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INVITED PAPER

Special Section on Fusion of Network Virtualization/Softwarization and Artificial Intelligence towards Beyond-5G Innovative IoT Services

Geolocation-Centric Information Platform for Resilient
Spatio-temporal Content Management
Kazuya TSUKAMOTO†a) , Hitomi TAMURA††b) , Yuzo TAENAKA†††c) , Daiki NOBAYASHI††††d) ,Hiroshi YAMAMOTO†††††e) , Takeshi IKENAGA††††f) , Members, and Myung LEE††††††g) , Nonmember

SUMMARY
In IoT era, the growth of data variety is driven by crossdomain data fusion. In this paper, we advocate that “local production
for local consumption (LPLC) paradigm” can be an innovative approachin cross-domain data fusion, and propose a new framework, geolocationcentric information platform (GCIP) that can produce and deliver diverse
spatio-temporal content (STC). In the GCIP, (1) infrastructure-based geographic hierarchy edge network and (2) adhoc-based STC retention system
are interplayed to provide both of geolocation-awareness and resiliency.
Then, we discussed the concepts and the technical challenges of the GCIP.
Finally, we implemented a proof-of-concepts of GCIP and demonstrated its
efficacy through practical experiments on campus IPv6 network and simulation experiments.
key words: geolocation-centric, cross-domain data fusion, local production for local consumption

1.

Introduction

Advances of sensor devices and communication technologies are bringing the CPS (Cyber-Physical System)/IoT (Internet of Things) into a reality. Physical “things” are digitized by diverse and massive number of devices and theinformation generated by those things is collected into thecloud as “big data”. New insights are derived by applying
Manuscript received July 12, 2020.
Manuscript revised August 6, 2020.
Manuscript publicized September 11, 2020.
†
The author is with the Department of Computer Science andElectronics, Kyushu Institute of Technology (KIT), Iizuka-shi,
820-8502 Japan.
††
The author is with the Department of Information Electronics,
Fukuoka Institute of Technology (FIT), Fukuoka-shi, 811-0295
Japan.
†††
The author is with the Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST), Ikomashi, 630-0192 Japan.
††††
The authors are with the Department of Electrical Engineering and Electronics, Faculty of Engineering, KIT, Kitakyushu-shi,
804-8550 Japan.
†††††
The author is with the Department of Information Scienceand Engineering, Ritsumeikan University, Kusatsu-shi, 525-8577
Japan.
††††††
The author is with the Department of Electrical and Computer
Engineering, City University of New York (CCNY), USA.
a) E-mail: tsukamoto@cse.kyutech.ac.jpb) E-mail: h-tamura@fit.ac.jpc) E-mail: yuzo@is.naist.jpd) E-mail: nova@ecs.kyutech.ac.jpe) E-mail: hiroyama@fc.ritsumei.ac.jpf) E-mail: ike@ecs.kyutech.ac.jpg) E-mail: mlee@ccny.cuny.edu
DOI: 10.1587/transcom.2020NVI0003

AI processing to big data, helping human activity to harmonize with the physical world.
In order to efficiently perform data mining, the preparation of cleaned, parsed, and formatted data is essential[1]. However, since current IoT systems are largely isolated
within each individual vertical domains due to lack of standardization and interoperability, a horizontal IoT platform
enabling cross-domain data fusion becomes critical challenge in collecting huge amount of data [2]. Many countries including US, EU, and Japan are currently engaged in
the discussion of the requirements for cross-domain fusion.
A noteworthy effort exists in the collaboration of the crossdomain data for smart cities [3] sharing such data as environmental data (i.e., map and temperature) and user behavioral
data (i.e., trajectory and interests). Major challenges for the
cross-domain data fusion remain:(a) Privacy issues for user behavioral data(b) Incompatible policies and standards among IoT service
providers(c) Isolation of facilities and infrastructure between network operators.
For (a), the “user’s trajectory, interests, and preferencesdata” are regarded as a part of “personal data” in many countries. EU ratified new rules for personal data treatment (i.e.,
GDPR: General Data Protection Regulations) in 2016, andstarted already its operation since 2018 [4]. The GDPR permits the processing of the personal data only for the important public interest of the Union or of a Member State. Other
countries including Japan are anticipated to follow this trendwith the mandate to handle personal data “in the consideration of geographical area”. Although several efforts on data
platform such as FIWARE [5] have accelerated the collaboration on cross-domain data, they are still limited to “open(processed) data”. That is, none of existing platforms treats
the personal data. As a result, many developed countries
have recently started the discussion on the utilization of personal data and announced the concept of data platform totreat the personal data at the nation level. In general, thecurrent platforms treat personal data with user’s permission,
but are operated by different IoT service providers for a specific purpose as described in (b) above.
Note that some of environmental and personal data
suitable for the cross-domain data fusion such as map andresidential address are generated and managed in the geographical proximity (e.g., city and prefecture). Therefore,

Copyright c 2021 The Institute of Electronics, Information and Communication Engineers

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we advocate the “local production for local consumption(LPLC) paradigm” as a viable approach for cross-domain
data fusion. In this regard, Edge Computing [6] can function
as data anchor point for cross-domain data fusion. However,as Telecom companies have their own network infrastructures, the feasibility of data sharing in a geographical service area is low due to a monopolistic stance in practice. It
is also difficult for IT companies to place their own facilities to the Telecoms’ network edge (described in (c) above).
Consequently, the cross-domain data fusion even in a small
region is a remote possibility at the moment.
Therefore, we propose a new framework, geolocationcentric information platform (GCIP) that can produce diverse contents derived spatio-temporally from cross-domain
data fusion and deliver them to users within a small region.
Note that the contents produced by the GCIP are referred toas the spatio-temporal contents (STCs) in this paper.
The rest of the article is organized as follows. We firstreview the related works in Sect. 2. The concept of the GCIP
is discussed in Sect. 3. We then clarify the essential functions and discuss technical issues with the proposed GCIP in
Sect. 4. The experimental results on our proofs-of-concepts(POCs) implementation are explained in Sect. 5. Finally, we
conclude this article in Sect. 6.
2.

Related Works

In this section, we first briefly discuss the characteristics ofdata generated by massive sensor devices and review the IoT
systems for managing diverse data. Then, we introduce theliteratures focusing on edge cloud network architecture andutilization of AI technologies for efficient data management,
and finally identify remaining challenges for horizontal IoT
platform enabling cross-domain data fusion we focus in thispaper.
2.1

Big Data and Cross-Domain Data Fusion

As stated in the Introduction, the proliferation of sensor
devices and deployment of diverse sensing systems explosively increase the amount of data disparate in quality andvalue. S. Oteafy [7] notes that the big sensed data (BSD) potentially depletes communication resources and even makes
data analytics unmanageable. That is, BSD management isa pressing issue. Lin [8] mentioned that the main objective of IoT is to interconnect various networks for efficient
data collection, analysis, and management across heterogeneous networks. The authors advocated that a horizontal
IoT platform enabling cross-domain data fusion becomes
crucial (as in [2]) and smart cities is one of the most representative CPS/IoT applications. Celes [9] first listed a setof cross-domain data in urban environment (referred to asurban data), and then overviewed their advantages and limitations for crowd management. They showed the understanding of crowd situations based on urban data can bringmany opportunities such as urban planning, intelligent transportation systems, and traffic optimization.

2.2

Network Architectures for BSD Management

Qiu [10] and Huru [11] focused on the network architectures and platforms to manage the BSD collected from diverse domain fields. The authors pointed out that horizontal data processing architecture that integrates cross-domain
IoT data platforms becomes crucial for providing a varietyof convenient services for our future. Qiu [10] proposeda layered approach in which all of BSD are transmitted
to cloud servers by networking layer and processed there.
Huru [11] proposed BigClue for data processing across multiple IoT domains. The BigClue is also a cloud-based dataprocessing architecture that performs the complex processing demanded by cross-domain IoT data in the cloud, andmakes decision.
The work in [2] introduced a new architecture employing fog/edge computing as a support of cloud computing.
Since the fog/edge computing can act as a bridge between
end devices and cloud server, the architecture has the potential to alleviate the burden of data processing and traffic
congestion, and to deliver better delay performance, while
increasing service resiliency and scalability. Elkheir [12]overviewed the available processing paths throughout the
lifecycle in managing IoT data. However, the authors did
not mention how to design the concrete system architecture.
Moysiadis [13] surveyed ongoing research projects focusing
on distributed fog/edge computing for IoT services. In thispaper, the authors clarified that the storage capacity at eachlocation can be determined by the volume of data from thedevices with geographical proximity. Oteafy [14] focusedon the fog computing architecture with the characteristics
of lower access latency and limited geographical proximity
as a means of coupling of services with local IoT components in user-centric contexts. However, the authors only
presented many future directions along this line, but did not
discuss how to achieve the convergence of communicationand computing in the edge cloud IoT system for providinguser-centric services.
While the above works mainly focused on the architecture of the edge cloud network, consideration of theirheterogeneous characteristics such as computation capacity, caching capacity, and communication performance becomes essential for improving user QoE. Hao [15] proposed
the Smart-Edge-CoCaCo algorithm that uses joint optimization of the wireless communication model, the collaborative filter caching model in edge cloud, and the computation offloading model. Zhou [16] extended the architecture for convergence of communication and computing, andproposed a heterogeneous communication and hierarchicalcomputing network architecture, thereby improving capacity over conventional cloud-based IoT system. In order tomeet users’ QoS requirements in edge and remote cloud environment, Liu [17] presented optimal multi-resource allocation policy for computing and bandwidth resources for
IoT services. Furthermore, Cicconetti [18] proposed thecloud server-less distributed computing architecture (con-TSUKAMOTO et al.: GEOLOCATION-CENTRIC INFORMATION PLATFORM FOR RESILIENT SPATIO-TEMPORAL CONTENT MANAGEMENT

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sisting of edge servers only) and considered severe limitations in edge domain in terms of scalability, performance,and reliability, compared with the cloud domain. Wu [19]also employed the collaborative edge computing (server-lessarchitecture), but focused on an important issue of establishing trustworthiness among all edge servers. To address thisissue, the authors proposed a blockchain-powered framework, called BlockEdge that can build trustworthy edgecentric network and deliver trusted services.
2.3

Optimization Using AI Technology

Recently many researchers are paying attention to AI technology for optimizing not only communication procedures
but also data processing required in the edge cloud network architecture. Karunaratne [20] discussed key machinelearning (ML) technologies and analyzed past efforts applying ML to solve various design and management tasks
related to wireless mesh networks. Guo [21] handled task
offloading for achieving efficient edge computing and mentioned that existing researches cannot deal with varying situations. To solve this issue, the authors utilized supervised
decision tree based ML technology into task offloading at
the edge for designing an intelligent task offloading scheme.
Rodrigues [22] mentioned that “Modeling of edge cloud network with a variety of users, servers, and applications” involves inevitably many parameters for optimal configurationof communication, resource allocation, and so on. Therefore, the authors presented unsupervised learning approachto achieve near-optimal solution and provided helpful guidance which ML solutions can be applied to which problems.
Finally, Chen [23] surveyed applications where state-of-art
deep learning are used at the network edge, and described
example applications including not only in-network cachingas a network function but also IoT contents that need to be
fused and processed together in the spatiotemporal manner.
However, the authors did not clarify how to provide theseapplications in the edge cloud network architecture.
2.4

Cache Management on Edge Cloud Network

Several papers focused on edge caching in which edges withcache storages can solve several problems of edge cloud network architecture. Yao [24] overviewed the state-of-the-artedge cloud network and its advantages, and discussed how
the location of edge caching impacts on the cache performances. Moreover, the authors divided the caching process
into four functions: content request, exploration, delivery,
and update, but mentioned that none of the existing studies
takes the dynamic IoT contents fused by multi-source and/or
variable IoT data into consideration. That is, how to design the caching strategies by incorporating the dynamic IoT
contents still remains as one of important challenges. Sinky[25] also focused on in-network, content-centric caching approaches for smart cities. The information of network topology, content popularity, and users’ trajectory is assumed andutilized for efficient content caching and delivery, thereby

minimizing service disruption. However, other three functions (contents request, exploration, update) were not discussed. Pattar [26] mentioned that combining the IoT data
can build dynamic IoT contents and reviewed state-of-theart content search methods. The authors reviewed searchmethods in view of location, content, and spatiotemporalbasis; however, none integrated all three attributes which
can substantially facilitate content search in edge cloud network. The authors also mentioned that dynamic and realtime recognition and response to user queries becomes important to improve the QoE of users.
To the best of our knowledge, none of existing studies
proposes a new information platform that can provide essential functions [24] for dynamic IoT contents fused by IoT
data. This paper is the first concept paper of geolocationcentric information platform for dynamic IoT contents management that embraces essential functions, in the resilientmanner.
3.

Geolocation-Centric Information Platform (GCIP)

As stated earlier, some types of environmental and user
behavioral data suitable for the cross-domain data fusion
are generated and managed in the geographical proximity.
Therefore, we assume that public organizations such as local
governments (LGs) (e.g., municipal and prefectural offices)
are responsible for collecting and managing these data, andcontents providers (CPs) contracted with the LGs are responsible for producing STCs by fusing the cross-domain
data obtained from LGs. That is, each of edge cloud can bebuilt by the cooperation between LGs and CPs.
For example, some static environmental and personal
data such as map, residential address, and social security information are strictly managed by LGs. On the other hand,more dynamic data such as temperature and user behavior
information are collected to the cloud server to provide a
specific service. If these dynamic data can be collected at
the edge cloud serving a specific geographical area, we will
be able to fuse these abundant cross-domain data, thereby
producing new/diverse STCs. For example, the relationshipbetween the discomfort index (DI) calculated from temperature and humidity and human traveling flow can be used forthe estimation of wireless resource utilization.
As mentioned in Sect. 2, to utilize STC effectively,
four essential functions consisting of STC production, STC
discovery, STC delivery, and fault tolerance management
should be provided in the resilient manner. Therefore, inthis paper, we here propose a new information platform,
geolocation-centric information platform (GCIP) with edge
cloud for Spatio-Temporal Content (STC) management is asshown in Fig. 1. We first outline the concept of the proposed
GCIP (shown in Fig. 2) and briefly describe its two main
components: (1) infrastructure-based geographic hierarchyedge network (GHEN) and (2) adhoc-based STC retention
system (STC-RS). Also provided is the description of adaptive use of these two components.

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Fig. 1

Essential functions for resilient STC management on edge cloud.

Fig. 3

3.2

Fig. 2

3.1

Conceptual architecture of the GCIP.

Concept of the GCIP

Since STC strongly depends on the location and time where
and when it is generated, we advocate that the most effective exploitation of STC could be to deliver it directly to theusers who are at the location (local production for local consumption, LPLC), rather than serving it from remote cloudservers.
GCIP manages the interplay between GHEN and STCRS for STC management. The GHEN is a hierarchical edge
network handling variables in geographical proximity, while
STC-RS is virtually built over multiple vehicles located ina certain region by forming a wireless network in the adhoc manner. The GHEN provides a network substrate supporting “geolocation-aware” STC management, whereas the
STC-RS maintains the geolocation-awareness in a resilientmanner even when the infrastructure fails.
As a result, our proposed GCIP can achieve consistent
STC management (production, maintenance, and delivery),while maintaining not only geolocation-awareness but also
resilient nature. Furthermore, as a secondary advantage,
since GCIP can complete all the processing required for the
STC management within the edge network, congestion in
the core network can be mitigated.

Topology and allocated mesh IDs of GHEN.

Infrastructure-Based Geographic Hierarchy Edge Network (GHEN)

A geographical space is virtually divided into meshes basedon the latitude and meridians lines and the meshes is organized in a hierarchical structure as shown in Fig. 3. One 1stlevel mesh is 80 km by 80 km square area and one 2nd levelmesh is 10 km by 10 km square area; that is, one 1st levelmesh is equally divided into 64 2nd level meshes. For thenth level for (3 ≤ n ≤ 10), we define that one nth level meshis divided into four (n + 1)th level meshes.
A unique number, called mesh ID of variable length isallocated to each of meshes: 1st level mesh is given a 4 digits decimal ID and additional 2 digits for 2nd level meshes.
Note that in Japan, geographical areas and those IDs for 1st
and 2nd level meshes are pre-allocated by NTT DoCoMo
Open iArea [27]† . The ID length allocated to higher than
3rd level meshes increases in proportion to the increase in
the level. Figure 3 illustrates an example ID allocation. Assuming the area covered by one Wi-Fi AP 50 m by 50 m
square area, we define the minimum (10th level) mesh sizeas 39 m by 39m square area. This mesh ID allows us toflexibly identify the geographical spaces in handling STCs.
To identify the mesh ID, any devices in GHEN need toacquire its geolocation information. Widely implemented
GPS in smartphone and network devices such as cellular
Base Station (BS) and Wi-Fi AP conveniently provide geolocation information. Even without GPS, inexpensive sensor devices can obtain approximate geolocation informationfrom their APs and BSs of the infrastructure network.
However, in practical environments, some network facilities are forced to be located at non-optimal area due tothe limitation of space availability: AP in home, cellular BS,
and OLT (Optical Line Terminal) in a NTT central office. As
a result, the topology of the GHEN inherently forms an irregular mesh as shown in Fig. 3. Also, as a large number of†
Although Open iArea is here employed as just one example
of grid reference system, we can use the grid code defined by any
kinds of reference systems including JIS X0410 (Japan), MGRS
(USA), European Grid [28] for the GHEN.

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APs are already deployed especially in the urban area, several APs can be accommodated within the minimum mesharea (10th level mesh).
3.3

Adhoc-Based STC Retention System (STC-RS)

As stated in Sect. 3.1, the STC-RS allows us to maintainthe geolocation-awareness in a resilient manner, even wheninfrastructure network fails. To achieve this, large transmission bandwidth, large storage, and high-performance CPU
are required as in the edge cloud. A virtual edge cloud isestablished over the VANET, thereby maintaining functionsof STC management even when the GHEN becomes inactive due to a variety of network problems.
Modern vehicles (e.g., smart cars) have three remarkable features. First, data can be collected by and analyzed within individual vehicles owing to significant invehicle storage, battery power, and high-level computational resources in the near future. Second, emerging
vehicle-to-everything (V2X) communication can provide
high transmission capacity via such wireless standards asIEEE DSRC/WAVE and 3GPP C-V2X (Cellular V2X).
Third, ubiquitous presence of highly mobile vehicles operating all over the world can function as a substrate layer overwhich efficient data collection and dissemination can be provided.
In this study, we employ user’s neighboring vehicles
as vehicular edge clouds for STC management, where eachvehicle acts as an edge cloud. If the STCs are replicatedand managed in multiple vehicles existing in the geographical proximity, users can obtain their desired STCs fromone of the neighboring vehicles even when some vehicles
move away and/or break down, not to mention the failureof the GHEN. As a result, the STC-RS achieves the LPLC
paradigm for STC management in the resilient manner.
3.4

Interaction of GHEN and STC-RS

Basic concept of the hybrid system consisting of GHEN and
STC-RS is already described in our prior work [29]. The cooperation between GHEN and STC-RS is critical if GHEN
fails. If GHEN fails, STC-RS should first consider the geographical area of GHEN. For example, when a user moves
from the failed area to the neighboring mesh area, the handover of STC management from STC-RS to GHEN shouldbe performed according to the user mobility.
Since the information of vehicles’ mobility and density
in the wide area is available by the support of the GHEN, notonly the estimation of the status of the STC-RS but also the
preparation of the handover process of the STC management
described above can be performed effectively.
4.

Essential Functions and Technical Issues of the
GCIP

In this section, we first clarify the essential functions, andthen discuss technical issues with the proposed GCIP, which

engender main research items for GHEN and STC-RS.
4.1

Essential Functions• STC production: Integration of environmental data
and user behavioral data in a certain geographical proximity can produce diverse STCs which have been difficult to realize so far. However, since the data are gathered into cloud servers, the data collection at the edge
network beyond the border of network operators needs
to be negotiated among relevant network operators. We
assume here that the operators agreed already with data
sharing under limited conditions of geographical proximity to provide diverse STCs for users. Furthermore,user’s consent should be obtained in advance.
Since edge servers for data management and meshrouters are assumed to be isolatedly operated by LGs
and Telecom companies, edge router should replicate
the data and transfer the replicated data to the edge
server, while transmitting original data to the cloudserver. As a result, the data transmitted from certain geographical areas are collected at the edge server managed by LGs. In parallel, to produce appropriate STCs
flexibly, how the CP retrieves the necessary data fromthe edge server managed by LGs should be carefullyplanned.
• STC discovery: The data collected from massive IoT
devices will experience spatio-temporal change in viewof the amount and generation rate. As a result, theSTCs produced by the CPs by the cross-domain data
fusion is also inherently spatio-temporal. In such acase, users who want to get a STC on the edge server
cannot directly discover which CP has the desired one.
Therefore, methods for STC discovery should be properly designed.
• STC delivery: Since users may search desired STCs
from anywhere in the Internet, the associated locations
can be widely different. If a remote user wants to getsome STC for a specific geographical area, the granularity of the STC needs to be dynamically modified
according to its privacy policy that is differential privacy. After discovering the desired STCs, an effective
delivery method should be selected from among multiple candidates in response to both of the user locationand the demands for the STCs such as time constraints
and confidentiality.
• Fault tolerance management: Resilience is closely
related to reliability of networks when a subset of edge
clouds becomes inactive. GCIP service disruption may
happen due to edge network failures, temporary hardware failures, maintenance, power outage, access imbalance congestion, etc. Therefore, alternative methods that can maintain all of the STC management functions (production, discovery, delivery) should be considered even when none of edge clouds are available.
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Fig. 4

Physical location-aware communication (PLAC) method.

JUNO2 project [30].
4.2
4.2.1

Research Items in GHEN
Geolocation-Aware Data Collection

In order to collect geolocation aware data, we introduced in
Sect. 3 the mesh ID, so that all network devices such as meshrouter and terminals for GHEN can identify their own geolocation and corresponding mesh IDs to handle all of data containing the same mesh ID. We present a new data communication method that can consider geolocation, called physicallocation-aware communication (PLAC) method.
For smooth deployment of the new PLAC method, we
adopt the geolocation aware IPv6 compatible (GIP) addressformat [31], which is compatible with the current IPv6. The
GIP does not modify the network prefix (the upper 64 bits)
as show in Fig. 4. Instead, a mesh ID described in Sect. 4.2
is embedded in the lower 64 bits of IPv6 address because
the lower 64 bits can be freely determined while avoiding
address duplication (as in the stateless autoconfiguration).
Specifically, a mesh ID is placed in the beginning of thelower 64 bits and used as a part of network address. Since
the network address length matches the size of physical region, the PLAC is achieved by IP routing on the basis oflongest match by using the extended network address (prefix + mesh ID). This means that by choosing either IP or GIP
address, the users can indicate their willingness to agree or
not the geolocation aware data collection at mesh router in
the GHEN. Thus, the use of the GIP address by a user indicates her consent to geolocation aware data collection† .
4.2.2

Publish/Subscribe-Based Networked Data Preprocessing for STC Production

As stated in Sect. 3, we assume that local governments†
We assume that users need to choose GIP address on a perapplication basis when they agree with geolocation aware data collection at mesh routers in the GHEN. The actual operating procedures are supposed to be like a means of selecting an access network such as Wi-Fi or LTE on smartphone.

(LGs) such as municipal and prefectural offices are responsible for collecting and managing these data, and content
providers (CPs) contracted with the LGs are responsible for
producing STCs by fusing the cross-domain data obtained
from LGs. To achieve this, we consider that a data storage(DS) server and data fusion (DF) server(s) are deployed ineach mesh area, operated by LGs and CPs respectively. The
former is operated by LGs and the latter is by CPs. Since
the interplay of these servers yields the diverse STCs, thecommunication method between the DS server and the DF
server should be defined. The Publish/Subscribe model will
be used for this communication.
In this model, the mesh router publishes the collected
data to the DS server and the DS server stores/aggregates the
published data. Each of the DF servers, as the “subscriber”,
requests the stored information from the DS server. Datamining will be performed on the DF server to produce useful
STCs by exploiting an appropriate AI technology.
In general, intensive preprocessing including anonymous processing, Fourier analysis, labeling, and object extraction is required before AI processing, but many processing functions can be made common because of theirwide range of use cases. Therefore, we will consider a networked preprocessing method in which multiple DF servers
with different functions execute the preprocessing in order.
Since efficient preprocessing order can be designed by considering both the functions each DF server maintains andthe network load, the flexible functions of SDN (SoftwareDefined-Netwoking) technology is utilized for obtaining theinformation. Note that since we here focus on the edge network operated by LGs and CPs, the scalability problem in
SDN can be mitigated.
4.2.3

User/CP Requirement Matching-Based STC Search

Since users and sensor devices are numerous and diverse,
their data transmission timing, intervals, and the amountare also significantly different. Therefore, the retrieved
data from the DS server will change depending on not only
“time” but also “location”. Moreover, the types of processing may range from simple computations such as finding
the maximum/minimum or mean value over a set of sensed
data (e.g., movement speed and temperature) to more complex data aggregation, spatial/temporal correlation or filtering operations on the raw data. As a result, the STCs produced by the DF server will also exhibit the spatio-temporalcharacteristics.
The popularity of STCs requested by users also varies
spatio-temporally. Therefore, a search techniques tailoredto both location and contents are necessary. Although wide
variety of search techniques exist [26], studies still lack addressing the spatio-temporal characteristics in terms of the
STCs and users’ request (popularity). In the proposed GCIP,
since the DS server can collect the statistical information ofboth users’ and CPs’ requests, we proposed a new content
discovery method that evaluates the relevancy between topics specified user requests and topics representing IoT data

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used for creating STCs, called matching, and presents thecandidates for the desired STCs based on the relevancy [32].
4.2.4

Geolocation-Aware STC Delivery on GHEN

In the proposed GCIP, every IoT data or STCs need to be delivered through the mesh structure; that is, the delivery routeto the location of designated destination is automatically determined by following the mesh structure in which every
data must go upward through router(s) on higher level(s)and again go downward through router(s) on lower level(s).
This means that not only the amount of data traffic increases
but also the area suffered from the traffic is wider, in proportion to the extension of the length of delivery route.
Even if only the summarized information is sufficientfor distant users, transmitting detailed information may pose
privacy concern. The private information accessible for local users can be visible to distant users as well. To solve thisissue, we focus on the gap between transmitted granularity
and required granularity of data, and propose a new data delivery method in GCIP that can take the physical distanceinto account.
According to the mesh architecture presented in
Sect. 4.2.1, the number of packets that routers in the network process increases in accordance with the physical distance between the source and destination. We rather exploit
this to control data granularity. Specifically, we proposed anin-network processing mechanism on GCIP, which changes
information granularity when a communication data passes
each router [33]. In this mechanism, we put a same functionof data processing into every router and that function is executed for communication passing from lower-level mesh tohigher-level mesh. As the number of routers increases, theinformation granularity becomes coarse due to the increaseof the number of data processing. On the other hand, when
the data is transmitted to a very near location (e.g. within thesame mesh), data processing never happens.
4.3

Research Items in STC-RS

The STC-RS allows us to maintain the geolocationawareness in a resilient manner, even when infrastructure
network (GHEN) fails.
4.3.1

STC Production in High Mobility Environment

Although STC-RS is virtually built on a VANET to locallymaintain all the functions of STC management provided by
the GHEN, the VANET has significantly different properties
compared with the GHEN. For example, vehicles inherently
have high mobility and their density is time dependent. As aresult, the topology of the VANET envisioned in this system
becomes dynamic. Thus, the availability of the functions
for data analysis and/or processing for STC production will
also be time-varying. Therefore, how to produce the STC
by using the available functions at any point of time should
be considered.

4.3.2

STC Distribution in High Mobility Environment

As discussed in Sects. 3.4 and 4.1, to guarantee the GHEN’s
fault-tolerance and achieve physical location-based deliveryof STCs, we proposed the STC-RS using vehicles whereusers in the certain area can receive STC from a vehicle
spreading a data uniformly. However, since the STCs canbe produced over the VANET, the STC retention will become extremely difficult due to the vehicles’ high mobility
and time-varying VANET topology. Therefore, we proposed
the data transmission control method (DTCM) for achievingthat each vehicle periodically distributes STCs by wirelesscommunication within its specific area (defined as the target area) whose geographical size is to be determined by
the mesh structure of the GHEN [34]. We defined the coverage rate (CR) as a target metric. The CR is represented
TC can be received
by T otal area TSotal. Furthermore, the reduction ofarea
number of data transmissions is also our objective for theeffective use of wireless resources for the STC-RS.
The process of the DTCM begins when the vehicle receives the STC from the STC producer (e.g. DF server(s)on GHEN). Each vehicle sets data transmission interval d,which is notified by the STC producer whenever receiving
initial data (STC)† . Here, the subscript i indicates a uniquevehicle ID, i ∈ V, where V is the set of all vehicles, and thesubscript t indicates the sequence number of data transmission cycles (i.e., the number of d) after the first data reception. First, the vehicle sets a random backoff time s (< d)to avoid packet collisions. After s has elapsed, the vehicle
sets the transmission probability in the following procedure
to control the number of packet transmissions while maintaining coverage. Each vehicle in the target area estimates
the number of neighboring vehicles n(i,t−1) from the beaconstransmitted by vehicles every cycle t, which has to be set
smaller than d. Next, when the number of neighboring vehicles is four or more, the vehicle’s transmission range has thepotential to be completely covered by that of all neighboringvehicles. For example, when the neighboring four vehicles
are located to a vehicle’s north, south, west, and east (theideal arrangement), the vehicle’s transmission coverage area
is already completely enclosed by the neighboring vehicles.
Therefore, if n(i,t−1) < 4, the vehicles set transmission probability p(i,t) = 1 and always send the STC packet since it isdifficult to cover the entire target area with just the neighboring vehicles. On the other hand, if n(i,t−1) ≥ 4, the vehicle
determines the transmission probability based on the number of STC packet transmissions by neighboring vehicles toavoid packet collisions. In order to calculate the estimatednumber of STC packet transmissions m(i,t) by the neighboring vehicles in the t-th cycle, the vehicle uses the number ofreceived STC packets l(i,t−1) from the neighboring vehicles
in the last (t−1-th) cycle. m(i,t) is calculated by the following
equation:†

Note that we here assume a STC can be included in one
packet.

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m(i,t) = α ∗ l(i,t−1) + (1 − α) ∗ m(i,t−1)(1)where α indicates the moving average coefficient. Finally,
the vehicle determines p(i,t) based on the m(i,t) as follows:
β−l(i,t−1)(0 < m(i,t) < β)p(i,t−1) + n(i,t−1)

+1



p(i,t) = (2)p(m(i,t) = β)(i,t−1) p(i,t−1) − l(i,t−1) −β (m(i,t) > β)n(i,t−1) +1
where β is the target number of data transmissions around avehicle.
By determining the transmission probability of STC by
the DTCM, high delivery rate can be achieved while avoiding waste of wireless resources. Moreover, we have madeseveral extensions for simultaneous multiple STCs retention [35] and secure communication with other vehicles [36][37]. Especially, in high mobility environment, since the vehicles move out of the target area and move into the newtarget area, we have proposed a method for quick STC distribution and deletion [38] to limit the retention area of STC.
However, a new deletion method of the STC needs to be developed in the consideration of vehicle’s movement and the
STC’s target area.
5.

Experiments

In this section, we have conducted the experiments to show
the efficacy of (i) STC production on the GHEN and (ii)
STC distribution over the VANET.
5.1

STC Production on the GHEN.

The preliminary experiments were conducted on real campus IPv6 network [39]. In this experiment, we examined
whether the GHEN can collect the IoT data by exploiting
PLAC method, even when the data are generated in adjacentproximity but transmitted from different 10th level meshes(i.e., different subnets/prefixes). As shown in Fig. 5, eachof 10th level mesh networks has corresponding mesh router(R10 0 and R10 1) and an access network. Five end devices(D1 1, D1 2, D1 3, D2 1, D2 2) are developed by using
Raspberry Pi and equipped with different types of sensors.
These end devices are connected via Ethernet and transmit
sensed data periodically (every 5 minutes) throughout theexperiment.
For the second experiment, we examined the feasibilityof STC production on the GHEN by fusing the sensed datacollected in the geographical proximity. We consider the
discomfort index (DI) as a simple example of the STC. Here,
we assume that some of sensors in the geographical proximity send the temperature information to a specific cloudserver for work smart, while other sensors send the humidity data to a different cloud server for smart cities. Then,the DI is calculated from these two cross-domain data as“T − (0.55 − 0.0055RH) × (T − 14.5)” [40]. Note that T
indicates the mean value of air temperature and RH indicates the 5 minutes average of relative humidity because theFig. 5

Demonstration for IoT data collection.

sensor devices transmit their average data every 5 minutes.
The PLAC method allows three sensor devices (D1 1,
D1 2, D1 3) deployed in the mesh with ID 50303410 to forward their sensed data to R9 1 via their mesh router (R10 0).
Similarly, sensed data from D2 1 and D2 2 in a mesh with
ID 50303411 are forwarded to R9 1 via their mesh router(R10 1). Note that these 10th level meshes have different
network prefixes, i.e., different subnets. However, since theR9 1 is located at the same (9th ) mesh network, if the data
storage (DS) and data fusion (DF) servers are also locatedat the same 9th mesh network, all of the sensed data canbe collected from the mesh router. We here use ApacheKafka, which provides publish-subscribe based messaging
system [41], for communication between end devices andDF servers managing STCs. Since a broker is necessary formediation between producers and consumers, we deployeda broker on the 9th mesh network. For simplicity, the broker
functions as the DS server and the DF servers, and typical
Web API is used for indicating the STC produced in the broker.
Figure 5 also shows the STC generated at the broker.
Fig. 5 illustrates not only a geographical area where the STC
was produced but also a time series variation of DI value.
Since the GIP address includes a mesh ID, the broker canroughly identify the geographical area of end devices by analyzing the IPv6 header of the packets received from theend devices. Moreover, the broker can store the publisheddata, and thus the change in the DI value can naturally beshown. Through these experiments, we demonstrated thatthe proposed GHEN can produce STCs by using the PLAC
method.
5.2

STC Distribution over the VANET (STC-RS)

We evaluated our proposed method using the Veins combined network simulator OMNeT++ and the road traffic
simulator SUMO. As shown in Fig. 6, a road model on agrid of 2 km square with 200 m distance between each roadis used as the simulation area, and vehicles drive randomlyon this road at a 40 km/h. The communication range of vehicles, the beacon transmission interval, the data transmissioninterval d, α, and β are set to 300 m, 1 s, 5 s, 0.5, and 4,

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Fig. 6

Simulation topology.

respectively. These parameters were set to appropriate values for proposed method based on the evaluation of previous
works [34]. One STC is transmitted from the center of thetarget area (STC origin) at the start of the simulation and isretained by the vehicle for 100 s within a 750 m radius of
the target area. Note that we here set the radius of the target area to approximately three times (hops) of that of thecommunication range in order to show the effectiveness ofthe proposed retention method on relatively large area. Furthermore, to examine how the change in the node density
impacts on the retention performance, the number of vehicles is varied from 75 to 300.
We compared the proposed method with the naive
method (always with p(i,t) of 1), which can maintain highcoverage but not taking into account the consumption of
wireless resource. Figures 7 and 8 show the CR and thetransmission reduction rate (TRR), when the number ofvehicles is varied. Note that, the TRR is calculated by
T X num. o f the proposed method“(1 − T otal
T otal T X num. o f the naive method ) × 100”. In terms of
CR, the proposed method achieves more than 99% but isslightly lower than the naive method. On the other hand, we
confirmed that the TRR of the proposed method is approximately 70% lower than that of the naive method in highnode density. The results show that the proposed method canmaintain a high CR while significantly reducing the numberof data transmissions.
6.

Conclusion

In this paper, we advocate that “local production for local
consumption (LPLC) paradigm” is an innovative approachin cross-domain data fusion and propose the geolocationcentric information platform (GCIP) that can produce,
maintain, and deliver new/diverse spatio-temporal content(STC).
In the GCIP, we assumed that local governments (LGs)are responsible for collecting and managing data, and contents providers (CPs) contracted with the LGs are responsible for producing STCs by fusing the cross-domain dataobtained from LGs. CPs are also responsible for the development of the required functions for resilient STC man-

Fig. 7The CR vs. the total number of nodes (vehicles).

Fig. 8The TRR vs. the total number of nodes (vehicles).

agement in edge cloud. By integrating VANET-based STCRS with the infrastructure-based GHEN, we provide muchneeded resilience to the GCIP.
Technical issues of GCIP have been discussed with
its two cooperating components: GHEN, the geolocationbased hierarchical mesh and resilient VANET-based STCRS. Then, we showed the feasibility of the proposed GHEN
via a POC system, which implemented and demonstrated
effective data fusion of raw data obtained from different networks within GHEN by using the PLAC method. Practical experiments have been conducted on campus IPv6 network. Furthermore, we also showed the effectiveness of the
STC distribution by the proposed STC-RS through simulation experiments. Although the POCs demonstrated in thispaper were very limited, we have already started several researches on other technical issues described in Sect. 5, suchas Publish/Subscribe-based networked data preprocessing,User/CP requirement matching-based STC search, STC production/distribution in high mobility environment, and theway of GHEN and STC-RS interplay. We plan to report
performance of the proposed GCIP in the near future.

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Acknowledgments
This work has been supported by JSPS KAKENHI Grant
Numbers JP18H03234, the “Resilient Edge Cloud Designed
Network (19304),” the Commissioned Research of National
Institute of Information and Communications Technology(NICT), Japan, and in part by National Science Foundation(No. 603503991).
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Yuzo Taenaka
received the D.E. degree inInformation Science from Nara Institute of Science and Technology (NAIST), Japan, in 2010.
He was an assistant professor at the Universityof Tokyo, Japan, and is currently an associate
professor at Laboratory for Cyber Resilience,
NAIST since April 2018. His research interests
include information network, cybersecurity, distributed system, and software defined technology.

Kazuya Tsukamoto
received the D.E. degree in computer science from the Kyushu Institute of Technology (KIT), Japan, in 2006. From
April 2006, he was a JSPS research fellow at
KIT. In 2007, he was an assistant professor in
the Department of Computer Science and Electronics, KIT, and then has been an associate professor in the same department since April 2013.
His research interests include wireless network,
vehicular networks, IoT, edge computing, andgeolocation-based networking. He is a memberof the IEEE, and the ACM. He also serves as an associate editor of IEICE
Transactions on Communications.

Takeshi Ikenaga
received his D.E. degree in computer science from Kyushu Institute of Technology (KIT), Japan in 2003. Since
September 2011, he has been a professor in the
Department of Electrical Engineering and Electronics, Faculty of Engineering, KIT. His research interests include performance evaluationof computer networks and QoS routing. He is amember of the IEEE.

Hitomi Tamura
received the D.E. degree incomputer sciences and electronics from Kyushu
Institute of Technology (KIT), Japan in 2005.
From 2005 to 2007, she was a Post-Doctoral Researcher in KIT. From 2007 to 2008, she was a
Researcher in the Human Media Creation Center, Kyushu. Since 2011, she has been an Assistant Professor in Fukuoka Institute of Technology. She is a member of the IEEE.

Daiki Nobayashi
received the D.E. degreefrom Kyushu Institute of Technology (KIT),
Japan in 2011. From April 2010, he was a JSPS
Research Fellow. Since March 2012, he has
been an Assistant Professor in Department ofElectrical and Electronic Engineering, Facultyof Engineering, KIT. His research interests include network architectures and protocols. He
is a member of the IEEE.

Hiroshi Yamamoto
received D.E. degrees
from Kyushu Institute of Technology, Japanin ’06. From April ’06 to March ’10, he
worked at FUJITSU LABORATORIES LTD.,
Japan. From April ’10 to March ’15, he wasan Assistant Professor in Nagaoka University ofTechnology. Since April ’15, he has been an
Associate Professor in Ritsumeikan University,
Japan. His research interests include computer
networks, distributed applications, and sensor
networks. He is a member of the IEEE.

Myung Lee
received BS, MS from Seoul
National University and Ph.D from Columbia
University respectively. He is currently a professor with the Dept. of Electrical and Computer
Engineering, City University of New York. His
current researches include Secure V2X communications, Edge Cloud resource management,
IoT, machine learning, stochastic computing application to intrusion detection. He publishes
extensively in these areas and hold over 25 U.S.
and international patents. His research groupdeveloped the first NS-2 simulator for IEEE 802.15.4, a standard NS-2
distribution widely used for wireless sensor network research. Dr. Lee
co-received three best paper awards. He actively contributed to international standard organizations IEEE (TG chairs for 802.15.5 WPAN Meshand 802.15.8 PAC) and ZigBee. He is a past president of the KSEA.