Green IndustrIal Internet of thInGs archItecture: an ... - IEEE Xplore

of involved devices with sensing, processing, and communications capabilities, consume substantial amounts of energy and lead to an increasing carbon footprint. On the other hand, IIoT systems typically consist of low-power devices supported by batteries, which constrains the continuous operations of IIoT systems. Recently, various challenging issues in IoT systems have been investigated. The authors in [1] proposed an architecture that addresses the The authors focus on the sense entities domain where huge amounts of energy are consumed by heterogeneity and inter-operability in IoT. The authors in [2] proposed a conceptual system a tremendous number of nodes. The proposed framework includes three layers: the sense layer, skeleton to tackle the flexibility and extendibility the gateway layer, and the control layer. This hierarchical framework balances the traffic load and issues. More recent advances in green IoT can be enable a longer lifetime of the whole system. found in [3]. In this article, we adopt an architecture for energy-efficient IIoT, which is comprised Kun Wang, Yihui Wang, Yanfei Sun, Song Guo, and Jinsong Wu of a sense entities domain, RESTful service hosted networks, a cloud server, and user applications [4]. Further, energy efficient mechanisms are proposed in the sense entities domain that conform to the system architecture. In the IIoT domain, data collection rely heavily bstract on the massive sensor nodes and smart devices. Thus, optimizing sensing, processing, and commuThe Internet of Things (IoT) can support colnications for IoT devices may effectively reduce laboration and communication between objects energy consumption [5]. Being the backbone of automatically. However, with the increasing numIIoT systems, wireless sensor networks (WSNs) are ber of involved devices, IoT systems may conthe main source of energy consumption. There sume substantial amounts of energy. Thus, the are four types of topological structures for the relevant energy efficiency issues have recently deployment of large scale WSNs: mesh, plane, been attracting much attention from both acahierarchical, and hybrid. Both the exact deploydemia and industry. In this article we adopt an ment scheme for mesh WSNs energy-efficient architecture for Industrial IoT (IIoT), which con- COMMUNICATIONS and the ad hoc deployment scheme for plane WSNs suffer sists of a sense entities domain, from limited overall lifetime. The RESTful service hosted networks, scheme for hierarchical WSNs, a cloud server, and user applicawhich places nodes in a tiered framework and tions. Under this architecture, we focus on the limits communications among sensor nodes, can sense entities domain where huge amounts of improve routing efficiency dramatically and make energy are consumed by a tremendous numthe network more scalable and extensible. The ber of nodes. The proposed framework includes hierarchy schemes for hybrid WSNs deploy nodes three layers: the sense layer, the gateway layer, in tiered ways even when direct communication and the control layer. This hierarchical framework among sensor nodes is available, which also limits balances the traffic load and enables a longer lifethe overall lifetime. In this article we adopt hierartime of the whole system. Based on this deploychical deployment and present a three-layer archiment, a sleep scheduling and wake-up protocol tecture for the deployment of nodes and devices is designed, supporting the prediction of sleep in the sense entities domain. By distinguishing intervals. The shifts of states support the use of nodes as sense nodes, gateway nodes, and conthe entire system resources in an energy-efficient trol nodes, the traffic loads can be balanced, and way. Simulation results demonstrate the signifithus network lifetimes may be prolonged. Our cant advantages of our proposed architecture in architecture differs from those in previous studies, resource utilization and energy consumption. since a link traffic balance constraint is proposed ntroduction in order to reduce excessive energy consumption. Based on the proposed three-layer framework, With the rapid development of industrial inforwe improve energy efficiency by proposing a novel matization, the Internet of Things (IoT) has been sleep scheduling protocol, in which the prediction considered an extremely important and promisof sleep intervals is first taken into consideration. ing component of the future transformation of The objective of the protocol is to switch some industrial systems. By leveraging radio-frequency Kun Wang, Yihui Wang, and nodes to sleep mode when the nodes are not in identification (RFID) and more general sensors, Yanfei Sun are with Nanjing University of Posts and working state and wake them up when required. industrial plants and equipment may be protected Telecommunications. The The sleep interval of the nodes is impacted by by high quality surveillance and control based on corresponding author is many factors that we will discuss later. The wake a wide range of wireless and intelligent devices. Yanfei Sun. up protocol is presented correspondingly and the Industrial IoT (IIoT) systems are becoming more Song Guo is with The Hong reprovision of resources is also mentioned. complicated with growing scales, which leads to a Kong Polytechnic University. Contributions: The contributions of our paper number of significant challenges that need to be are summarized as follows: considered, such as increasing energy consumption. Jinsong Wu is with the • A system model for energy-efficient IIoT is In the early stages, one of the purposes to Universidad de Chile. adopted, which consists of a sense entities adopt IIoT was to reduce resource consumption domain, RESTful service hosted networks, a and carbon emissions of industrial systems. HowDigital Object Identifier: cloud server, and user applications. ever, IIoT systems themselves, including a diversity 10.1109/MCOM.2016.1600399CM

Green Industrial Internet of Things Architecture: An Energy-Efficient Perspective

A

ST A N D A R DS

I

48

0163-6804/16/$25.00 © 2016 IEEE

IEEE Communications Magazine — Communications Standards Supplement • December 2016

• In the sense entities domain, we design a three-layer hierarchical framework to realize the deployment of nodes in IIoT, achieving the goal of saving energy and increasing network lifetimes. • Based on the aforementioned architecture, we develop an activity scheduling mechanism to switch nodes to sleep mode and wake them up when required based on the calculation of sleep interval. The remainder of our article is organized as follows. We describe the system model developed for energy-efficient IIoT. We present the three-layer framework for the deployment of nodes in the proposed model. We demonstrate the activity scheduling and wake up mechanism on the basis of the predicted sleep intervals. We demonstrate the experimental results and conclude this paper.

Virtual environment Application

Application server

OS Hypervisor

Interface

Cloud server

Energy-Efficient IIoT Architecture

The overall architecture toward energy-efficient IIoT is illustrated in Fig. 1, which is comprised of a sense entities domain, RESTful service hosted networks, a cloud server, and user applications. Smart devices and nodes are deployed in the sense entities domain. To further optimize energy savings, they are classified into sense nodes (SNs), gateway nodes (GNs), and control nodes (CNs). The network hosts RESTful web services and connects the sense entities with the cloud server. The cloud server virtualizes objects, which then are transferred to the server applications. Processing and computation for the extracted data from the sense entities domain are also made on the cloud server. The application server interface assists the client to communicate with application server without access to the server side codes, while direct access can be made by the administration (admin) node.

Sense Entities

IIoT networks involve sensor nodes and smart devices that are Internet Protocol (IP) enabled and RFID attached. Compared with smart devices, sensor nodes have strict energy constraints due to their dependence upon batteries. Although there are differences between their capabilities in terms of memory and processing, for the convenience of discussion and without loss of generality, we omit these differences. We adopt a novel deployment for these nodes targeting the energy efficiency issue, which will be discussed later. SNs collect the desired information data from their interested area and send them to GNs. Then GNs store the data in buffer and forward them to CNs. Also, GNs run a protocol to calculate the sleep intervals of SNs, which will be discussed in a later section. CNs work as the manager to allocate resources under them and redirect the aggregated data to the networks. Allocation of SNs to specific GNs is also decided by the CNs.

RESTful Service Hosted Networks

Nowadays, REST methodology has been considered [6] in many IIoT proposals, since it makes the integration and accessibility of the heterogeneous devices easier and more convenient. Thus, our network hosts a RESTful service where functionalities and data are regarded

Constrained RESTful environment

Client

Networks CN

Admin

GN SN

SN

Sense entities

Figure 1. Energy-efficient IIoT architecture. as resources that are able to be accessed with uniform resource identifiers. For resource-constrained environments, this RESTful service makes the applications lightweight, simple, and fast [7]. Our proposed RESTful service networks may serve as a bridge between physical sense entities and virtual objects. They receive the specification data from the sense entities side, such as product ID, device IP address, or device features, and notify the cloud sever to create the virtual objects with the semantic description of the sense entities.

Cloud Server

In our energy-efficient IIoT architecture, the cloud server includes the following two components. Virtual Environment: Physical things, included in the sense entities domain, are virtualized for service lookup in the virtual environment. Then the virtual objects are hosted and composited as applications performed inside the virtual machine. Processing capabilities are also enhanced in this way. In addition, the virtual environment is responsible for meshing up diverse objects to initiate composite ones, which are made regarding the system situations or service requests from the users. Further processing and computation of data is also made in the virtual environment. The data harvested by SNs are in large volumes and raw form. Thus, it is necessary to store, compute, and analyze them and extract interpretable information. The virtual environment uses storage and


49

In our proposed system architecture, we focus on the sense entities domain where intelligent collections of monitored data in IIoT systems are made via massive nodes. Thus, optimizing their sensing, processing, and communications can efficiently reduce energy consumptions.

analytical technologies with the aid of the cloud computing platform. Application Server and Interface: The application server allows SNs to communicate with the client through the interface or via direct access from the admin. The registry is made by tracking the services and physical entities that are available within the entire IoT. Thus, activities in both the physical and virtual environments can be monitored, and notification will be sent to the authorized user. The server interface works as a protection of the sever, preventing direct access from the client application. This keeps the server code transparent to the client, preventing unnecessary and potentially dangerous release of the code. However, the admin is allowed direct access to the server in order to make necessary modifications. This is also the interface that provides visualization of the processed data derived from the raw data collected by SNs.

User Applications

Working as client side applications, the user applications can be classified into the following two categories according to the authentication mechanisms. Client Applications: To use virtual objects hosted as applications in the virtual environment, the client uses application interface to send requests to the server. Direct access is not allowed within this authentication. Admin Applications: Unlike the client application, an admin application has the right to access the server directly. Then the admin can promptly make necessary modifications to the system and monitor the performance of the whole system.

Three-Layer Framework of the Sense Entities Domain Design Goals

In our proposed system architecture, we focus on the sense entities domain where intelligent collections of monitored data in IIoT systems are made via massive nodes. Thus, optimizing their sensing, processing, and communications can efficiently reduce energy consumption. As the backbone of IIoT systems, WSNs can be a reference when things are deployed in IIoT. However, compared with WSNs, IIoT achieves a larger scale and involves much more heterogeneous devices, which in return calls for efficient and effective information collection, processing, and communication approaches. To address the mentioned problems, in this paper we follow the hierarchical deployment and arrange nodes in a three-layer approach in the sense entities domain for the deployment of nodes in the IIoT based on our proposed architecture.

Three-Layer Framework

We place nodes involved in the IIoT in a hierarchical style with static routing configuration, avoiding a complex routing protocol as suggested in [8]. The three layers of our architecture are illustrated in Fig. 2, i.e., from bottom to top, the sense layer, the gateway layer, and the control layer. Sense Layer: Nodes to collect the desired

50

Control node Control layer

Gateway nodes Gateway layer

Sensor

Sense layer

RFID Phone

Camera

Figure 2. Three-layer architecture of sensing entities domain.

information from the interested area are placed in the sense layer. Being responsible for data collection, a SN senses the target environment and sends information to a GN. The SNs are classified into trigger-based and periodic, according to the data collection and transmission frequency that are specified by a certain application. The former transmit data only when a particular event occurs. Otherwise, they just wait. However, periodic nodes collect and transmit information on the arrival of a query at regular intervals. Both types collect data in their buffers and transmit data via their communication hardware. Unlike sense nodes in other deployment schemes, we pursue the goal of saving energy and balancing traffic loads. Thus, in our deployment, direct communications between SNs are not allowed and must take GNs as relay nodes. A SN only sends the acquired data to and receives packets from its upper GN. For this, nodes in IIoT are not necessary to implement sophisticated hardware or run complicated routing mechanisms, thus reducing computational complexity and system cost. Gateway Layer: The gateway layer is a collection of nodes with relatively high processing capabilities to run a relatively complicated routing protocol and thus work as the state managers for the lower SNs. GNs also serve as relays to forward the data harvested by their SNs to the cloud server through CNs for further computation and processing. In the scenario discussed in the next section, a GN can calculate the sleep intervals of the SNs connected to it. There is no constraint on communications between two GNs. Control Layer: In the control layer, there are CNs that serve as the manager of GNs. In addition to transmitting data harvested by SNs to the cloud server and fetching required information


from the upper layer, CNs decide the allocation of SNs to GNs based on many factors that will be discussed later.

Communication Mechanism

Based on the aforementioned architecture, we propose the communication mechanism for any two nodes. First, we formulate the communication scenario as follows. Let m and n be two nodes in the system and S be the set of SNs. Also denote G and C as the collection of GNs and CNs. Let r be the communication radius of each node and d(m, n) as the distance between m and n. The communication mechanism can be outlined as follows: 1. For any m  S,n  S, communications between m and n are not allowed. 2. For any m  S,n  G, if d(m, n)  r, the data transmission process between m and n can be made. 3. For any m  G,n  G  C, if d(m, n)  r, m and n can reach each other.

System Deployment Constraints

In such a hierarchical system, we provide two constraints when deploying energy-efficient IIoT. Based on our architecture and the constraints, a comprehensive optimization of the deployment of IIoT can be achieved. Energy Consumption Constraint: The energy consumption in data communication is much higher than in data sensing and processing in IIoT. Thus, for discussion simplicity, in our architecture we only consider energy required for transmitting and receiving data. According to the Friis free space model [9], the energy consumption for a node to transmit information is affected by the length of the data, the energy consumption of radio electronics, and the distance between the source and destination nodes. The energy consumption of radio electronics also determines that for receiving data. Based on this analysis and the communication mechanism described above, the energy expenditure of each node in the sense layer, the gateway layer, and the control layer can be calculated [10]. In particular, the receiving energy consumption of a SN can be omitted based on the fact that it only receives signal messages that are comparably small. Link Traffic Balance Constraint: In our architecture, CNs serve as the manager of GNs as well as the “wired” bridge between sense entities and cloud server. Thus, they have more bandwidth than SNs and GNs and the bandwidth is not constrained at CNs. However, the wireless linked GNs must satisfy some requirements. For a GN that communicates not only with SNs in the bottom layer but also the neighboring GNs, the link must ensure that the summation of the transmitting and receiving data rates are under the maximum rate threshold. However, a SN and a CN with a single transmitting or receiving function only need to guarantee that the data rate of this function process is under the maximum. With GNs of relatively strong performance upon the SNs, they are responsible for most of the traffic loads. Thus, link flows balance can be achieved in this three-layer approach.

Sleep Scheduling and Wake Up Protocol Protocol Overview In our proposed architecture, the sense entities domain is comprised of battery-powered nodes, which are responsible for energy-consuming data processing and communications. We improve the energy efficiency via sleep scheduling, of which the objective is to switch some nodes to sleep mode during their inactive periods and wake them up when required. Unlike what has been done in other works, we make the sleep interval predictable according to the usage history and remaining battery level of the nodes. The predicted value in return boosts the utilization of all the resources in IIoT via reprovisioning the allocated resources to other nodes in active mode. Although the authors in [11] proposed a method to improve energy efficiency by prolonging the sleep period of nodes, the mechanism to predict the sleep interval of a node was not available.

Sense Nodes

On the side of a SN, it is said to be “active” when it is of high energy, sensing and transmitting data to a GN. When turning off its transceivers, it switches to a low-energy state called sleep mode and stays in sleep mode until a wake-up signal sent by the GN is received. A SN can be switched off for a long duration of time when it is not necessary to sense the target environment. There are three situations in which a GN sends a wake-up signal. Two of them are when the sleep interval has expired and a query arrives. In these cases, the SN again senses the environment to fill its buffer. The third case is when a communication request is made by another SN, and the SN receives the incoming data into its buffer. After the completion of this action, it switches back to the sleep mode. Thus, battery power can be used efficiently.

The application server allows SNs to communicate with the client through the interface or the direct access from the admin. The registry is made via tracking the services and physical entities that are available within the whole IoT. Thus, activities in both physical and virtual environment can be monitored and notification will be sent to the authorized user.

Gateway Nodes

Serving as storage media for the sensed data from SNs and the controller of the connected SNs, GNs are the major contributors toward energy saving. The sleep interval of a SN collected to a GN can be calculated in two steps [12]. First, the GN obtains a predicted value based on the previous usage history of SN. Second, the actual sleep interval is calculated on the basis of the predicted value and other various factors. Predicted and Actual Sleep Intervals: Let tin be the predicted value and Tin be the actual value of the nth sleep interval of SNi. The GN obtains tin+1 by using the exponential average of the previous sleep intervals. Then the actual sleep interval can be calculated by adding a factor rti to rtin+1. In calculating the predicted sleep interval, there involves both the past history in t in and the latest information in Tin. The initial value t0 is constant and based on each application.  is a controlling parameter that determines the relative weight of the present and the past. When  = 1, this indicates the next sleep interval is just the last interval. When  = 0, this indicates the most recent scenario poses no effect on the next sleep interval. It is the node type that determines the value of . For a trigger-based node, the value


51

SN1

D Overlap area

SN2

r

Figure 3. SNs’ coverage area overlaps. Noncritical

Sleep interval expires?

No

Yes

A query reaching?

Yes

No

Communication request?

Critical

Send a wake-up signal

Figure 4. Wake up mechanism. is close to 0, since the occurrence time of the trigger event is based on past experiences. For a periodic node, the value is close to 0.5, providing that the present and past scenarios are considered equally important. Then calculating the actual sleep interval, there is a factor rTi indicating the change of sleep interval. Whether the value is zero, positive, or negative depends on various factors for the node type. Factor r T i in Two Node Types: The r T i is affected by the factors varying with the node type. Periodic nodes: • Conflict factor (i): There is a possibility that the coverage area of nodes may overlap, as shown in Fig. 3. Thus  i calculates the amount of SNi’s overlapped region using r and Dij, where r represents the coverage of SN and Dij stands for the distance between SNi and SNj. • Battery level (Ei): With a high battery level, an SN can have a short sleep interval. On the other hand, a longer sleep interval comes with a decrease in battery level for energy efficiency. • Coefficient of variation (CoV): The changes between the previously and currently sensed values are monitored by GNs. If a notable variation is detected, the sleep interval will be decreased to allow SNs to collect infor-

52

mation from the target environment. Similarly, the sleep interval will be prolonged if the value does not deviate much. Trigger-based nodes: • Conflict factor ( i): There is a slight difference compared with the one in the periodic nodes that the occurrence places of the trigger event decide the conflict area. There is a factor p i indicating the probability of the nonoccurrence of the trigger event in SNi’s coverage area. With a lower pi, SNs are more likely to cover the occurrence place of the trigger event and their sleep interval will be reduced, and vice versa. • Battery level (Ei): For trigger-based nodes, it is more important to sense when the trigger is fired in order to save energy. However, the sleep interval will be prolonged when the battery level is rather low to gain a whole lifetime increase from the system perspective. Wake up Mechanism: With the calculated value, GN sends a signal to wake up the SN when the sleep interval expires. On the third occasion that an SN wants to talk to another, the communication is carried out via a GN, as shown in Fig. 4. The sender node indicates the communication type as critical, calling for an immediate action. Noncritical communication refers to the case where the contained information from the communicated message can be dealt with in the near future. Based on this, when a SN wants to talk to another SN in sleep mode, the GN will check the type of the information. When it is critical, a wakeup signal is sent immediately to guarantee no packet loss. Otherwise, the GN saves the information from the sender SN and forwards it when the SN wakes up. Thus, it is the GN that helps efficiently utilize energy by switching the state of the SNs.

Control Nodes

When it comes to CNs, we mainly focus on their control over GNs. They relay the information collected by SNs to the cloud server. Any required information is passed on to GNs through CNs. In other words, they set up a bridge between the cloud server and sense entities. In addition, the allocation of SNs to GNs is decided by a CN based on the battery level of the GNs and the distance between the SNs and the GNs. The allocation of SN i to GN j is then calculated using d(i, j), which shows the distance between SNi and GNj and Ej, which is the battery level of GNj that is under the control of the CN. When there is a battery level shift occurring at any GN, the CN calculates a(i, j) again. In this way, the CN acts as a manager to balance the loads of the system and thus obtain an efficient utilization of the resources.

Performance Evaluation

In this section, the evaluation is given to validate the effectiveness of our proposed architecture measured by resource utilization and energy consumption.

Experimental Setup

We deployed 300 nodes in a 100  100 m 2 region and they included 250 SNs and 50 GNs. The server at the lab acts as the CN and the cloud server. Among the SNs, there are both periodic


100

1.0

Original Optimized

60

40

0.6

0.4

0.2

20

0

Optimized Original

0.8 Energy consumption (nJ)

Resource utilization rate (%)

80

1e7

0.0 0

20

40

60

80 100 Time (min)

120

140

160

180

0

0

30

0

60

0

90

0

120

0

150

0

180

0 210

24

00

00 27

00

30

Number of nodes (b)

(a)

Figure 5. Experimental results: a) Resource utilization; b) Energy consumption. nodes and trigger-based nodes. The assignment of each SN to a spcific GN is calculated by the CN, and the energy-efficient algorithm is running at the GN’s side. Data collected by the SNs are sent to the data center that hosts Hadoop via their GNs. The conflict factor is assumed to be zero. Given the number of nodes that may be at a low level, we use a bootstrapping technique [13] to bootstrap nodes 300 to 3000. Then, the performance of these nodes is observed within a 180-minute experiment, with and without calculated sleep intervals. To determine the scalability, we repeat the experiment with a varying number of nodes. After that, resource utilization and energy consumption are measured and shown in Fig. 5a and Fig. 5b, where the optimized curve stands for the results of adopting our proposal based on the original ones.

Performance Analysis

As shown in Fig. 5a, considerable improvement in resource utilization is achieved due to the strict schedule of mode switching using predicted sleep intervals for nodes. Figure 5b shows the architecture’s scalability, as a significant amount of energy can be saved by increasing the number of nodes. This is due to the fact that as the network becomes larger, our architecture will play a more important role in optimizing energy consumption.

Conclusions

With the emerging applications of IoT [14, 15], it may be adopted to protect industry plants via control and surveillance, which in turn will introduce critical energy consumption issues. From the perspective of energy saving in industry, we adopted an architecture composed of a sense entities domain, RESTful service hosted networks, a cloud server, and user applications. Then, to the massive deployments of nodes in the sense entities domain, we presented a three-layer architecture that includes a sense layer, a gateway layer, and a control layer. By forbidding direct communication between two SNs and using GNs as relay nodes, the proposed structure may save energy and prolong the lifetime of the system. Moreover,

regarding the nodes, a sleep scheduling and wake up protocol has been proposed. By calculating the sleep interval of SNs, the GN can change the state of SNs for the purpose of efficient energy utilization. Meanwhile, the CN decides the allocation of SNs to GNs. An evaluation has validated the effectiveness of our architecture in improving resource utilization and energy consumption.

Acknowledgment

This work is supported by NSFC (61572262); the NSF of Jiangsu Province (BK20141427); NUPT (NY214097); the Open Research Fund of the Key Lab of Broadband Wireless Communication and Sensor Network Technology (NUPT), the Ministry of Education (NYKL201507); the Qinlan Project of Jiangsu Province; the ERANet LAC Project (ELAC2015/T10- 0761); and CONICYT FONDEF (ID16I10466).

References

[1] C. Sarkar et al., “A Scalable Distributed Architecture Towards Unifying IoT Applications,” Proc. IEEE World Forum on Internet of Things (WF-IoT), Mar. 2014, pp. 508--13. [2] J. Leu, C. Chen, and K. Hsu, “Improving Heterogeneous SOA-based IoT Message Stability by Shortest Processing Time Scheduling,” IEEE Trans. Services Comp., vol. 7, no. 4, Oct. 2014, pp. 575--85. [3] C. Estevez and J. Wu, “Recent Advances in Green Internet of Things,” Proc. IEEE Latin-American Conf. Commun. (LATINCOM), Nov. 2015, pp. 1–5. [4] S. F. Abedin et al., “A System Model for Energy Efficient Green-IoT Network,” Proc. Int’l. Conf. Information Networking (ICOIN), Jan. 2015, pp. 177–82. [5] H. Chao, Y. Chen, and J. Wu, “Power Saving for Machine to Machine Communications in Cellular Networks,” Proc. IEEE GLOBECOM Wksps., Dec. 2011, pp. 389–93. [6] Z. Sheng et al., “Recent Advances in Industrial Wireless Sensor Networks Towards Effcient Management in IoT,” IEEE Access, vol. 3, June 2015, pp. 622–37. [7] P. Li, S. Guo, and J. K. Hu, “Energy-Efficient Cooperative Communications for Multimedia Applications in Multi-Channel Wireless Networks,” IEEE Trans. Computers, vol. 64, no. 6, June 2015, pp. 2670–279. [8] G. Han et al., “Cross-Layer Optimized Routing in Wireless Sensor Networks with Duty Cycle and Energy Harvesting,” Wireless Commun. and Mobile Comp., vol. 15, no. 16, Mar. 2015, pp. 1957–81. [9] W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An Application-Specific Protocol Architecture for Wireless Microsensor Networks,” IEEE Trans. Wireless Commun., vol. 1, no. 4, Oct. 2002, pp. 660–70.


53

[10] J. Huang et al., “A Novel Deployment Scheme for Green Internet of Things,” IEEE Internet of Things J., vol. 1, no. 2, Apr. 2014, pp. 196–205. [11] J. Liang et al., “An Energy-Efficient Sleep Scheduling with QoS Consideration in 3GPP LTE-Advanced Networks for Internet of Things,” IEEE J. Emerg. Sel. Topics Circuits Syst., vol. 3, no. 1, Mar. 2013, pp. 13–22. [12] N. Kaur and S. K. Sood, “An Energy-Efficient Architecture for the Internet of Things,” IEEE Systems J., no. 99, Oct. 2015, pp. 1–10 . [13] X. Bao et al., “Helping Mobile Apps Bootstrap with Fewer Users,” Proc. ACM Conf. Ubiquitous Computing (Ubicomp), Sept. 2012, pp. 491–500. [14] K. Wang et al., “LDPA: A Local Data Processing Architecture in Ambient Assisted Living Communications,” IEEE Commun. Mag., vol. 53, no. 1, Jan. 2015, pp. 56–63. [15] K. Wang et al., “Mobile Big Data Fault-Tolerant Processing for eHealth Networks,” IEEE Network, vol. 30, no. 1, Jan. 2016, pp. 1–7.

Biographies

Kun Wang [M’13] ([email protected]) received the B.Eng. and Ph.D. degrees from the School of Computer, Nanjing University of Posts and Telecommunications, Nanjing, China, in 2004 and 2009, respectively. From 2013 to 2015 he was a post-doctoral fellow with the Electrical Engineering Department, University of California at Los Angeles (UCLA), CA, USA. In 2016 he was a research fellow with the School of Computer Science and Engineering, University of Aizu, Fukushima, Japan. He is currently an associate professor with the School of Internet of Things, Nanjing University of Posts and Telecommunications. His current research interests are mainly in the area of big data, wireless communications and networking, smart grid, energy Internet, and information security technologies. He is a member of ACM.

54

Yihua Wang ([email protected]) is a graduate student with the School of Internet of Things at Nanjing University of Posts and Telecommunications, China. Her current research interests include wireless sensor networks, embedded systems, and cyber-physical systems. Yanfei Sun ([email protected]) received the Ph.D. degree in information networks from the Nanjing University of Posts and Telecommunications, Nanjing, China, in 2006. He has been a professor with the School of Automation, Nanjing University of Posts and Telecommunications, since 2006. His main research interests are in the areas of intelligent optimization, network management, machine learning, and future networks. Song Guo [M’02, SM’11] ([email protected]) received a Ph.D. degree in computer science from the University of Ottawa, Canada. He is a full professor in the Department of Computing, The Hong Kong Polytechnic University. His research interests are mainly in the areas of wireless communication and mobile computing, cloud computing and networking, and cyber-physical systems. He serves as an associate editor of IEEE TGCN and IEEE TETC. He is a senior member of ACM. Jinsong Wu [M’99, SM’11] ([email protected]) is with the Department of Electrical Engineering, Universidad de Chile, Santiago, Chile. He was the lead editor and a co-author of the book Green Communications: Theoretical Fundamentals, Algorithms, and Applications (CRC Press, 2012). He is the founder andfounding chair of the Technical Committee on Green Communications and Computing (TCGCC) of the IEEE Communications Society, which was established in 2011 as an official Technical Subcommittee (TSCGCC) and elevated as TCGCC in 2013. He is an associate editor of IEEE Communications Surveys and Tutorials, IEEE Systems Journal, and IEEE Access.
