although most of the current service interactions on the cloud are based on Simple Object Access Protocol (SOAP) which is a protocol specification for exchanging structured information in the implementation of web services in computer networks, the SOAPREST transformation can be achieved using additional adapters. This adapter can receive the REST service invocation request and transform it into the SOAP service invocation request [21].
6.3
Customized application and service layer
This layer is built upon the specifications and methodologies of RESTful web services and provides the managed interfaces which consists of development environment and application programming interfaces (APIs) to support customized vehicular applications and services. Similar to our prior work [21], the managed interface can be implemented by integrating the Apache ODE6 management interface, the JBoss jBPM7 management interface and series of open source packages. During a sensor device’s run-time, once this layer receives a web service request from a user, it can automatically analyze the requested Uniform Resource Identifier (URI) and the related parameters encapsulated by HTTP, so as to determine the specific class (e.g., JAVA class) to invoke the corresponding web services based on the configuration files. After the operation of the related web services, the sensor cloud will return the results to the user through HTTP. Thus, compared to traditional service-oriented architecture (SOA) based solutions, the advantage of the proposed architecture is that developers can focus on developing the functions of vehicular applications without concerns of transforming raw sensing data to contextual information, and the mapping between specific service request and the corresponding context information in run-time. Fig. 6 shows the user-cloud-sensor interactions in the proposed system.
Cloud management layer
Beyond the basic management services like data storage, visualization and failure handling, we propose the real-time big data analysis as a key service in this layer. Consider the limited resource of sensor devices, diverse contextual data need to be uploaded to the cloud platform for further processing. Such data collected from independent sensor sources often
6 7
57
http://ode.apache.org/ http://jbpm.jboss.org/
(b) Overall system performance of the sensor cloud
Response
Average time delay (ms)
Data set 1 Data set 2 Data set 3 Data set 4 (1000) (1500) (2000) (36000)
Response time 4683
4475
4626
Process time
40
461
702
4395 2483
Total time
2234
4736
7445
136073
Cloud platform
Smart phone platform
(a)An illustration of management web portal
Hardware H
Cloud Platform
Request
Software
• Amazon EC2 M1 Medium Instance, 2 EC2 Compute Unit • 3.75 GB memory, 410 GB storage • 32-bit or 64-bit platform • I/O Performance: Moderate
Big data uploading & analysis
• OS: Ubuntu 14.04 • Servers: ApacheTomcat 8.08 • BPEL engine: Apache ODE1.3.4
Offloading Nexus 4 smart phone
Figure 6: User-cloud-sensor interactions and its performance the task via cloud consists of: 1) response and communication time between the remote cloud platform and the sensor device; and 2) processing time of the task. The results show closed performance of response time with an average of 4.5s, while the process time mostly depends on the size of the data set. As a comparison, we run the application directly on Nexus 4 smartphones, which is a reasonable example to illustrate sensor cloud processing capability, and it shows that sensor cloud can better achieve communication and computation efficiency when running lightweight vehicular application services, whereas the remote cloud platform is more efficient in processing high complexity and big data applications. This result is consistent with analytical result in Section 5. It is worth noting that since the current cloud server is designated to support different types of vehicular applications simultaneously, its response time is inevitable a bit longer comparing to that of servers deployed for specific applications.
7.
ln ln Kl3 Kll3 + ρ r + ρ r + 2r , 2 ll ,lr t gl,r gr,l t
min
ll + lr = L, t ≤ T .
(12) In order to simplify the notation, we use gl,r to denote gr,l because of the symmetric channel assumption, and n = 1. According to the Kuhn-Tucker condition (p.244: KKT conditions for convex problems [26]), the inequality constraint in (12) can be converted to the equality constraint and have the convex function `(ll , lr , λ) =
K 3 lr lr K 3 ll +ρ +ρ + lr +λ(ll +lr −L) , (13) T2 gl,r gl,r T 2
Let α = TK2 and β = g2ρ , we can derive the optimal partition l,r which must satisfy the following conditions ∂`(ll , lr , λ) ∂ll ∂`(ll , lr , λ) ∂lr
CONCLUSIONS
=
3αll2 + λ
(14)
=
3αlr2 + β + λ ,
(15)
Then we obtain
We have shown in this paper that it is advantageous to employ cooperative sensor cloud computing to process vehicular applications. For the future work, we plan to propose additional robust peer-selection mechanisms, which can account for other parameters of importance as the fairness measures, such as the remaining energy of each nodes.
−λ −λ − β , lr2 = , 3α 3α Since ll + lr = L, we have ll2 =
(16)
3αL2 β2 β − − . (17) 4 12αL2 2 Substituting (17) into (16), we obtain the unique optimal solution. λ=−
APPENDIX A.
s.t.
PROOF OF THEOREM 1
We use the Lagrange multiplier method to solve the optimization problem. According to (4) and (5), the optimization problem in (6) can be written as
58
B.
PROOF OF PROPERTY 4
1) For local execution: According to (4) and (5), we obtain Kl3 l T2
·
gl,r ρlr
Ecl Et
[11] P. TalebiFard, H. Nicanfar, X. Hu, and V. Leung, “Semantic based networking of information in vehicular clouds based on dimensionality reduction,” in Proceedings of the third ACM international symposium on Design and analysis of intelligent vehicular networks and applications, pp. 69–76, ACM, 2013. [12] W. Zhang, Y. Wen, K. Guan, D. Kilper, H. Luo, and D. Wu, “Energy-optimal mobile cloud computing under stochastic wireless channel,” IEEE Trans. on Wireless Commu., vol. 12, pp. 4569–4581, September 2013. [13] C. Mei, D. Taylor, C. Wang, A. Chandra, and J. Weissman, “Sharing-aware cloud-based mobile outsourcing,” in Proc. IEEE CLOUD, pp. 408–415, June 2012. [14] A. P. Miettinen and J. K. Nurminen, “Energy efficiency of mobile clients in cloud computing,” in Proc. of the 2nd USENIX Conf. on Hot Topics in Cloud Computing, HotCloud’10, (Berkeley, CA, USA), pp. 4–4, USENIX Association, 2010. [15] J. M. Rabaey, A. Chandrakasan, and B. Nikolic, Digital Integrated Circuits (2nd Edition). Prentice Hall, 2002. [16] T. Burd and R. W. Brodersen, “Processor design for portable systems,” J. of VLSI Signal Processing, vol. 13, pp. 203–222, 1996. [17] W. Yuan and K. Nahrstedt, “Energy-efficient CPU scheduling for multimedia applications,” ACM Trans. Comput. Syst., vol. 24, no. 3, pp. 292–331, 2006. [18] D. Halperin, B. Greenstein, A. Sheth, and D. Wetherall, “Demystifying 802.11n power consumption,” in Proc. of International Conference on Power Aware Computing and Systems, HotPower’10, 2010. [19] M. Palattella, N. Accettura, X. Vilajosana, T. Watteyne, L. Grieco, G. Boggia, and M. Dohler, “Standardized protocol stack for the Internet of (Important) Things,” IEEE Commun. Surveys Tuts., vol. 15, pp. 1389–1406, Third 2013. [20] J. Lee and N. Jindal, “Delay constrained scheduling over fading channels: Optimal policies for monomial energy-cost functions,” in Proc. IEEE Int’l Conf. on Commu. (ICC)., pp. 1–5, June 2009. [21] X. Hu, T. Chu, H. Chan, and V. Leung, “Vita: A crowdsensing-oriented mobile cyber-physical system,” IEEE Trans. Emerging Topics in Computing, vol. 1, pp. 148–165, June 2013. [22] X. Hu, X. Li, E.-H. Ngai, V. Leung, and P. Kruchten, “Multidimensional context-aware social network architecture for mobile crowdsensing,” IEEE Commun. Mag., vol. 52, pp. 78–87, June 2014. [23] X. Li, S. E. Madnick, and H. Zhu, “A context-based approach to reconciling data interpretation conflicts in web services composition,” ACM Trans. Internet Technol., vol. 13, pp. 1:1–1:27, Nov. 2013. [24] X. Hu, Q. Liu, C. Zhu, V. C. M. Leung, T. H. S. Chu, and H. C. B. Chan, “A mobile crowdsensing system enhanced by cloud-based social networking services,” in Proceedings of the First International Workshop on Middleware for Cloud-enabled Sensing, MCS ’13, pp. 3:1–3:6, 2013. [25] A. Rector, J. Roger, P. Zanstor, and E. Haring, “OpenGALEN: open source medical terminology and tools,” American Medical Informatics Association, 2003. [26] S. Boyd and L. Vandenberghe, Convex optimization. Cambridge University Press, 2003.
=
. Since lr ≤ ll , we have
Ecl Kl3 gl,r El Kgl,r ≥ 2l · ⇒ c ≥ · Et T ρll Et ρ
ll T
2 ,
(18)
≤ ll∗ ≤ L, we can obtain the lower bound performance Kg El L 2 of local execution as Ect ≥ ρl,r · 2T . Replacing (10) into equation leads to the result. 2 Kg Er 2) For remote execution: similarly we have Ecr = ρl,r · lTr . Since 0 ≤ lr∗ ≤ L2 , we can obtain the upper bound performance Kg Er L 2 . of remote execution Ecr ≤ ρl,r · 2T Because
L 2
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