Nghiên cứu xây dựng mô hình ngữ nghĩa cho phép quản lý và truy vấn các thiết bị trong internet vạn vật

Nghiên cứu xây dựng mô hình ngữ nghĩa cho phép quản lý và truy vấn các thiết bị trong internet vạn vật Nghiên cứu xây dựng mô hình ngữ nghĩa cho phép quản lý và truy vấn các thiết bị trong internet vạn vật luận văn tốt nghiệp thạc sĩ

BỘ GIÁO DỤC VÀ ĐÀO TẠO TRƯỜNG ĐẠI HỌC BÁCH KHOA HÀ NỘI

- HOÀNG QUỐC HỒNG NHẬT

AcknowledgmentsFirst of all, I would like to send my sincere thanks to the lecturers in Hanoi University of Science and Tech-nology, as well as, the lecturers in the School of Information and Communication Technology Particularly,

I would like to send my special thanks to Dr Nguyen Binh Minh, Dr Vu Tuyet Trinh who have given meprecious instructions and experience to complete this thesis

I also would like to thank my family The support and encouragement from my family is always themotivation for me to move forward

Co-author declaration and confirmation

I, co-author with Hoang Quoc Hong Nhat in the article Multiple Peer Chord Rings Approach for Device covery in IoT Environment, confirm the contribution of the author including:

Dis-• Contribute to the design and develop Multiple Peer Chord Rings architecture

• Implement simulation environment and perform experiments

• Write the draft of the article

I completely agree to the author Hoang Quoc Hong Nhat use this article for the purpose of research, writingand report Master Thesis at Hanoi University of Science and Technology

Hanoi, 15thof October, 2018Responsible author

Nguyen Binh Minh

Over the last years, in line with the development of information and electrical technologies, Internet of Thing(IoT) has moved from being a far vision to an increasing market reality However, how to manage things inthe manner of allowing the dynamic combination among different devices in diverse contexts is one of IoTchallenges today To address the problem, digital twin concept was put forward with a world that have theconvergence of the physical and the virtual Creating the virtual representation for physical assets or devices

is first and foremost step to furtherly realize a series of smart operations In addition, another IoT challenge

is that there are a lot of IoT solutions deployed everywhere However, towards a large scale smart area (e.g.building, city, or country), the key requirement is to integrate the solutions with many smart contexts (e.g.health care, disaster alert, or resource monitor) together In this study, we propose an overlay managementarchitecture based on Chord algorithm to manage physical things on a dynamic digital representation layer.The architecture thus is a combination of multiple Chord rings, and each of them is an overlay network amongdevices in a smart context We also bring forward a multi-attributes management mechanism on the architecture

to effectively support semantic discovery with device attributes

1.1 Introduction 10

1.2 Scenario and Challenges 12

1.3 Goal and Assumption 15

1.3.1 Goal 15

1.3.2 Assumption 15

2 Background 16 2.1 Chord 16

2.2 Related Work 17

2.2.1 Things Management and Discovery 17

2.2.2 Semantic in IoT 19

2.2.3 Attribute indexing scheme on Chord-based DHT 19

2.2.4 Our contributions 20

3 Designing Model 21 3.1 Overall 21

3.2 Multi-Chord-Ring 24

3.2.1 Architecture 24

3.2.2 Operational Mechanisms 26

3.3 Semantic Discovery 33

3.3.1 Attribute Management 33

3.3.2 Applying semantic discovery to ring management 38

4 Experiment and Evaluation 39 4.1 Key Management 39

4.1.1 Key Lookup 39

4.1.2 Semantic Query 41

4.1.3 Querying an attribute on a single ring 41

4.2 Node Mobility 42

4.2.1 Single Node Join 42

4.2.2 Shared Nodes Join 43

4.3 Ring Propagation 45

List of acronyms and symbols

IoT Internet of ThingsDHT Distributed Hash TableP2P Peer-to-Peer

SHA1 Secure Hash Algorithm 1SHA256 Secure Hash Algorithm 256MD5 Message-Digest algorithm 5URI Uniform Resource IdentifierRFID Radio Frequency IdentificationEPC Electronic Product Code

N Number of nodes in a ring

2m Key space size of a ring

2p Key space size of attribute segment

log Logarithm base 2

hardwareID Physical identifier of a thing

keyID Logical identifier of a thing in a ring

ringID Logical identifier of a ring

globalID Logical identifier of a thing over entire systemattributeID Logical identifier of a attribute

attributeAID Logical identifier of attribute A

attributeAKey Logical identifier of a thing having attribute A

List of Figures

2.1 Example of context intersections on a hierarchical model in a smart residence area 18

3.1 Convergence between Physical Layer and Virtual Layer 21

3.2 Multi-Chord-Ring Architecture 25

3.3 Example of shared-node-table 29

3.4 Example of neighbor-ring-table 29

3.5 Example about a query propagation on multi-chord-ring architecture 32

3.6 An example about broadcasting on Chord ring 35

3.7 An example about attribute distribution tree and thing attribute-based query 36

4.1 Mean key lookup costs on a single ring in three cases of key space sizes using MD5, SHA1, and SHA256 hash functions 40

4.2 Mean key lookup cost on 1024-node rings with increasing faulty node number 40

4.3 Success rate of key lookup on 1024-node rings with increasing faulty node number 40

4.4 Path length cost for a single attribute query on a single ring 41

4.5 Network load cost for a single attribute query on a single ring 41

4.6 Single node join cost 42

4.7 Shared-Node Lookup Cost 43

4.8 Cost of shared-node lookup in two cases 4 shared-nodes and 8 shared-nodes with different strides 44 4.9 Shared node join enhancement by indexing attribute ”ring” instead of using neighbor-ring-table 45 4.10 Experimental topology of a multi-chord-ring architecture 46

4.11 Network load cost of the testing ring-propagation operation in two cases of using ring-propagation and attribute-query mechanisms 47

is also well-known that the IoT solutions require the ability of very high scale and dynamic adaptation to beable to expand on large space such as towards the development of smart cities or even smart country in the nearfuture.

In order to build up a large scale IoT environment like that, ”how to manage things efficiently” is a mental issue that needs to be addressed Things management is required not only to identify things and monitorthe join/leave of them, but also to provide the ability to query them Doing this is not easy because there are adiversity types of things as well as ways to communicate among them, creating a heterogeneous environment.There are also dozens of different contextual IoT solutions deployed on the same hardware infrastructure Forexample, fire alarm system, HVAC-type system and health monitoring system would work together in a smartbuilding and they frequently require the involvement of the same devices such as temperature sensor, cellphone or intermediate gateway to perform their own operations or data forwarding This can be seen as theintersection among smart contexts, that makes building a general management systems become more complex

funda-In addition, ”semantic interoperability” is a matter of concern [18] This means that different IoT services(in other word smart contexts) can access and interpret unambiguously desired data among a vast amount ofdata collected from a huge number of things It is also useful for discovering exactly a thing by providing asemantic description which expresses the purpose of data it provides, where it is located, or the technical andnon-technical attributes it has Dealing with those challenges, digital twin is a state-of-art concept that helpsthings management become easier and enables semantic interoperability

Digital Twin concept [12] by John Vickers and Dr Michael Grieves was put forward with a world that

has the convergence of the physical and the virtual Since that time, many authors have tried to define theterm digital twin by introducing models that digitize physical entities, manufacture processes and interactionsamong real-world objects in a variety of fields In the aerospace industry [15], digital twin provides virtual rep-resentations of structural mechanics along with their lifecycle, that enables optimizations and decisions makingrelying on the real-time data collected from sensors [20] proposed a paradigm towards smart manufacturing bycontrolling and monitoring the real production process through logic interaction with their digital twin, and [4]approached digital twin in simulation aspect In summary, a digital twin can be considered as a digital copy of aphysical object to display the state of the object through the data associated with it In addition, it also provides

a simple digital representation of the object so that many individuals, including from a variety of locations, cancollaborate to monitor and control In this direction, we take advantages of digital twin concept to representIoT environment on an overlay network to mask the heterogeneousness and complexity at physical lever

In this thesis, we propose a novel approach to manage things and thus provide discovery mechanisms forthem in a semantic way based on Chord algorithm [19] In our proposal, we build a virtual representationlayer based on multiple Chord rings architecture [3] which represents a large IoT environment as an integra-tion of multiple intersected Chord rings Each Chord ring is seen as a digital twin of a smart context whereinterconnections among things involved in are represented by overlay links Physical things are categorized

in to gateways and sensors, and they are also converted to their own digital twins in form of virtual objectsrepresented by keys on the rings In this proposed virtual representation layer, we identify each ring, gateway,and sensor by assigning them a hashed ID under several features to distinguish each other We also proposeoperation mechanisms to manage join/leave processes of things and query them based on their keys Dealingwith semantic interoperability, we design an attribute management to query things based on their semanticattributes With the approach, we can solve the diverse and complicated links problem among things in realsmart environments and hence enables IoT systems to manage and control things efficiently The approach alsomeets scalability, flexibility, robustness and maintainability requirements for large and mash-up smart contexts.Our work distinguishes with existing studies in four main aspects: adopting Chord to build a multiple rings

in a single unified architecture to identify things, store and query their digital twin locally on gateway devicesthemselves, managing intersections among rings through their shared nodes, controlling joining and leave ofthings in the architecture, and enabling semantic discovery for things based on their attributes

The rest of the thesis is organized as follows In the remaining parts of this first chapter, section 1.2 presents

a typical IoT scenario, which puts motivations for our study in section 1.3 We present the used theoretical basisand discuss some related work in the next chapter 2 In chapter 3.1, we describe our general approach of thevirtual representation layer to address the motivation challenges The model designing is detailed in chapter 3

In this chapter, our proposed Multi-Chord-Rings architecture along with the main operational mechanisms tomanage things including naming, things join/leave, things lookup and semantic discovery mechanism Chap-ter 4 provides our experiments, gained results and observations with our proposals through simulation in order

to demonstrate the efficiency and feasibility of the proposal Finally, chapter 5 concludes and figures out somefuture directions

1.2 Scenario and Challenges

First of all, in order to have a simple and intuitive view of things management and semantic discovery in theIoT environment, a specific scenario is given

BKTown is designed for entire BK University community The town is located on a large campus with morethan 50 hectares area and is divided into two main zones including study plaza and resident village The studyplaza completes with teaching facilities, study clusters, research centers and several cafeterias, including 33buildings in this zone The resident village has 11 buildings, a large gardens and a supermarket BK Town alsohas two stadiums (indoor and outdoor) and an indoor swimming pool People can go inside BK Town through

5 gateways There is a bus system provided for residents to move within the campus and also to link to outsidepublic transportation stations (e.g city buses and trains) Towards environmental sustainability, BK Town isbuilt around a lot of trees By applying high technologies, clean energy is taken full advantage and reused Keypoints for green environment of BKTown includes: CO2 reduction, resource recycling, minimizing the amount

of net water consumption and environmental impact caused by chemical substances, using clean energies (e.g.solar, wind) BKTown has created an intelligent, social, cultural, and green environment for BK Universitystaffs and students

In BKTown, by setting up many industry indoor and outdoor access points, high quality wifi is available

in all spaces (e.g amphitheatres, studios, buses, rooms) that enables to withstand large connection amount.Besides wireless, wired links (e.g power line communication - PLC, twisted pair, optical fiber) are provided fornon-mobility (e.g street lights, barriers, ventilators) and plug-in-able devices (e.g desktop computers, printers,scanners, servers, cameras) Also in BKTown, hundreds of thousands of smart devices, actuators and sensors(such as televisions, micros, projectors, interactive whiteboards, polycom systems, presence sensors, motionsensors, temperature sensor, humidity sensor, electronic lock, lighting control, water sensors, door/windowopener/closer, air-conditioners, heaters, robots, sirens, smart meters, smoke detectors, and so on) are deployed

at suitable places of rooms, corridors, floors, lobbies, halls, parkings, roads, path, stadium, and so forth Due tothe low-energy features, many small devices cannot connect persistently and transmit continuously their data tothe Internet as well as cloud data services for analyses Instead of, the devices send their data and receive controlorders from other more powerful things via short-range communication mediums (e.g bluetooth, infrared).These powerful devices thus pre-process or transmit sensing data to global Those intermediate things could

be computers, smart TVs, smart phones, network routers, etc that have capabilities of computing, storingand transmitting data In other word, things in BKTown are connected together via ad-hoc and heterogeneousnetworks with different protocols In addition, mobility devices (e.g smartphone and wearable things) can join

in and leave dynamically BKTown intelligent environment as a part of entire system

Based on the full spread of network connections, it is possible to gather real-time data around and to makeautomatically smart behaviors in different contexts or even linking multiple contexts at the same time Anumber of applications have built for BKTown that are divided into groups: population (live and study), data,transportation, and environment The first group focus on improving all life aspects of those who work, study,live and visit BKTown The next service group uses information to gain a deeper understanding of BKTown

and residents The third group makes transport systems safer, faster and fairer Finally the last group createsustainable, greener environment.

In order to consider the smart operations of BKTown IoT environment, some examples are given as follows:Example 1: For concrete example, a lecture will be happened after next 30 minutes All lecture studentsand professor are notified via mobile application based on schedule (the application is assigned privilege).Tracking and directing functions thus show the shortest path go to the lecture hall for whom are inside BKTown.Users also can set the notification time before lecture (e.g 30, 60 minutes or more) In this context, if usersdrive electric car/bike to the town, the application will suggest free parking slots, where are the nearest lecturehall Car license plate identification, GPS location detector, street cameras and parking space sensors thus arecombined to help make the suggestion above Electrical system and other lecture hall equipments like light,projectors, micro, interactive whiteboards and so forth are turned on before lecturers The system is optimized

in the manner of saving power according to collected sensor data (temperature, humidity, light, presence and

so on)

Example 2: Another example about smart management system of BK Towns study plaza: whenever aresearcher and his/her colleagues need to arrange online meetings in a room, they use the resource managementsystem to register in advance The assigned meeting rooms are equipped with sensors to control lights, airconditioning, heating and status (on/off) of all the available devices, making it much easier for these researchers

to personalize the climate Meeting rooms are monitored by an automated temperature control system, whichautomatically turns on/off the heating/air conditioning before the meeting and similarly after the meeting Thesensors adapt the changes to match the time of the year, the outside temperature, outside light and to the need

of the presenter in automatic set-up of the appropriate parameters for all resources before the meeting Thisensures his/her personal preferences are met while the building automatically adjusts these preferences in anenvironmental friendly way

Example 3: Emergency disaster management also is one of typical examples for smart environment ofBKTown In the case of a fire appears in a resident village tower Many emergency services are composedand operate together including: activate automatic fire systems, fire alarms, alerting watchmen, polices, doc-tors, alerting hospitals, identifying optimal roads/routes to be assigned for emergency for each person, who arestaying inside the tower and so on All scripts for emergencies are built in advance with cooperation of manydifferent services To do that data collected from environment (e.g inside the tower) like temperature, smoke,video and so on are used to determine emergency locations and situations Besides,through wearable or mo-bility devices, residents are identified, and their health statuses are monitored in order to help optimize rescueprocesses Smartphones are brought inside the towers (i.e there is a high probability that the smartphone owner

is in the tower) are detected through they join in a network and smart contexts of the tower (e.g identification,room finding) In the case of emergency, notifications will be sent to the smartphone to assist the owner allneeded information In another aspect, traffic network for all the emergency services and sensors is tuned inthe manner of ensuring and prioritizing connectivity (e.g optimal the best routes), bandwidth (e.g extension).From the above scenario, there are several research challenges in building BKTown towards to smart and

green space Within our research theme scope, three challenges could be figured out as follows:

Challenge 1: Scalable architecture Although all IoT services as well as smart contexts presented in theexamples above provide diverse functionalities for a specific scope, they operate based on a common infras-tructure of BKTown, including sensors, actuators, electrical equipments, network, clouds and other analyticapplications There are also devices capability diversities in communication, computation, storage, energyconsumption, produced data types and formats, etc Moreover, under device viewpoint, in BKTown space, athing usually joins in diverse smart contexts (e.g temperature sensors in a room provide degree data for bothfire emergency and air conditioning services or even more at the same time) As a consequence, scalability isrequired to accept the complex and heterogeneous architecture of the device physical lever Thus, this leads tothe first motivation question: ”How to manage effectively things in the heterogeneous environment with thecontext intersection?”

Challenge 2: Elasticity Elasticity in BKTown IoT environment is manifested by increase or decrease

of resources, services, things, and users amounts depending on the circumstances In the examples describedabove, according to device traits (e.g mobility, power), the thing number in smart contexts is changeable

by time Thus, mobile and wearable equipments can become gateways/terminal devices and play the role oftransmitting/receiving data from several other things/services to/from the Internet while they are connecting

in a local network The mobility devices also can leave from these contexts when their owners move out toother spaces There are several questions rising here: ”How to identify things in the large IoT environment

of BKTown? Which mechanisms enable to manage the join and leave processes in/from smart contexts forthings? How to find as well as locate things in the complicated architecture of contexts?”

Challenge 3: Semantic interoperability Another problem of BKTown environment is to integrate lessly between things together at anytime, anywhere in any context From this viewpoint, semantics interoper-ability means that different IoT services (in other word smart contexts) can access and interpret unambiguouslydata collected from things at the same time Semantics of the data (for example, with domain knowledge) canprovide machine-interpretable way on what the data represents, where it originates from, how it can be related

seam-to its surroundings, who is providing it, and what are the quality, technical, or even non-technical attributes.However, related to features such as distributed, shared infrastructures of IoT, semantic interoperability in BK-Town also requires comprehensive solutions for organization, discovery, and management of data In this way,the semantics model must provide the capability of sharing, processing by various services across differentdomains simultaneously The following questions could be highlighted in order to clear the semantic inter-operability problem in BKTown space: ”Which structure is suitable to the semantic interoperability model?How to map the semantics interoperability model to physical things effectively? What are mechanisms fororganization, discover, management things based on semantics of data collected from BKTowns commoninfrastructures?”

1.3 Goal and Assumption

de-• Appropriate management mechanisms to identify, manage join/leave processes, and query devices

• Multi-attributes mechanism to represent devices and query based on their semantic attributes, that ports semantic discovery

We also offer some assumption for our work as follows:

• In IoT environment, there are various type of devices, including sensing devices, actuating devices, datacollecting, processing or transferring devices, low-end electronic devices, and so on To simplify, weassume that all devices belonging into two types, including sensor and gateway Sensors are low-resourcedevices with the main role is to sense surroundings (e.g thermometers, smoke detectors, cameras, etc.)

or electronic devices (e.g light bulbs, fans, doors, etc.), meanwhile gateways are high-resource deviceswhich can collect data from sensors, process data or transfer data to each other (e.g smart phones, smartTVs, Raspberry Pi, etc.) Sensors cannot directly interact to each others, they need connect to gateway

to forward their sensed data

• The term ”smart context” used in this thesis is defined flexibly in many cases It may represent a servicedomain or a geographic area, depending on services deployed at application level We assume thatgateways in a context can connect together via some kinds of machine-to-machine protocols such asCoAP [17], LWM2M [21] So that, our proposal management layer can leverage those protocols forcommunication among devices

• Semantic discovery is normally known to resolve a semantic query (e.g ”reducing the room temperature

to 25 degrees”) then query related devices to execute smart actions The first phase can be done with

an processing engine and a library of rules (e.g ontology, SPARQL), that returns a set of keywords which describe devices Then, at the second phase, desired devices are queried based on thosesemantic-keywords So that, we assume that semantic queries are processed beforehand at the formerphase, and our work aim to provide mechanism to manage and query devices at the latter phase based onresolved semantic-keywords In this thesis, we call those semantic-keywords as term ”attributes”

up to m entries pointing to its neighbor nodes, namely finger nodes, for efficient routing In this way, Chordprovides a lookup service Whenever a node wants to search a data item with key k, it calls f ind successor(k)function to route the request to successor(k) In this process, closest preceding node(k) function is performed

to hop to the closest preceding node to k in the finger table For a Chord ring sized N, a lookup request costsO(logN) hops

In addition, Chord is also robust in fault tolerance By letting each node keep a list of some its successors,namely successor-list, and backup its keys on those nodes, Chord ensures high success rate for queries whensome nodes are faulty In our recent research [9], we verified that the success rate is still close to 100% withthe failure rate in the ring up to 50%

According to [19], Chord has some features as follows:

• Load balance: Chord acts as a distributed hash function, spreading keys evenly over thenodes; this provides a degree of natural load balance

• Decentralization: Chord is fully distributed; no node is more important than any other This

improves robustness and makes Chord appropriate for loosely organized peer-to-peer cations.

appli-• Scalability: The cost of a Chord lookup grows as the log of the number of nodes, so even verylarge systems are feasible No parameter tuning is required to achieve this scaling

• Availability: Chord automatically adjusts its internal tables to reflect newly joined nodes

as well as node failures, ensuring that, barring major failures in the underlying network,the node responsible for a key can always be found This is true even if the system is in acontinuous state of change

• Flexible naming: Chord places no constraints on the structure of the keys it looks up; theChord keyspace is flat This gives applications a large amount of flexibility in how they maptheir own names to Chord keys

Due to these features, we make use of Chord as back-end to build up an overlay architecture and mechanisms

to manage and discover things in a large IoT environment

There are a lot of existing works related to this study In this section, we classify roughly these works intoseveral groups according to their approaches as follows

Management architecture There are three strategies of things management in the IoT environment, including:centralized, hierarchical and decentralized Firstly, the centralized management means that all informationabout things will be stored in a data center or a cloud storage, seen as a central node, and all discovery activitieswill be handled there This strategy allows for simplicity in design and management and low cost of discoverybecause the only matter to do is to ask the central for any things’ information However, this strategy hasgreat difficulty in scaling as the number of things and the number of queries increases, besides, the risk offailures due to network errors or server overload is considerably potential Secondly, the hierarchical strategy

is a widely used solution in many IoT platforms today The duty of IoT platforms is to provide a common andunified architecture to coordinate entire system operations In the small space, at logical management layer ofIoT platform, things often are modeled in a hierarchical form consisting of several levels of root, parent andchild nodes Then, they are queried by resolving their URI consisting of routing path to them Likewise but

in a contextual way, in [11] and [13], a hierarchical tree of smart spaces (e.g., country, region, city, streets,buildings, rooms), whose each node is a gateway, was introduced to model an IoT environment Nonetheless,

in larger environments, due to the heterogeneous of network, maybe each device or object has more than onelink to connect to others and some of them can participant in many contexts at the same time Figure 2.1illustrates an example of context intersections in a smart residence area, where black lines display hierarchical

links between things, meanwhile red and green zones group things into contexts of fire emergency and climateadjustment respectively In this way, the hierarchical architecture at logical management layer may not besufficient, especially in mash-up scenarios that contain millions of devices belonging to different IoT solutionsintegrated together The reason is that we cannot determine what and where are root, parent, and child nodes

to control and manage efficiently While some devices can be looked up quickly, other thing control dataflows will get stuck Finally, the decentralized architecture represents the natural connection of things in IoTenvironment There is no central node and each node is connected to some neighbors In unstructured overlaynetwork model, management would be very complex and discovery is performed by flooding a request messageuntil the target node is found or the Time To Live (TTL) is reached, expending a high cost without ensuringefficiency In contrast, structured overlay network model helps to reduce the complexity of management anddiscovery costs

L3

L3

L2 L2

L3

L4 L4

L1

L0

L3 L4 L3

L4

Figure 2.1: Example of context intersections on a hierarchical model in a smart residence area

DHT Chord-based overlay networks in IoT Using DHT overlay networks seems to be an useful solution forthings discovery in IoT [8] introduced and compared two architectures for distributed and scalable discoveryservices in EPCglobal network to share Electronic Product Code between trading partners, including a flat and

a hierarchical architecture While the former organizes all EPCglobal subscribing companies over the world

as nodes in a same Chord overlay network, the latter partition those companies into different rings depending

on their geographical locations Likewise, in [7], the authors also exploited two-level architecture with a DGT(Discovery Geographic Table) and a DLS (Discovery Location Service) to connect IoT networks that can bedeployed to very large scale P Liu in [14] proposed another architecture with Chord rings, which correspondwith two levels to identify objects using many different RFID standards Each node in the high level Chord ring

is a specific standard and it is linked to the low level ring, where nodes with the same standard are connectedtogether Effort presented in [22] brought forward a model, in which many unstructured peer-to-peer networksare linked together through super nodes These nodes thus are located in a Chord ring In other words, the supernodes are elements of Chord’s ring and the unstructured peer-to-peer networks will be linked and their peerscould be discovered based on the DHT identifications Those above studies, however, are primarily focused onbuilding high-level discovery services which serve IoT smart services through intermediaries, and no one aim

to enable things discovery right on gateways’ network itself Moreover, some of those studies manage thingsinto discrete groups or regions, which are linked by an intermediate DHT network This limits interoperabilityamong things in different groups.

Unlike the above approaches, our previous work in [3] proposed a multi peer Chord ring architecture,where things are grouped and managed by multiple Chord rings deployed on gateway themselves These ringscan intersect together by sharing one or many common nodes, and there is no central or high-level ring like theexisting studies and therefore our system achieves scalability without depending on any main ring However,

in this work, routing request among rings takes a long path length and discovering things based on semantic isstill not mentioned In addition, only one key space are used for the entire architecture, leading to limitations

in the integration and elasticity capabilities

There are some levels of approaches to semantic interoperability in IoT, including data level, service leveland device level At data level, information collected from the IoT network is often modeled using ontologysemantic-web for data analysis problems Service level focuses on categorizing and discovering services based

on semantic descriptions to increase the effectiveness of the coordination and interoperability between services

to operate smart operations And at device level, semantic interoperability aims to provide data access and directcontrol of devices for coordination within a service through semantic attributes of devices In [11], the authorsintroduced a semantic-based IoT service discovery mechanism by grouping things as a hierarchical tree ofsmart spaces, each smart space is embodied by a semantic gateway Each semantic gateway gathers informationfrom sensors, clusters and aggregates its content, then sends to its parent gateway Requests are processed bymatching with a routing table in each gateway which representing clusters In the same direction of partitioningIoT environment into smart spaces, the study proposed in [13] enables semantic interoperability at devicelevel where devices’ information and capabilities are represented with virtual representations of semantic-web.Within a smart space, each device is implemented an agent to transform data and interoperate others by querythe virtual representations maintained by a semantic information brokers (SIB) For discovering devices outside

a smart space, a device’s agent has to query SIB Resolution Service and ucode Resolution Server to reach theappreciate SIB However, as mentioned in the previous part, tree-like structures of smart spaces in those studieshave some characteristics that are not well suited to express intersecting contexts environment, moreover, queryhandling is may be overburdened by only one gateway for each smart space

Our solution in this problem is to represent attributes of things as keys on Chord rings, so that semanticdiscovery simply becomes key lookup on Chord rings, which can be carried out by any gateway, instead of acentral semantic gateway or an outside semantic server/service

In order to enable semantic discovery on multiple Chord rings architecture like in [3], we review some existingstudies aiming to distribute and query data based on attribute on Chord-based DHT overlay network The

study named Mercury proposed in [2] distributes each attribute on a separate DHT ring-shape overlay networkwhere Chord is used to partition the value ranges of attributes into intervals However, the value ranges oneach ring depend on the distribution of nodes, so they also provide light-weight sampling mechanisms foruniformly sampling random nodes to balance the nodes in the ring In contrast, Multi-Attribute AddressableNetwork (MAAN) in [5] only use a single Chord ring to support multi-attribute and range queries The authorspartition Chord ring into segments corresponding to different attributes and attribute values are mapped tocorrect segment via uniform locality preserving hashing The study also showed that the number of routinghops to resolve a multi-attribute range query is O(logN + N × si), where N is the number of nodes in ring and

si is the ratio of query range width in identifier space to the size of the whole identifier space In a hybridperspective, effort in [1] proposed an approach that uses multiple Chord rings in looking up multi-dimensionaldata In this way, data is distributed and labeled on many rings, each of them corresponds to a dimension.There is a central Chord ring, which integrates these dimension rings and thus provides search mechanism forentire system Structurally, with a central ring and many dimension rings attached on, this architecture includestwo levels of organization with different roles Further multi-attribute query mechanism can be found in [16]

It exploited underlying DHT overlay network to support complex queries such as multi-attribute and rangequeries by leveraging z-cure approximates technique to present data in multi-dimensions then transformingthem 1-dimension key space It is also applied in IoT scope but at service level, moreover, the 1-dimension keywould be change when a new attribute is added to data (i.e increase number of data dimensions), leading tomismatches in highly flexible IoT environments

Therefore, our approach in this thesis having some points similar to MAAN, each attribute of things isallocated into a segment of Chord ring, but number of segments can be changed dynamically to adopt theflexibility of attributes

As compared with the works described above, our contributions in this thesis include:

• Proposing a novel virtual representation layer based on multiple peer Chord rings to provide mechanisms

to organize, control interoperability and discover things through virtual objects distributed on gatewaysthemselves

• Proposing naming mechanism to identify things and rings in different key spaces

• Enhancing multi-Chord-ring mechanism for efficient discovery things among different rings

• Proposing attribute management mechanism to enable semantic discovery on the architecture

Chapter 3

Designing Model

Figure 3.1: Convergence between Physical Layer and Virtual Layer

In this section, we describe our general approach to deal with the challenges introduced in the section 1.3.How to manage effectively things in large-scale scopes One of the IoT typical features is heterogeneous.Concretely, in smart environments, there is a huge number of devices deployed together including gateways,sensors, other electrical equipments, network hubs, and so on Besides, there are also devices capability di-versities in communication, computation, storage, energy consumption, produced data types and formats, etc

As a result, developing IoT services on the physical device level often is extremely difficult and complicated

because developers should know almost all the device features before interacting with them To solve thisproblem, in this work, we propose to develop a logical layer with virtual objects, which are representations ofdevices at physical layer This design is brought forward based on the digital twin concept referred at [cite] Inthis way, the virtual objects store information of the devices such as descriptions, access ways, produced datatypes, sensing data attributes (e.g temperature, light intensity, CO2 concentration and so on) Using objects

on the virtual logical layer, IoT services have the ability of controlling devices simply without regarding to theheterogeneities of equipments at physical layer

For the logical layer as proposed above, using a centralized database may bring convenient for IoT ers However, the solution is against nature decentralized feature of IoT even database service provides effectiveoperation functions for virtual objects (e.g update, search, delete) In addition, the centralized database stillhas other weaknesses: the storage could be received many queries from IoT services at the same time Thus,the database is bottleneck of whole system Next, the storage often is deployed on cloud (to allow sharing dataamong different services more easily) that requires an Internet connection However, in IoT, not all devicescan connect directly to the Internet to transmit data Instead, the data usually travels on many intermediatehops (i.e gateways) using local network before arriving the cloud For those reasons, the storing solution forvirtual objects in our architecture is designed to operate in a strong distributed environment In this direction,all virtual objects data also is stored in a distributed way on physical devices

develop-At the physical layer, we assume that all devices belong to two types, including sensor and gateway Inwhich sensors are low-resource devices with the main role is to sense surroundings (e.g thermometers, smokedetectors, cameras, etc.), or electronic devices (e.g light bulbs, fans, doors, etc.), and so on Sensors may nothave the ability of storing and processing data, they simply send their sensing and status data to other devices(i.e gateways) Conversely, gateways are high-resource devices which have high capabilities of communica-tion, computation, and storage (e.g smart phones, tablets, etc.), lightweight computers (Arduino, Raspberry

Pi, Galileo, etc.), smart household appliances (TVs, refrigerators, air conditioners, etc.), and so on Because ofthe performance limitations of sensors, gateways are responsible for receiving, storing and preprocessing datatemporarily collected from sensors before forwarding to the clouds Similarity, in the virtual layer, we alsopropose that there are two kinds of virtual objects, including virtual sensor and gateway in accordance withsensor and gateway in physical layer

Usually, at physical layer, a number of devices coordinate in specific smart contexts At the proposed ical layer in our approach, we also groups virtual objects together based on the context that physical devicesparticipate in For instance, at physical layer, all air-conditioners and other related equipments (fans, ventila-tors) in a building work together in a smart climate context Thus, at virtual logical layer, all virtual sensors andgateways that represent devices joining in the smart climate context are grouped together into a virtual climatecontext To organize and manage the virtual context, we employ Chord ring algorithm [cite] In other word,the objects of virtual climate context are arranged into a Chord ring The overlay network provided by Chordhelp make virtual principle links, create naming and discovery mechanisms for the virtual objects (i.e sensorsand gateways)

log-The architecture of Chord overlay networks described above is called multi-Chord-ring organization log-Thereare three types of objects in the virtual layer, namely ring, node, and key Ring is a Chord network that takesshape from nodes Node represents a gateway of the physical layer at the virtual layer Key in the virtual layer

is a identifier for physical device (including both gateways and sensors) As presented above, an IoT context ismodeled in the virtual layer by a Chord network (ring) Chord protocol thus use these keys belonging to ring

to determine locations of virtual objects in the virtual layer

In this way, there are maybe intersections between these contexts that is formed through common devices,which participate in many different contexts at the same time Concretely, at physical layer, there are two inter-section cases, covering share of gateways and sensors among different contexts The common gateways thusenable to collect data and control many sensors that belong to several applications simultaneously Meanwhile,the common sensors also have the ability of providing data to many different IoT applications at the same time.For example, a wireless router could join in both climate and fire-alarm systems with the gateway role fortransmitting data from sensors to cloud services On the other hand, a sensor offers temperature information

in a room for both smart climate (adjusting appreciate temperature) and emergency-monitor (detecting fire)contexts In our proposed virtual layer, the common gateways act as share nodes among multiple Chord net-works at the same time While virtual objects of the common sensors will be stored separately and repeatedly

on Chord overlay networks This organization helps our system can control sensor objects explicitly and thecommon gateway objects are bridges between diverse Chord overlay networks (a.k.a different virtual contexts).Thus, a node can query other nodes that are located on any Chord networks Figure 3.1 illustrates the mappingway between devices in the physical layer and the virtual objects in the virtual layer

As described before, to identify objects, we propose a naming mechanism that allocates keys for both ways and sensors in our virtual layer The mechanism also provides identifiers for rings to distinguish themtogether Besides, to manage join and leave processes in/from a ring of sensors, we design sensor registra-tion/unregistration procedures based on Chord protocol to assign sensor virtual objects on an appropriate ringnode Meanwhile ring-updating function helps maintain link information on rings when a node joins or leavesin/from system To find objects the multi-Chord-ring architecture, a key-lookup mechanism is developed withtwo sub-functions, including shared-node lookup help to route key finding query to a shared node, and ringlookup to send the query from one ring to many other rings through found shared nodes The multi-Chord-ringarchitecture and its operational mechanisms is presented in detail at Section 3.2

gate-Dealing with the challenge of semantic discovery among devices and contexts, a naive solution is to use acentralized database server to stores all devices’ keys along with their related attributes such as temperature,humid, light, atmosphere ingredients, and so forth Thus, all discovery queries would be processed easily on theservers via searching function However, the centralized database always makes the bottleneck for IoT systemsand decreases its operation performance as presented before To resolve this, we propose a semantic discoverymechanism built on top of multi-Chord-ring architecture that allows querying a set of objects according to theircommon attributes In this direction, each virtual sensor object is declared its attributes (e.g location attributesare building name, floor or room numbers, data attributes could be sensing data types) On the multi-Chord-

ring layer, each attribute also is assigned an identifier, notated as attributeID Then, we partition rings intomany parts corresponding to those attributes, each part is sized 2p, in range of [attributeID, attributeID + 2p).

If a device has a attribute, the device is assigned an additional key, called attribute-key, within the specifiedrange of that attribute To be similar to the device’s identifier key (that is used to identify devices as mentionedabove), the attribute-key is also used to represent the device on the ring, however, the attribute-key features thedevice in term of the attribute that the device has In other word, a device now is represented by multiple keys,including one identifier-key and many attribute-keys those match the attributes of the device The virtual object

of the device is now also located on many appropriate nodes within many different key ranges of the attributes.This results that a set of devices which have a same attribute can be easily queried with a given attributeID

by examining the corresponding partition of the attribute Therefore, semantic discovery is enabled on themulti-Chord-ring layer We describe in details our proposed semantic discovery mechanism in Section 3.3

We start to describe the multi-Chord-ring architecture by considering how to organize things, including ways and sensors, in a context on a Chord ring With the purpose that things are managed through virtualobjects, and those are hosted by the gateways themselves, we deploy a node of the Chord ring on each gateway.Due to Chord protocol, those nodes can communicate through overlay links, that makes simple and transparentthe complex underlay physical routes between gateways In other words, when a gateway enters the smart con-text, it acts as a new node to join the ring, which was formed by nodes deployed on the other gateways in thecontext The node also initializes a virtual object with an identifier-key to represent its own gateway and hosts

gate-it by gate-itself In contrast, the way a sensor enters the smart context is not the same as the gateway Sensors can notjoin directly the ring as gateways for lack of communication capability In stead, they join the ring indirectlythrough the gateways they connect to Concretely, when a sensor connects to a gateway, the gateway createsfor the sensor a virtual object and assigns it an identifier-key Then, the gateway looks for the successor node

of the key to distribute the virtual object of the sensor on it In this direction, things management in a context is

a matter of distributing and looking up virtual objects based on their keys The figure 3.2a is a symbolic design

of a Chord ring where sensor s is connecting to node n and its virtual object is being stored by node n’.Dealing with the multiple smart contexts scenario as mentioned in the previous section, we can create manyChord rings, each of them manages a smart context However, in fact, there may be many common gatewaysand common sensors which participate in multiple smart contexts at the same time From overlay perspective,this means that the common gateways can simultaneously work as nodes of different Chord rings, and commonsensors’ virtual objects are stored on different Chord rings, resulting intersections between rings Therefore,

we propose an architecture, namely Multi-Chord-Ring, to represent those intersections, as well as, to managethings in intersecting smart contexts

Single NodeShared Node

(b) Things organization on multi-ring architecture

Figure 3.2: Multi-Chord-Ring ArchitectureThere are three types of objects that are used to represent the context, gateway and sensor as follows:

• Rings: (i.e Chord ring) represent smart contexts A ring is an overlay network using Chord DHTprotocol to distribute and lookup virtual objects of things in a smart context by nodes

• Nodes: are deployed on gateways and represent the gateways themselves Nodes in the same ring municate with each other through overlay links to distribute and look up virtual objects of thing in thecontext that the ring represents Nodes are categorized into single-node and shared-node Single-node is

com-a normcom-al node thcom-at is pcom-articipcom-ating in only one ring com-and shcom-ared-node is com-a specicom-al node thcom-at is tcom-aking pcom-art

in two or more rings simultaneously When a single-node joins other ring(s), it become a shared-node tobridge those rings

• Keys: represent things Each thing in a smart context is identified by a key on the corresponding ring.Based on keys, a virtual object of a thing is distributed on a certain node of the ring obeying Chord DHTalgorithm

Figure 3.2b illustrates an symbolic design of multi-Chord-ring architecture The shared-nodes are theintersections of the rings The common sensors, that connect to shared-nodes, are presented by many virtualobjects, each of which is managed on a different ring To maintain Chord protocol, each node on a ring has

a set of overlay links to its neighbor nodes including: a predecessor, a successor and a finger-table However,because a shared-node works in many rings, it has multiple sets of overlay links, each set is used for a particularring

A prominent feature of multi-Chord-ring architecture is the flexibility of rings interconnection Any nodecan be a shared-node, making it easy to extend a new ring Moreover, the number of shared-nodes between twolinked rings is not limited, as a result, a shared-node can leaves the system without affecting the link betweenthe two rings This feature leads the device replacement and the system extension more convenient, meetingthe demand of smart contexts