Research Article  Open Access
Zhengyang Song, Yucong Duan, Shixiang Wan, Xiaobing Sun, Quan Zou, Honghao Gao, Donghai Zhu, "Processing Optimization of Typed Resources with Synchronized Storage and Computation Adaptation in Fog Computing", Wireless Communications and Mobile Computing, vol. 2018, Article ID 3794175, 13 pages, 2018. https://doi.org/10.1155/2018/3794175
Processing Optimization of Typed Resources with Synchronized Storage and Computation Adaptation in Fog Computing
Abstract
Wide application of the Internet of Things (IoT) system has been increasingly demanding more hardware facilities for processing various resources including data, information, and knowledge. With the rapid growth of generated resource quantity, it is difficult to adapt to this situation by using traditional cloud computing models. Fog computing enables storage and computing services to perform at the edge of the network to extend cloud computing. However, there are some problems such as restricted computation, limited storage, and expensive network bandwidth in Fog computing applications. It is a challenge to balance the distribution of network resources. We propose a processing optimization mechanism of typed resources with synchronized storage and computation adaptation in Fog computing. In this mechanism, we process typed resources in a wirelessnetworkbased threetier architecture consisting of Data Graph, Information Graph, and Knowledge Graph. The proposed mechanism aims to minimize processing cost over network, computation, and storage while maximizing the performance of processing in a business value driven manner. Simulation results show that the proposed approach improves the ratio of performance over user investment. Meanwhile, conversions between resource types deliver support for dynamically allocating network resources.
1. Introduction
With the extensive development of IoT applications, it is necessary to promote storage, computing, and communication capability of IoT devices, such as efficient storage, semantic integration, and parallel processing. Due to powerful computing capability compared with edge devices, cloud computing puts all the computing tasks on the cloud to process data efficiently and provides highly vitalized storage and computing services on top of massively parallel distributed systems [1–3]. However, with the growing quantity of data generated at the edge, the speed is becoming the bottleneck for the cloudbased computing paradigm [4]. Also, future wireless access networks will face the challenge of transporting the everincreasing volume of data centric traffic with ever tighter timing and quality of service (QoS) requirements [5]. In order to reduce the communication bandwidth needed between edge devices and the central data centre, nascent technologies and applications for mobile computing and IoT are driving computing towards dispersion [6]. Conventional methods, which are based on bandwidth allocation scheme, improve effectively bandwidth utilization efficiency [7, 8]. Some methods [9–11] achieve realtime data analysis, but they have not considered user investment [12, 13], processing cost [14], and the efficient utilization of IoT resources with reduced risks [15, 16] of security and robustness [17] issues.
Fog computing stresses the proximity to terminal users and client objectives, dense geographical distribution and local resource pools, latency reduction, and backbone bandwidth savings to achieve better quality of service (QoS) [18]. Meanwhile, the computing paradigm pushes data analytic and knowledge generation power away from centralized nodes to the logical extremes of a network near the source of the data. Therefore, a large number of heterogeneous, complexity, and hierarchy resources can be processed effectively in fog computing systems. However, storage and computation power in fog computing systems are not balanced. We argue that it is a challenge to provide better load balancing of resources in the Internet of Things. From the perspective of resource owners, obtaining costeffective resources processing services remains the primary concern for adoption of fog computing services. In [19], the authors not only studied the tradeoff between network performance improvement and the communication overhead of transmitting network information but also proposed to optimize resource allocation in wireless networks.
Fog devices have increasingly generated a large amount of resources including data, information, and knowledge. In [20], Chaim illustrated the concepts of defining data, information, and knowledge. We use Knowledge Graph to solve the problem of extracting relationships from sources of knowledge [21, 22]. Knowledge Graph has become a powerful tool for representing knowledge in the form of labelled digraphs and gives textual information semantics. Knowledge base contains a set of concepts, examples, and relationships [23]. In [24], Duan et al. clarified the architecture of Knowledge Graph in terms of data, information, knowledge, and wisdom. In [25], the authors proposed to designate the form of Knowledge Graph as four basic forms: Data Graph, Information Graph, Knowledge Graph, and Wisdom Graph. In [26], the authors proposed to answer five Ws questions through constructing the architecture composed of Data Graph, Information Graph, Knowledge Graph, and Wisdom Graph. The development of software service system can be divided into data sharing, information transmission, and knowledge creation stages in terms of data, information, and knowledge [27]. In fog computing systems, the computing resources for providing device services are highly limited such as storage capacity, computing power, and network bandwidth. The demand for huge search space and computation power has become a big challenge. We propose a mechanism to improve the ratio of performance over investment when providing resources processing services. The proposed approach minimizes processing cost over network, computation, and storage while maximizing the performance of processing in a business value driven manner. Multitypes users’ investment and corresponding benefit rate decide final resource combination cases, which makes the system have a relatively stable state. Furthermore, we design several simulations to verify the feasibility and evaluate the performance of the proposed approach. Currently we did not consider negative situations of which improper arrangement of resources might lead to penalties [28] instead of rewards.
The rest of this paper is organized as follows. In Section 2, we define typed resources and explain the proposed system model. Section 3 provides a running example. Section 4 presents practice of storage and computing collaborative adaptation towards typed IoT resources. Section 5 reports the simulation and summarizes the simulation results. Section 6 reports related work. Finally, we draw conclusions and outline aspects of future work in Section 7.
2. Definitions and System Model
In this section, we propose the definition of typed resources. Furthermore, we design the processing architecture of typed resources in the Internet of Things. In order to satisfy the transmission demands of nodes in a given wireless network, we construct the system model through restricting network resources.
2.1. Definitions of Typed Resources
Wide application of the Internet of Things has been acquiring a huge amount of typed resources. We classify resources into three types: data, information, and knowledge [27]. Data is not specified for stakeholder or machine. Data is the collection of discrete elements and concepts. Information is not specified for stakeholder or machine. Information is conveyed through conceptual mapping and combination of data and is used for interaction. Knowledge is used to reason and predict unknown resources such as data, information, and knowledge. Table 1 provides an explanation of typed resources of data, information, and knowledge. Definitions of resource elements and graphs are as follows.

Definition 1 (resource element ()). We define resource element as, , and represent data, information, and knowledge in DIK hierarchy, respectively, where DIK is an abbreviation of , , and .
Definition 2 (graphs). In our previous work [25], we specified Knowledge Graph in a progressive manner as four basic forms: Data Graph, Information Graph, Knowledge Graph, and Wisdom Graph. In this work, we propose to specify the existing concept of Knowledge Graph in three layers. We define graphs as , , and represent Data Graph, Information Graph, and Knowledge Graph, respectively. is used to model the temporal and spatial features of . is a combination of related . expresses the interaction and transformation of between entities in the form of a directed graph. is a collection of statistical rules summarized from known resources.
In order to optimize resources processing, it is important to convert the types of resources in the Internet of Things. We give the explanations of the type conversion mechanism as follows.
(a) For conversion from to , in the absence of context, is meaningless. We convert to through reorganizing . The new collection of corresponds to a different class or concept, which is .
(b) For conversion from to , inherits semantic relationships from standard mode and is effectively integrated and reused by other applications. In the conversion process from to , we use knowledge validation technology to eliminate redundancy and inconsistency of through linking sources and semantic constraints. Then, we identify the most reliable to form .
(c) For conversion from to , is used to express the interaction and collaboration between entities. Through the classifying and abstracting interactive records or behaviour records related to the dynamic behaviour of entities, we obtain in the form of statistical rules. We infer from known resources and collect necessary in the process of inference through appropriate research techniques such as experiments and surveys.
(d) Regarding conversion from to , we know that conversion from to is the transition from concept set to resource instances. expresses the dynamic interaction and collaboration between entities; we obtain through observing an object at a certain time in a static state.
(e) For conversion from to , according to knowledge reasoning, we establish relevant examples for extracted collection of . Relationships between nodes are associated with instances in the form of attributes.
(f) For conversion from to , the schemaless feature of makes it possible to link and utilize a richer knowledge base to help users make decisions, from retrieval to creation.
2.2. Processing Architecture of Typed Resources in the Internet of Things
Generally, we obtain through mining and through deducting and intelligence from . Therefore, , , , and wisdom are the layers of a gradual relationship. The Knowledge Graph is divided into four levels: , , , and Wisdom Graph. Figure 1 shows the processing architecture of typed resources in IoT on the basis of , , and . In this architecture, graph types are allowed to convert between each other. We provide the definitions of , , and as follows.
Definition 3 (). We define as is a collection of discrete elements expressed in the form of various data structures including arrays, lists, stacks, trees, and graphs. featuring a static state is more about expressing a structural relationship. is used to model the temporal and spatial features of .
Definition 4 (). We define as is a combination of related . expresses the interaction and transformation of between entities in the form of a directed graph. records the interaction between entities including both direct interaction and indirect interaction. Also, can be expressed using multiple tuples.
Definition 5 (). We define as is of free schema and expresses rich semantic relationships, which is conductive to have a completing mapping towards user requirements described through natural language. is a collection of statistical rules summarized from known resources. further improves and perfects the semantic relations between the entities on the basis of and and then forms a semantic network connected by a large number of interactive relationships.
2.3. System Model
We propose a transmission optimization model of typed resources in the Internet of Things, which is called IoT_TOM. Figure 2 shows a network composed of multiple wireless nodes. In this given network, AB is a communication link between fog nodes A and B. We denote buffer size of each node as . We denote the number of DIK packets to be forwarded as . A DIK packet is a unit of resource made into a single package in forms of a resource combination in our proposed IoT_TOM. We denote the average length of DIK packets as l. We denote bandwidth between A and B as . We denote resource flow capacity on this link as . In practice, the distribution of network resources is not balanced in fog computing applications. The proposed mechanism of converting resource types achieves global optimization to deal with this problem. We use resources forwardingwaiting equilibrium and bandwidth utilization equilibrium to evaluate the performance of the proposed mechanism.
2.3.1. Bandwidth Utilization Equilibrium
Given a wireless network, we consider utilizing bandwidth efficiently. It is necessary to dynamically allocate bandwidth. Therefore, we consider transmitting DIK resource packets between IoT nodes, which aims to obtain enough storage and computing resources through consuming bandwidth. BE_{buse} denotes bandwidth utilization equilibrium. BE_{buse} is related to the network parameters such as bandwidth between IoT nodes and resource flow. Let IR_{ban} denote the bandwidth idle rate; then BE_{buse} is the variance of IR_{ban}. IR_{ban} and BE_{buse} can be illustrated asIn (6), B_{ij} denotes bandwidth between IoT nodes. F_{ij} represents resource flow on the link.
2.3.2. ForwardingWaiting Equilibrium
Resources forwardingwaiting time reflects the performance of the proposed IoT_TOM, which includes two parameters, forwardingwaiting rate (FR_{wait}) and forwardingwaiting equilibrium (BE_{wequ}). It is important to reduce the traffic load and congestion in the network in the absence of packet loss to balance resource flow. Let N_{i}l be the size of resources to be forwarded at node i; then BE_{wequ} is a variance of FR_{wait}. BE_{wequ} and FR_{wait} can be illustrated asThe cooperation between forwardingwaiting equilibrium and bandwidth utilization equilibrium enables users to utilize IoT resources more efficiently. We denote the objective function for defining utilization of typed resources in the Internet of Things as F, which can be illustrated asIn (10), and , which can be obtained through data training, are parameters that contribute to BE_{buse} and BE_{wequ}, respectively. The smaller F is, the better resource flow capacity distribution performs.
3. Running Example to Provide UAVs Courier Service
Today’s unmanned aerial vehicles (UAVs) courier service is a typical IoT application in fog computing. However, it is difficult to address bandwidth bottlenecks and management complexity of a UAV dispatch centre. For example, Figure 3 shows the geographical location distribution of UAVs providing courier services over the air. Dotted directional lines represent resources transmission directions using conventional methods, which may require expensive satellite navigation. The black directional lines display resource transmission directions of providing courier service using a fog computing system.
In an ideal situation, IoT resources without redundancy and inconsistency are transmitted in low user investments. The actual situation may not follow the ideal situation. For example, D_{DIK} resources of (longitude and latitude) featuring redundancy and inconsistency make the scale of D_{DIK} collection large. Therefore, if storage capacity at node UAVA is not enough, we need to consume corresponding bandwidth to transmit these D_{DIK} resources to other nodes, which aims to obtain storage resources. We consider converting the resource type to I_{DIK} or K_{DIK} when transmitting these D_{DIK} resources, which aims to change the scale of initial D_{DIK} collection. Table 2 shows partial D_{DIK} resources generated by UAVA. And Table 3 shows partial I_{DIK} resources generated by UAVB.


For UAVA, according to D_{DIK} resources generated from time t_{1} (2018.1.1, 9:00) to t_{2} (2018.1.1, 9:20), we sum up that two relationships between , and time are longitude = k_{1}time+b_{1} and latitude = k_{2}time+b_{2}. In fact, we sum up 400 linear relationships between , and time roughly. Conversion from D_{DIK} to I_{DIK} is illustrated asWe store these I_{DIK} resources converted from D_{DIK} resources in form of I_{DIK}k_{1}time+b_{1}, k_{2}time+b_{2}), (k_{1}, b_{1}, k_{2}, b_{2}, time (t_{1}, t_{2})), which requires storage space of 32 bits. Then total storage space to store these I_{DIK} resources is 32 400 = 12.5 Kbit.
Table 3 shows I_{DIK} resources generated by UAVB. Through counting and summarizing these rules, we obtain 100 K_{DIK} resources roughly. Conversion from I_{DIK} to K_{DIK} is illustrated asWe store these K_{DIK} resources in form of K_{DIK} (rain, down, UAVB), which requires storage space of 4 bits. Then total storage space to store these K_{DIK} resources is 4 250 = 0.97 Kbit.
Hence, these conversions between resource types reduce the scale of collected resources. In our proposed resource type conversion mechanism, there are at most 27 27 kinds of resource combinations in each node over the network. As shown in Figure 4, we take one resource combination case as an example. Conversion from into aims to obtain storage resources through consuming computing capability. In general, we keep the system in a relatively stable state with storage and computing collaborative adaptation.
Because of complex courier tasks and geographic environment conditions of UAVs, resources distribution is unbalanced. Global optimization of the UAVs wireless network can solve this problem to a large extent. We design a mechanism that minimizes processing cost over network, computation, and storage while maximizing the performance of processing in a business value driven manner, which is illustrated in Section 4. Figure 5 displays resources allocation results in region 1 of the wireless network by using the proposed mechanism. Initial resource combinations on UAVA and UAVB are as follows:
(a)
(b)
Combination (UAVA) = , , ), (, , . And the resource scale on UAVA is 12 Kbit, bandwidth (A → a) is 6 Kpbs, and buffer size (A) is 2 Kbit.
Combination (UAVB) = , , ), (, , . And the resource scale on UAVA is 26 Kbit, bandwidth (B → a) is 6 Kpbs, and buffer size (B) is 2 Kbit.
Total storage spaces at node UAVB and node UAVB are 5 Kbit and 10 Kbit, respectively. Thus, we convert resource types and transmit these resources to node a, which is illustrated as follows:
Combination (UAVA)’ = , I_{DIK}, K_{DIK}), (DG_{DIK}, IG_{DIK}, . As shown in Figure 5(a), the resource scale on UAVA is 24 Kbit and bandwidth (A → a) = 4 Kpbs and buffer size (A) = 3 Kbit.
Combination (UAVB)’ = , I_{DIK}, K_{DIK}), (DG_{DIK}, KG_{DIK}, . As shown in Figure 5(b), the resource scale on UAVB is 23 Kbit and bandwidth (B → a) = 3 Kpbs and buffer size (B) = 2 Kbit.
However, there are two problems in implementing this idea. First, storage and computation capabilities of IoT devices in fog computing systems are different. Thus, it is necessary to obtain storage capacity by transferring typed resources. Second, both the performance of processing resources and user investments should be considered to improve user investment benefit.
4. Storage and Computing Collaborative Adaptation towards IoT Resources
In fog computing systems, there may be insufficient storage and computation capability at some nodes. We consider transmitting IoT resources to other nodes, aiming to obtain storage and computation resources through consuming bandwidth. Given a wireless network, some nodes consume storage capacity to obtain enough computing resources. Some nodes consume computing capability to obtain enough storage resources. In general, conversion between resource types provides a support for balancing the distribution of network resources in the Internet of Things.
In this section, we describe optimizing processing mechanism of typed resources with collaborative storage and computation adaptation. We define IoT resources and resources on as follows.
Definition 6 (IoT resources). We define IoT resources as a tuple IR = < IRT, IRS >. IRT is the type set of IoT resources represented by a triad < , , >. IRS is the scale of different kinds of IoT resources represented by a triad < , , >. Each irs denotes the scale of resource in the form of irt.
Definition 7 (resources on ). We define resources on as a tuple RoG = <RGT, RGS>. RGT is the type set of resources on represented by a triad < , , >. RGS is the scale of different kinds of resources on represented by a triad < , , >. Each rgs denotes the scale of resources in the form of rgt.
4.1. Computation of Resource Type Conversion Cost
Considering the disadvantages of conventional resources processing methods, we propose to convert both types of resources in IR and RoG. First, we would like to convert the types of resources in IR, which needs to invest corresponding cost. Specifically, we assign values from to each element of the type set IRT. Then we use these values to form the combination case IRT’ = < ’, ’, ’ >, where ’, ’, and ’ belong to , , . As shown in Table 4, we denote as the atomic conversion cost per unit resource in IR. Conversion cost of resource types from IR to IRT’ can be illustrated asSecond, we convert the types of resources in RoG. Specifically, we assign values from to each resource of the type set RGT of RG. Then we use these values to form the combination case RGT’= < , , >, where , , and belong to , , . As shown in Table 5, we denote as the atomic type conversion cost per unit resource in RoG. Conversion cost of resource types from RGT’ to RoG can be illustrated as
4.2. Computation of Cost of Processing IR in RoG
The proposed processing architecture of typed resources is adopted to process IoT resources. Cost of processing IR in RoG is related to the resource scale. As shown in Table 6, we denote as the atomic cost of processing unit , , or resource in corresponding graph resources. Then cost of processing IR in RoG can be illustrated asWe convert the resource types in hopes of reducing the scale of resources when transferring IR in fog computing systems, aiming to balance resources load in a given wireless network. We assume that bandwidth of some nodes is enough in a period of time. We transmit IR to obtain storage and computation resources of other nodes through consuming bandwidth, aiming to satisfy users’ general requirements. As shown in Table 7, let C_{transD}, C_{transI}, and C_{transK} indicate the atomic cost of transmitting unit , , or , respectively. Cost of transmitting IR can be illustrated as
4.3. Calculation of Users’ Investment Benefit
We optimize the performance of processing resources through converting the type of resources and rational allocations of infrastructures in Internet of Things. According to previous subsections, we calculate the total cost of processing IR in fog computing applications by using our proposed mechanism. Total_Cost denotes the total cost of this resources processing service, which can be illustrated as Let denote inquired user investment; then corresponding to each resource combination can be illustrated aswhere represents the atomic investment that can be obtained through data training. Different investment programs correspond to different benefit ratios (R_{e}). R_{e} is used to measure the ratio of performance over investment, which is illustrated as Then we compare and of each program with and to determine whether the condition “> &<” is satisfied. Let R_{e0} be equal to the current when is greater than . Algorithm 1 describes the specific process of investment driven resource processing approach.

5. Experiment
The goal of our experiments is to evaluate the proposed processing optimization approach towards typed resources. We first established a small wireless network as our simulation environment with discrete illustration [29]. We observed and analysed user investment benefit (R_{e}) of six nodes in this system. Then, we measured some necessary network indicators to illustrate effects of using the proposed mechanism to optimize resources processing. Finally, we compared our proposed IoT_TOM with a conventional mechanism.
5.1. Experiment Setup
As shown in Figure 6, we first established a small real wireless network with fourteen nodes. These nodes consisted of different kinds of Arduino MCUs. Six nodes of them were equipped with ESP8266 WIFI modules and some sensors for collecting environment resources. As shown in Table 8, we set up some necessary input parameters that include senor nodes and energy model [30]. We deployed these nodes in four subareas in an area of 500 m 400 m.

5.1.1. Experiment Procedure
We aimed to illustrate the feasibility of our approach through providing users resources processing services. The proposed mechanism allowed each node to finish storage, computation, and transmission tasks. We assumed that a user demanded to observe environmental resources of this real wireless network anywhere. For convenience, we assigned values to some necessary parameters in the equations, such as , , and . We evaluated the performance of the proposed mechanism in terms of investment benefit and F value. The running time is ten minutes. In our experiment, we used some rules to process collected data into three resource collections of , , and . For example, photosensitive sensors collected resources of light intensity, which were , 4, 8, 16… . We processed these resources into resources in forms of (x=). Table 9 shows an example for classifying typed resources in a practical environment.

5.1.2. Statistics Analysis
For convenience, we only considered six resource combinations to illustrate the feasibility of the proposed mechanism. We estimated and after calculating , , , and . As shown in Figure 7, points surrounded by the red box on the curves indicate our recommended resource combinations. In these resource combinations, users are able to acquire resource services of maximum processing performance with minimum investment. In our experiments, the proposed mechanism dynamically recommended different resource combinations for different network environments to meet user demands better. Figure 7 shows that No. 6 of node A, No. 4 of node B, No. 5 of node C, No. 2 of node D, No. 4 of node E, and No. 2 of node F are final recommended resource combinations.
(a)
(b)
(c)
(d)
(e)
(f)
Furthermore, we measured some necessary parameters of the proposed IoT_TOM including F_{ij}, l, and N_{i} in order to observe the state of this wireless network. As shown in Table 10, FR_{wait} of link AF is greater than that of other nodes. Storage capacity of node F is 4 Mbit, which is insufficient to store 7 Mbit IoT resources. Therefore node F consumed more bandwidths to obtain enough storage resources compared with other nodes. On the whole, some nodes consumed computing capability to obtain enough storage resources. Some nodes consumed storage capacity to obtain computing resources. Some nodes consumed bandwidth to obtain storage resources. In order to evaluate the performance of IoT_TOM, we estimated BE_{buse} and BE_{wait} which are 0.36 and 0.28, respectively. F value reflects resource flow capacity distribution. In this experiment, we calculated F value according to (10). The calculated F value is 0.34, which means that the proposed mechanism effectively allocated network resources through converting resource types. Table 10 shows that IR_{ban} and FR_{wait} of most nodes are smaller than 0.5, which is not the best result in terms of network parameters intuitively. In order to illustrate advantages of our proposed approach, in next subsection, we compared our proposed mechanism with a conventional mechanism under the same resources scale.

5.2. Comparison with Other Methods
We compared the proposed mechanism with a conventional bandwidthallocationbased mechanism. We used the performance of traditional bandwidthallocationbased mechanism as the baseline for comparison. We modelled this simulation on the basis of NSGAII which is a modified version of NSGA (Nondominated Sorting Genetic Algorithm) released in 1994 used to optimize multiobjectives [31]. The objective function is illustrated asIn (20), is a parameter that contributes to the scale of irs_{D}. And six restrictive functions are as follows:We calculated user investment benefits of these two mechanisms. Figure 8 shows the values per node at corresponding investment to illustrate benefit difference between these two methods. In the simulation, we assigned values to parameters in the equations, such as and . But the actual value of each parameter should be obtained through data learning. According to previous experiment results, we find that investment benefit of our proposed method is smaller compared to conventional method under the same investment. The results show that the proposed method performs better when providing resources processing services.
In order to reflect the performance of processing resources in this fog computing application compared with the conventional method, we analysed F value of a given wireless network. Figure 9 shows F value of two mechanisms under different investments. The simulation results indicate that the bandwidthallocationbased method demands fourteen units cost to achieve objective F value. F value of the proposed method is smaller compared with the conventional method.
We used a social network to illustrate the impact of network size on performance of IoT_TOM. The network contains 1133 nodes and 10903 edges, which is from Datatang [32]. We used a sample set of 862 nodes and 1932 edges including five given areas. This simulation was conducted on a Lenovo ThinkPad E431 desktop with a 2.50 GHz Intel core i5 CPU and 8G RAM, running the pycharm Edit on Windows 10 operating system. As shown in Figure 10, (6, 12) represents that the number of nodes is 6 and the number of edges is 12. The results show that the bigger the scale of a given wireless network is, the better the distribution of network resources performs in a certain range.
6. Related Work
The dynamic reconstruction of computation and storage resources not only improves the utilization of resources but also simplifies management. Some of the workloads that use common resource computing and storage technologies can handle the current cloud system to avoid saturated clouds [33]. However, the cloud computing paradigm is not suitable to solve the problem of unbalanced distributions of network resources in the Internet of Things. We argue that it is necessary to consume bandwidth to transmit resources between IoT nodes, aiming to obtain storage and computation resources of other nodes in order to satisfy user demands. In [34], the authors proposed a distributed multilevel storage (DMLS) model to solve the problems of restricted computation, limited storage, and unstable network. A wirelessbased Software Defined Mobile Edge Computing (SDMEC) provided support for autoscaling network storage resource based on the network demand [35]. It is important to dynamically allocate bandwidth in fog computing application. In [36], the authors proposed a bandwidth allocation scheme based on collectable information. An integrated resource allocation method shared the limited bandwidth among multiple wireless body area networks (WBANs) [37]. In [38], the authors studied the tradeoff between the latency and reliability in task offloading to mobile edge computing (MEC). We argue that it is important to achieve global optimization over a wireless network in forms of converting the resource types. The bandwidths of a given wireless network are expensive in fog computing, which makes it a challenge to reduce latency of accessing resources. In [39], the authors proposed an efficient software defined data transmission scheme (ESDDTS) to maximize data transmission throughput and consistently maintain a flow’s latency. T. Taleb et al.’s proposed scheme enforces an autonomic creation of MEC services to allow anywhereanytime data access with optimum Quality of Experience (QoE) and reduced latency [40]. A hierarchical model composing data, information, knowledge, and wisdom is usually represented in the form of a pyramid [41]. We divide resources in the Internet of Things into data, information, and knowledge. We consider converting resource types to improve user investment benefit in a business value driven manner. We proposed a threetier resource processing architecture based on Data Graph, Information Graph, and Knowledge Graph, aiming at optimizing storage, transfer, and processing typed resources.
7. Conclusions and Future Work
Physical services in the Internet of Things are heterogeneous, largescale, and resourceconstrained. Fog computing makes IoT devices more intelligent and saves communication bandwidth. But distributions of network resources in fog computing applications are not balanced. We proposed to convert resource types to deal with this problem. Based on different resource combinations that feature a better user investment benefit, conversions between resource types change the resource scale. In order to satisfy users’ general requirements, we consider transmitting typed resources to obtain enough storage and computing resources of other nodes through consuming bandwidth. On the whole, optimized processing of typed resources with storage and computing collaborative adaptation effectively improves the ratio of performance over investment. Meanwhile, processing resources in our proposed threetier architecture of Data Graph, Information Graph, and Knowledge Graph optimizes spatial and temporal efficiency. The proposed mechanism achieves global optimization over a wireless network to provide costeffective resource processing services. According to simulation results, users invest minimum costs to obtain maximum performances of processing IoT resources. Generally, our proposed mechanism keeps most fog computing systems in a stable state, which is important for improving the user experience.
In the future, we will continue our study of resources management to improve performance of processing in a business value driven manner and conduct further experiments. We will also explore machine learning approach to optimize processing of resources including data, information, and knowledge.
Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Acknowledgments
This paper is supported by Hainan Key Development Program (no. ZDYF2017128), NSFC (no. 61662021, no. 61363007, and no. 61502294), and the IIOT Innovation and Development Special Foundation of Shanghai (no. 2017GYHLW01037).
References
 R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, “Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility,” Future Generation Computer Systems, vol. 25, no. 6, pp. 599–616, 2009. View at: Publisher Site  Google Scholar
 M. H. U. Rehman, V. Chang, A. Batool, and T. Y. Wah, “Big data reduction framework for value creation in sustainable enterprises,” International Journal of Information Management, vol. 36, no. 6, pp. 917–928, 2016. View at: Publisher Site  Google Scholar
 M. H. Ur Rehman, P. P. Jayaraman, S. Ur Rehman Malik, A. Ur Rehman Khan, and M. M. Gaber, “RedEdge: A novel architecture for big data processing in mobile edge computing environments,” Journal of Sensor and Actuator Networks, vol. 6, no. 3, article no. 17, 2017. View at: Publisher Site  Google Scholar
 W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge Computing: Vision and Challenges,” IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, 2016. View at: Publisher Site  Google Scholar
 I. Tomkos, L. Kazovsky, and K.I. Kitayama, “Nextgeneration optical access networks: Dynamic bandwidth allocation, resource use optimization, and QoS improvements,” IEEE Network, vol. 26, no. 2, pp. 4–6, 2012. View at: Publisher Site  Google Scholar
 M. Satyanarayanan, “The emergence of edge computing,” The Computer Journal, vol. 50, no. 1, pp. 30–39, 2017. View at: Publisher Site  Google Scholar
 Z. Ma, Q. Zhao, and J. Huang, “Optimizing bandwidth allocation for heterogeneous traffic in IoT,” PeertoPeer Networking and Applications, vol. 10, no. 3, pp. 610–621, 2017. View at: Publisher Site  Google Scholar
 Y. Ito, H. Koga, and K. Iida, “A bandwidth reallocation scheme to improve fairness and link utilization in data center networks,” in Proceedings of the 13th IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016, pp. 1–4, March 2016. View at: Google Scholar
 S. K. Sharma and X. Wang, “Live Data Analytics with Collaborative Edge and Cloud Processing in Wireless IoT Networks,” IEEE Access, vol. 5, pp. 4621–4635, 2017. View at: Publisher Site  Google Scholar
 K. Staniec and M. Habrych, “Telecommunication platforms for transmitting sensor data over communication networks—state of the art and challenges,” Sensors, vol. 16, no. 7, article no. 1113, 2016. View at: Publisher Site  Google Scholar
 X. Wu, S. Zhang, and A. Ozgur, “STAC: Simultaneous Transmitting and Air Computing in Wireless Data Center Networks,” IEEE Journal on Selected Areas in Communications, vol. 34, no. 12, pp. 4024–4034, 2016. View at: Publisher Site  Google Scholar
 C. Yin and C. Wang, “The perturbed compound Poisson risk process with investment and debit interest,” Methodology and Computing in Applied Probability, vol. 12, no. 3, pp. 391–413, 2010. View at: Publisher Site  Google Scholar  MathSciNet
 C. Yin and Y. Wen, “An extension of PaulsenGjessing's risk model with stochastic return on investments,” Insurance: Mathematics and Economics, vol. 52, no. 3, pp. 469–476, 2013. View at: Publisher Site  Google Scholar
 P. Li, M. Zhou, and C. Yin, “Optimal reinsurance with both proportional and fixed costs,” Statistics & Probability Letters, vol. 106, pp. 134–141, 2015. View at: Publisher Site  Google Scholar  MathSciNet
 D. Hua and L. Zaiming, “Optimal reinsurance with both proportional and fixed costs,” Applied MathematicsA Journal of Chinese Universities, vol. 27, no. 2, pp. 150–158, 2012. View at: Google Scholar
 Y. Wang and C. Yin, “Approximation for the ruin probabilities in a discrete time risk model with dependent risks,” Statistics & Probability Letters, vol. 80, no. 1718, pp. 1335–1342, 2010. View at: Publisher Site  Google Scholar
 W. Sun and L. Peng, “Observerbased robust adaptive control for uncertain stochastic Hamiltonian systems with state and input delays,” Lithuanian Association of Nonlinear Analysts. Nonlinear Analysis: Modelling and Control, vol. 19, no. 4, pp. 626–645, 2014. View at: Publisher Site  Google Scholar  MathSciNet
 A. Brogi and S. Forti, “QoSaware deployment of IoT applications through the fog,” IEEE Internet of Things Journal, vol. 4, no. 5, pp. 1185–1192, 2017. View at: Google Scholar
 J. Hong and V. O. Li, “Optimal resource allocation for transmitting network information and data in wireless networks,” in Proceedings of the IEEE International Conference on Communications, ICC '10, pp. 1–5, 2010. View at: Google Scholar
 C. Zins, “Conceptual approaches for defining data, information, and knowledge,” Journal of the Association for Information Science and Technology, vol. 58, no. 4, pp. 479–493, 2007. View at: Publisher Site  Google Scholar
 Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu, “Learning entity and relation embeddings for knowledge graph completion,” in Proceedings of the in Proceedings of the TwentyNinth AAAI Conference on Artificial Intelligence, pp. 2181–2187, Austin , Texas, USA, 2015. View at: Google Scholar
 J. Pujara, H. Miao, L. Getoor, and W. Cohen, “Knowledge Graph Identification,” in Proceedings of the 12th International Semantic Web Conference, The Semantic Web  ISWC '13, vol. 8218, pp. 542–557, 2013. View at: Google Scholar
 O. Deshpande, D. S. Lamba, M. Tourn et al., “Building, maintaining, and using knowledge bases: A report from the trenches,” in Proceedings of the 2013 ACM SIGMOD Conference on Management of Data, SIGMOD '13, pp. 1209–1220, 2013. View at: Publisher Site  Google Scholar
 Y. Duan, L. Shao, G. Hu, Z. Zhou, Q. Zou, and Z. Lin, “Specifying architecture of knowledge graph with data graph, information graph, knowledge graph and wisdom graph,” in Proceedings of the 15th IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2017, pp. 327–332, gbr, June 2017. View at: Publisher Site  Google Scholar
 L. Shao, Y. Duan, X. Sun, Q. Zou, R. Jing, and J. Lin, “Bidirectional value driven design between economical planning and technical implementation based on data graph, information graph and knowledge graph,” in Proceedings of the IEEE International Conference on Software Engineering Research, Management and Applications, pp. 339–344, 2017. View at: Google Scholar
 L. Shao, Y. Duan, X. Sun, H. Gao, D. Zhu, and W. Miao, “Answering Who/When, What, How, Why through Constructing Data Graph, Information Graph, Knowledge Graph and Wisdom Graph,” in Proceedings of the The 29th International Conference on Software Engineering and Knowledge Engineering, pp. 1–6. View at: Publisher Site  Google Scholar
 L. Shao, Y. Duan, X. Sun, Q. Zou, R. Jing, and J. Lin, “Bidirectional value driven design between economical planning and technical implementation based on data graph, information graph and knowledge graph,” in Proceedings of the 15th IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2017, pp. 339–344, gbr, June 2017. View at: Publisher Site  Google Scholar
 D. Kong, L. Liu, and Y. Wu, “Best approximation and fixedpoint theorems for discontinuous increasing maps in Banach lattices,” Fixed Point Theory and Applications, vol. 2014, no. 1, pp. 1–10, 2014. View at: Publisher Site  Google Scholar
 Y. Dong, L. Song, M. Wang, and Y. Xu, “Combinedpenalized likelihood estimations with a diverging number of parameters,” Journal of Applied Statistics, vol. 41, no. 6, pp. 1274–1285, 2014. View at: Publisher Site  Google Scholar  MathSciNet
 W. Sun, K. Wang, C. Nie, and X. Xie, “Energybased controller design of stochastic magnetic levitation system,” Mathematical Problems in Engineering, vol. 2017, no. 3, pp. 1–6, 2017. View at: Google Scholar
 K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, “A fast elitist nondominated sorting genetic algorithm for multiobjective optimization: NSGAII,” in Parallel Problem Solving from Nature PPSN VI, vol. 1917 of Lecture Notes in Computer Science, pp. 849–858, Springer, Berlin, Germany, 2000. View at: Publisher Site  Google Scholar
 Datatang, http://www.datatang.com/data/796.
 S. AlonsoMonsalve, F. GarciaCarballeira, and A. Calderon, “Fog computing through publicresource computing and storage,” in Proceedings of the 2nd International Conference on Fog and Mobile Edge Computing, FMEC 2017, pp. 81–87, May 2017. View at: Publisher Site  Google Scholar
 J. Xing, H. Dai, and Z. Yu, “A distributed multilevel model with dynamic replacement for the storage of smart edge computing,” Journal of Systems Architecture, vol. 83, pp. 1–11, 2018. View at: Publisher Site  Google Scholar
 J. AlBadarneh, Y. Jararweh, M. AlAyyoub, M. AlSmadi, and R. Fontes, “Software Defined Storage for cooperative Mobile Edge Computing systems,” in Proceedings of the 4th International Conference on Software Defined Systems, SDS 2017, pp. 174–179, esp, May 2017. View at: Publisher Site  Google Scholar
 Y. Ito, H. Koga, and K. Iida, “A bandwidth allocation scheme to meet flow requirements in mobile edge computing,” in Proceedings of the 2017 IEEE 6th International Conference on Cloud Networking (CloudNet), pp. 114–5118, September 2017. View at: Google Scholar
 D.R. Chen, “A QoS Bandwidth Allocation Method for Coexistence of Wireless Body Area Networks,” in Proceedings of the 25th Euromicro International Conference on Parallel, Distributed and NetworkBased Processing, PDP 2017, pp. 251–254, rus, March 2017. View at: Publisher Site  Google Scholar
 J. Liu and Q. Zhang, “Offloading Schemes in Mobile Edge Computing for UltraReliable Low Latency Communications,” IEEE Access, vol. 6, pp. 12825–12837, 2018. View at: Publisher Site  Google Scholar
 E. Kim and S. Kim, “An Efficient Software Defined Data Transmission Scheme based on Mobile Edge Computing for the Massive IoT Environment,” KSII Transactions on Internet and Information Systems, vol. 12, no. 2, pp. 974–987, 2018. View at: Google Scholar
 T. Taleb, S. Dutta, A. Ksentini, M. Iqbal, and H. Flinck, “Mobile edge computing potential in making cities smarter,” IEEE Communications Magazine, vol. 55, no. 3, pp. 38–43, 2017. View at: Publisher Site  Google Scholar
 J. Rowley, “The wisdom hierarchy: Representations of the DIKW hierarchy,” Journal of Information Science, vol. 33, no. 2, pp. 163–180, 2007. View at: Publisher Site  Google Scholar
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Copyright © 2018 Zhengyang Song et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.