STECEQL: A spatio-temporal constraint event query language for internet of vehicles

Đăng ngày 4/2/2019 3:55:27 PM | Thể loại: | Lần tải: 0 | Lần xem: 7 | Page: 8 | FileSize: 0.43 M | File type: PDF
STECEQL: A spatio-temporal constraint event query language for internet of vehicles. In STeCEQL, we use time interval as temporal model and grid map as spatial models. We give the syntax and operational semantics of STeCEQL based on the temporal and spatial model. Finally, our experiments illustrate the effectiveness of the operational semantics.
International Journal of Computer Networks and Communications Security
VOL. 2, NO. 12, DECEMBER 2014, 448–455
Available online at: www.ijcncs.org
ISSN 2308-9830
STECEQL: A Spatio-Temporal Constraint Event Query Language
for Internet of Vehicles
HUIYONG LI1, YIXIANG CHEN2 AND YUJING MA3
1, 2, 3 Software Engineering Institute, East China Normal University, Shanghai, P.R. China
E-mail: 1lihuiyongchina@126.com
ABSTRACT
In recent years, the complex event technology has been widely used in the monitoring and real-time
querying information of the internet of things. The internet of vehicles is a novel researching area of the
intelligent transportation systems, which developed with the technology of internet of things. It is different
from the traditional internet of things. There are a large number of moving objects and they will produce
large amounts of temporal and spatial information in the internet of vehicles. It becomes the core issue of
the complex event technology in the internet of vehicles, how to effectively represent and process these
information of the moving objects. We propose a novel temporal and spatial constraint complex event
query language STeCEQL for the internet of vehicles. In STeCEQL, we use time interval as temporal
model and grid map as spatial models. We give the syntax and operational semantics of STeCEQL based on
the temporal and spatial model. Finally, our experiments illustrate the effectiveness of the operational
semantics.
Keywords: Event Driven Architecture, Event Query Language, Internet of Things, Internet of Vehicles,
Grid Map, Mobile Systems.
1
INTRODUCTION
the
clock
technology,
position
technology
(e.g.
RFID, GPS), orientation sensors, speed sensors and
With
the
development
of
Internet
of
things
other sensors, users can very easily get the time,
(IoT)[1]and Cyber-Physical System(CPS)[2], many
position,
direction,
speed
and
other
information
new
methods
and
viewpoints
were proposed
to
about the internet of vehicles. However, there are a
solved the problem in the research of Intelligent
lot of mobile objects are moving very fast in the
Transportation
Systems
(ITS).
The
Internet
of
system and they instantly generate large amounts of
Vehicles
use
the
vehicle-mounted
electronic
data
in
the
moving
process.
It
is
a
very
big
sensing
device[3],
the
mobile
communication
challenge to store and process these data using the
technology,
the
car
navigation
system,
the
traditional database. Meanwhile, the traditional data
information
terminal
device
and
the
intelligent
mining techniques cannot monitor the information
network platform to contact each other between the
in time.
vehicles and the road, cars and trucks, cars and
To solve the above problems, many researchers
people, vehicles and urban. By these networks, we
have
applied
the
complex
event
technology
to
can effective monitor, schedule and management
sensor networks and the internet of things. The
the time, space, speed and other information of
complex event technology can filter the amounts of
vehicles, people and road.
data through the event query language into the
Internet
of
Vehicles
is
performance
crucial
events
concerned
by
the
users
[6].
When
a
system and
the
system’s
time
performance
and
concerned event occurs, the event based system can
spatio-temporal
consistence
are
the
key
of
the
real-time
or
near
real-time
process
these
system [4,5]. So it is very important to real-time
information according to the pre-defined rules. In
monitor
the
time,
space
and
other
context
the
event
based
system,
it
greatly
reduces
the
information of various components of the system
processing and storing load since the system only
during the running time. With the applications of
need
store
the
concerned
event
and
discard
449
Huiyong li et. al / International Journal of Computer Networks and Communications Security, 2 (12), December 2014
unwanted data. So the complex event technology
[13].
There are six base event operators: same
has a very wide range of applications in the internet
location,
remote,
sequence,
concurrency,
of things.
conjunction, and disjunction. And four complex
Since the existing complex event query language
event
operators:
same
location
and
sequence,
cannot effectively express the events’ mobile and
remote
and
sequence,
same
location
and
spatio-temporal
characteristics in the
internet of
concurrency, remote and concurrency. They have
vehicles. This paper presents a novel complex event
used SpaTec language in the monitoring system of
query language to internet of vehicles: STeCEQL
London bus and propose the system architecture
(spatio-temporal consistence event query language).
[14]. Jin Beihong has proposed a complex event
The
remainder
of
this
paper
is
structured
as
Query language:
CPSL
[15].
The
language
can
follows: section 2 introduces the related
works.
describe many temporal and spatial models and
Section 3 describes the temporal model and the
their relationships. In SpaTec language, the spatial
spatial model of the internet of vehicles. Section 4
model is a region with the central point. And in
presents the syntax and operational semantics of
CPSL language, the spatial models are points set
STeCEQL. Section 5 gives some simulation data
and convex polygons. These two complex event
examples to reasoning complex event expresses.
languages do not consider the direction information
The last Section concludes this paper.
of the spatial model.
We think that each object of internet of vehicles
2
RELATED WORK
can share the spatial information from geographic
information systems and they can access to the
An
event
query
language
is
a
high
level
global map. The direction information is important
programming language. A simple event express is a
in the system. Based on the above considerations,
specification of a certain kind of single events. A
we
propose
a
complex
event
query
language:
complex event express is a specification of a certain
STeCEQL. We main emphasis on the following
combination of events using multiple simple event
three improvements:
describing
the
correlation
of
the
events.
The
(a) Using the grid map model as the spatial model
complex event technology has been successfully
of internet of vehicles.
used in the research of internet of things. It is very
(b)
Based
on
the
grid
map
model,
give
the
important
to
use
the
appropriate
spatial
and
method to judge the direction and the relationship
temporal
models
in
the
complex
event
query
between the locations.
language of internet of things.
(c) Proposed an effectively complex event query
Xchange
is
a
complex
event
query
language
language STeCEQL.
based on the complex event relational algebra [7].
The syntax of the language likes the SQL language
3
THE EVENT INSTANCE AND THEIR
and contains the time model and time event to
support the computation of time. ETALIS is a
TEMPORAL MODEL, SPATIAL MODEL
complex Query language based on the rules and the
time relationships include: during, starts, equals,
finishes, meet etc. [8]. RCEDA can describe a
series of sequential events and a series of events
occurred in time intervals [9]. CE language can
express a combination of continuous, parallel and
repeated events and it allows users to use the
We look the objects in the internet of vehicles as
agents (e.g. a traffic signal, a car or a traffic speed
limit etc.). The agents’ properties can be detected
and sensed by several sensors. These properties
include time, location, speed and many other
values. In the event-based system, we associate
interval show the relationship between events [10].
Some events Query language focus on the spatial
relationship between events. Xiaoyan Chen design
an intelligent location-based service, which
contains two spatial relationship predicates: Within
and Distance [11]. Bamba discuss the issues about
the region alerts [12]. For example, users firstly
described a certain region and if there is any object
these properties with agent and call these as base
event instances.
Definition 1: The event instance of the internet
of vehicles is that the various prosperities of the
concerned agents in the system when they perform
an action or during some states. For example: Event
instance 1: the car C’ speed is 124 km/h at 1300km
moving into the region, the alarm will sound.
Recently, some researchers concern about the
relationship between the spatial and temporal
model of complex query language. Moody Ken
presents a complex event Query language: SpaTec
of a highway during 14:38:30 and 14:38:40. Event
instance 2: the traffic light L is red during 20:14:10
and 20:14:35. Assume that the general format of the
event instance is: e=
Attributen>.
450
Huiyong li et. al / International Journal of Computer Networks and Communications Security, 2 (12), December 2014
3.1 The Temporal Model of Internet of Vehicles
and it is widely used in the field of processing the
Time is continuous in the real world, but we
map and the image.
consider it is discrete and orderly in internet of
vehicles.
Since
the
sensors
periodically
identify
data, we cannot know the specific time point of the
base event in the system and only know the interval
time
of
it.
According
to
the
existing
research
results,
we
give
the
definition
of
the
temporal
model of the event as below.
Definition 2: The timestamp
of
the
event
Fig. 1. Point, Line and Area of Grid Map
instance is a time interval and it is a sequence
composed of two time points. Time-stamp= (start-
time, end-time), and the start-time is before the
end-time. For example: time-stamp1= (20:14:10,
20:14:35).
There are a lot of research results about the
relationship of the interval temporal model. Allen
has defined seven relationship between the time
intervals [16]. We simplified his model and the
relationship of our model are BEFORE, AFTER,
EQUAL, OVERLAP and DURING.
Base the grid map, we can define the location
model in the internet of vehicles:
Definition 3: In internet of vehicles, the location
is a set of grids which the base event occurred in.
Its value is the set of sequence numbers of its rows
and columns: location-stamp={(row1, column1),
(row2, column2), (row3, column3), (row4,
column4), (row5, column5), (row6, column6),…}.
In general spatial model, there are three kinds of
model: point, line and area. We look the point and
the line as a special location in our spatial model. It
3.2 The Spatial Model of Internet of Vehicles
The following we discuss the spatial model of
internet of vehicles. The internet of vehicles is an
important part of the intelligent transport system
and the smart city. Its spatial information generally
comes from the geographic information system
(GIS). In the geographic information system, the
spatial model (or the map model) should be
represented by vector map or grid map. The vector
map model can occupy less storage space, but it
spends a lot of time to calculate. Otherwise, the grid
map model need less time to calculate, but it
occupies more space. Since it is very important to
rapidly detect the event and make decision in the
event based system, we use the grid map to model
the space of internet of vehicles.
In the grid map, we look the space as a whole
is shown as Figure 1.
The direction is an important attribute of the
moving agents in internet of vehicles. We define
the direction relationship based on the grid map as
below:
Definition 4: As shown in figure 2, there are
eight directions in the spatial model of internet of
vehicles: direction-stamp={NORTH, SOUTH,
WEST, EAST, NORTHEAST, SOUTHEAST,
SOUTHWEST, NORTHWEST}.
We can judge the direction of the locations
through the row and column numbers of the grid
unit. For example: if A.row-number < B.row-
number, we can say that agent A stayed at NORTH
of agent B. On the other hand, the direction of a
moving agent can be detected by its orientation
sensors.
continuous entirety and divided the space into array
of uniform size grid. There is only one row and
column number of each grid. The number of rows
and columns depends on the characteristics and
spatial resolution of the system. The grid is the
basic unit of the grid map and the shape of a grid
usually is square, triangular or hexagonal. In the
square grid map, all grids have the same direction
Fig. 2. Directions of STeCEQL
HƯỚNG DẪN DOWNLOAD TÀI LIỆU

Bước 1:Tại trang tài liệu slideshare.vn bạn muốn tải, click vào nút Download màu xanh lá cây ở phía trên.
Bước 2: Tại liên kết tải về, bạn chọn liên kết để tải File về máy tính. Tại đây sẽ có lựa chọn tải File được lưu trên slideshare.vn
Bước 3: Một thông báo xuất hiện ở phía cuối trình duyệt, hỏi bạn muốn lưu . - Nếu click vào Save, file sẽ được lưu về máy (Quá trình tải file nhanh hay chậm phụ thuộc vào đường truyền internet, dung lượng file bạn muốn tải)
Có nhiều phần mềm hỗ trợ việc download file về máy tính với tốc độ tải file nhanh như: Internet Download Manager (IDM), Free Download Manager, ... Tùy vào sở thích của từng người mà người dùng chọn lựa phần mềm hỗ trợ download cho máy tính của mình  
7 lần xem

STECEQL: A spatio-temporal constraint event query language for internet of vehicles. In STeCEQL, we use time interval as temporal model and grid map as spatial models. We give the syntax and operational semantics of STeCEQL based on the temporal and spatial model. Finally, our experiments illustrate the effectiveness of the operational semantics..

Nội dung

International Journal of Computer Networks and Communications Security VOL. 2, NO. 12, DECEMBER 2014, 448–455 Available online at: www.ijcncs.org ISSN 2308-9830 STECEQL: A Spatio-Temporal Constraint Event Query Language for Internet of Vehicles HUIYONG LI1, YIXIANG CHEN2 AND YUJING MA3 1, 2, 3 Software Engineering Institute, East China Normal University, Shanghai, P.R. China E-mail: 1lihuiyongchina@126.com ABSTRACT In recent years, the complex event technology has been widely used in the monitoring and real-time querying information of the internet of things. The internet of vehicles is a novel researching area of the intelligent transportation systems, which developed with the technology of internet of things. It is different from the traditional internet of things. There are a large number of moving objects and they will produce large amounts of temporal and spatial information in the internet of vehicles. It becomes the core issue of the complex event technology in the internet of vehicles, how to effectively represent and process these information of the moving objects. We propose a novel temporal and spatial constraint complex event query language STeCEQL for the internet of vehicles. In STeCEQL, we use time interval as temporal model and grid map as spatial models. We give the syntax and operational semantics of STeCEQL based on the temporal and spatial model. Finally, our experiments illustrate the effectiveness of the operational semantics. Keywords: Event Driven Architecture, Event Query Language, Internet of Things, Internet of Vehicles, Grid Map, Mobile Systems. 1 INTRODUCTION With the development of Internet of things (IoT)[1]and Cyber-Physical System(CPS)[2], many new methods and viewpoints were proposed to solved the problem in the research of Intelligent Transportation Systems (ITS). The Internet of Vehicles use the vehicle-mounted electronic sensing device[3], the mobile communication technology, the car navigation system, the information terminal device and the intelligent network platform to contact each other between the vehicles and the road, cars and trucks, cars and people, vehicles and urban. By these networks, we can effective monitor, schedule and management the time, space, speed and other information of vehicles, people and road. Internet of Vehicles is performance crucial system and the system’s time performance and spatio-temporal consistence are the key of the system [4,5]. So it is very important to real-time monitor the time, space and other context information of various components of the system during the running time. With the applications of the clock technology, position technology (e.g. RFID, GPS), orientation sensors, speed sensors and other sensors, users can very easily get the time, position, direction, speed and other information about the internet of vehicles. However, there are a lot of mobile objects are moving very fast in the system and they instantly generate large amounts of data in the moving process. It is a very big challenge to store and process these data using the traditional database. Meanwhile, the traditional data mining techniques cannot monitor the information in time. To solve the above problems, many researchers have applied the complex event technology to sensor networks and the internet of things. The complex event technology can filter the amounts of data through the event query language into the events concerned by the users [6]. When a concerned event occurs, the event based system can real-time or near real-time process these information according to the pre-defined rules. In the event based system, it greatly reduces the processing and storing load since the system only need store the concerned event and discard 449 Huiyong li et. al / International Journal of Computer Networks and Communications Security, 2 (12), December 2014 unwanted data. So the complex event technology has a very wide range of applications in the internet of things. Since the existing complex event query language cannot effectively express the events’ mobile and spatio-temporal characteristics in the internet of vehicles. This paper presents a novel complex event query language to internet of vehicles: STeCEQL (spatio-temporal consistence event query language). The remainder of this paper is structured as follows: section 2 introduces the related works. Section 3 describes the temporal model and the spatial model of the internet of vehicles. Section 4 presents the syntax and operational semantics of STeCEQL. Section 5 gives some simulation data examples to reasoning complex event expresses. The last Section concludes this paper. 2 RELATED WORK An event query language is a high level programming language. A simple event express is a specification of a certain kind of single events. A complex event express is a specification of a certain combination of events using multiple simple event describing the correlation of the events. The complex event technology has been successfully used in the research of internet of things. It is very important to use the appropriate spatial and temporal models in the complex event query language of internet of things. Xchange is a complex event query language based on the complex event relational algebra [7]. The syntax of the language likes the SQL language and contains the time model and time event to support the computation of time. ETALIS is a complex Query language based on the rules and the time relationships include: during, starts, equals, finishes, meet etc. [8]. RCEDA can describe a series of sequential events and a series of events occurred in time intervals [9]. CE language can express a combination of continuous, parallel and repeated events and it allows users to use the interval show the relationship between events [10]. Some events Query language focus on the spatial relationship between events. Xiaoyan Chen design an intelligent location-based service, which contains two spatial relationship predicates: Within and Distance [11]. Bamba discuss the issues about the region alerts [12]. For example, users firstly described a certain region and if there is any object moving into the region, the alarm will sound. Recently, some researchers concern about the relationship between the spatial and temporal model of complex query language. Moody Ken presents a complex event Query language: SpaTec [13]. There are six base event operators: same location, remote, sequence, concurrency, conjunction, and disjunction. And four complex event operators: same location and sequence, remote and sequence, same location and concurrency, remote and concurrency. They have used SpaTec language in the monitoring system of London bus and propose the system architecture [14]. Jin Beihong has proposed a complex event Query language: CPSL [15]. The language can describe many temporal and spatial models and their relationships. In SpaTec language, the spatial model is a region with the central point. And in CPSL language, the spatial models are points set and convex polygons. These two complex event languages do not consider the direction information of the spatial model. We think that each object of internet of vehicles can share the spatial information from geographic information systems and they can access to the global map. The direction information is important in the system. Based on the above considerations, we propose a complex event query language: STeCEQL. We main emphasis on the following three improvements: (a) Using the grid map model as the spatial model of internet of vehicles. (b) Based on the grid map model, give the method to judge the direction and the relationship between the locations. (c) Proposed an effectively complex event query language STeCEQL. 3 THE EVENT INSTANCE AND THEIR TEMPORAL MODEL, SPATIAL MODEL We look the objects in the internet of vehicles as agents (e.g. a traffic signal, a car or a traffic speed limit etc.). The agents’ properties can be detected and sensed by several sensors. These properties include time, location, speed and many other values. In the event-based system, we associate these properties with agent and call these as base event instances. Definition 1: The event instance of the internet of vehicles is that the various prosperities of the concerned agents in the system when they perform an action or during some states. For example: Event instance 1: the car C’ speed is 124 km/h at 1300km of a highway during 14:38:30 and 14:38:40. Event instance 2: the traffic light L is red during 20:14:10 and 20:14:35. Assume that the general format of the event instance is: e=. 450 Huiyong li et. al / International Journal of Computer Networks and Communications Security, 2 (12), December 2014 3.1 The Temporal Model of Internet of Vehicles Time is continuous in the real world, but we consider it is discrete and orderly in internet of vehicles. Since the sensors periodically identify data, we cannot know the specific time point of the base event in the system and only know the interval time of it. According to the existing research results, we give the definition of the temporal and it is widely used in the field of processing the map and the image. model of the event as below. Definition 2: The timestamp of the event Fig. 1. Point, Line and Area of Grid Map instance is a time interval and it is a sequence composed of two time points. Time-stamp= (start-time, end-time), and the start-time is before the end-time. For example: time-stamp1= (20:14:10, 20:14:35). There are a lot of research results about the relationship of the interval temporal model. Allen has defined seven relationship between the time intervals [16]. We simplified his model and the relationship of our model are BEFORE, AFTER, EQUAL, OVERLAP and DURING. 3.2 The Spatial Model of Internet of Vehicles The following we discuss the spatial model of internet of vehicles. The internet of vehicles is an important part of the intelligent transport system and the smart city. Its spatial information generally comes from the geographic information system (GIS). In the geographic information system, the spatial model (or the map model) should be represented by vector map or grid map. The vector map model can occupy less storage space, but it spends a lot of time to calculate. Otherwise, the grid map model need less time to calculate, but it occupies more space. Since it is very important to rapidly detect the event and make decision in the event based system, we use the grid map to model the space of internet of vehicles. In the grid map, we look the space as a whole continuous entirety and divided the space into array of uniform size grid. There is only one row and column number of each grid. The number of rows and columns depends on the characteristics and spatial resolution of the system. The grid is the basic unit of the grid map and the shape of a grid usually is square, triangular or hexagonal. In the square grid map, all grids have the same direction Base the grid map, we can define the location model in the internet of vehicles: Definition 3: In internet of vehicles, the location is a set of grids which the base event occurred in. Its value is the set of sequence numbers of its rows and columns: location-stamp={(row1, column1), (row2, column2), (row3, column3), (row4, column4), (row5, column5), (row6, column6),…}. In general spatial model, there are three kinds of model: point, line and area. We look the point and the line as a special location in our spatial model. It is shown as Figure 1. The direction is an important attribute of the moving agents in internet of vehicles. We define the direction relationship based on the grid map as below: Definition 4: As shown in figure 2, there are eight directions in the spatial model of internet of vehicles: direction-stamp={NORTH, SOUTH, WEST, EAST, NORTHEAST, SOUTHEAST, SOUTHWEST, NORTHWEST}. We can judge the direction of the locations through the row and column numbers of the grid unit. For example: if A.row-number < B.row-number, we can say that agent A stayed at NORTH of agent B. On the other hand, the direction of a moving agent can be detected by its orientation sensors. Fig. 2. Directions of STeCEQL 451 Huiyong li et. al / International Journal of Computer Networks and Communications Security, 2 (12), December 2014 Based the above spatial model, we define eleven relationships: OUT-NORTH, OUT-SOUTH, OUT-WEST, OUT- EAST, OUT- NORTHEAST, OUT-SOUTHEAST, OUT-SOUTHWEST, OUT-NORTHWEST, EQ, OP, IN. 4 SYNTAX AND OPERATIONAL SEMANTIC OF STECEQL 4.1 Syntax of STeCEQL In internet of vehicles, the complex event query language STeCEQL mainly involves the following syntax sets: The agents set of the internet of vehicles: AGENT. The elements of AGENT are the objects which act events in the internet of vehicles. We call the element of AGENT as agent. Boolean value set: T={true, false}. The normal attributes of the agents may be an integer or a real number, e.g. temperature, speed etc. Assume the numbers set A= Z∪R, and we call its element as a. In addition, the syntax sets also include the time sets and the space sets. The temporal stamps set is called TIME-STAMP and its element represented by t. The spatial stamps set: LOACATION= {POINT-STAMP, AREA-STAMP }. And its element represented by l. The direction set: DISTANCE= {NORTH, SOUTH, WEST, EAST, NORTHEAST, SOUTHEAST, SOUTHWEST, NORTHWEST}. Its element called d. We call the storage unit sets X, and x is its element. The storage unit can store a variety of base event instance properties. The numerical attributes Boolean expressions set: ABexp and its element b. The temporal expressions set: TBexp and its element time. The spatial expressions set: LBexp and its element location. The base event expressions set: EBexp and its element e. The complex event expressions set: CEexp and its element ce. The syntax of the STeCEQL rules are as following: ABexp: b::= true| false| xa = a| xa != a | xa > a | xa >= a | xa < a | xa <= a TBexp: time::= true| false| xt BEFORE t | xt AFTER t | xt EQUAL t | xt OVERLAP t | xt DURING t LBexp: location::= true| false| xl EQ l | xl OP l | xl IN l | xl NORTH l | xl SOURTH l | xl EAST l | xl WEST l | xl NORTHWEST l | xl NORTHEAST l | xl SOURTHWEST l | xl SOURTHEAST l DBexp: direction::= true| false| xd = d | xd != d EBexp: e::= agenttime (b1;b2;b3) | agentlocation (b1;b2;b3) | agentlocation (b1;b2;b3) | agent(location, direction) (b1;b2;b3) CEexp: ce::= e1 AND e2|e1OR e2|e1BEFORE e2 |e1EQUAL e2|e1OVERLAP e2|e1DURING e2 |e1EQ e2|e1OP e2|e1IN e2|e1 NORTH *e2 In the above syntax rules, the operators of the temporal and spatial relationship are same as section 3. In the base event expression, the mobile agents’ event expression include: temporal, spatial and direction expression. The other agents’ event expression may include: temporal and spatial expression or only temporal expression. In addition, the elements of the complex event expression can be base events and complex events. 4.2 Opreational Semantics of STeCEQL In order to accurately explain the meaning of complex event expressions of STeCEQL language, we describe the operational semantics of the STeCEQL language: Assume the state set Σ consist of the function σ from the storage unit set to the attributes set. So σ(X) is the value of storage unit X under stateσ. The ordered pair represents that the evaluation result of any numeric attribute is itself. The ordered pair < b, > true represents that the evaluation result of expression b is true, under state . The Boolean value of the complex event expression is true or false and the rules are following: ABexp: xa = a, true, if (xa) = a xa = a,  false, if (xa)  a xa!= a, true, if (xa)  a xa!= a,  false,if (xa) = a xa > a, true, if (xa) > a xa > a,  false, if (xa)  a xa >= a, true, if (xa)  a xa >= a,  false, if (xa) < a xa < a, true, if (xa) < a 452 Huiyong li et. al / International Journal of Computer Networks and Communications Security, 2 (12), December 2014 xa < a,  false, if (xa)  a xa <= a, true, if (xa)  a xa <= a,  false, if (xa) > a TBexp: x BEFORET t , true, if (x ).endnt.endn x AFTERt ,  false, if (x ).start1t.endn x EQUALt, true, if (∀(x ).starti =t.starti and ∀(x ).endi = t.endi),i =1,2n x EQUALt ,  false, if (∃(x ).starti t.starti x OVERLAPt, true, if ( (x ).endnt.start1 and(x ).endnt.endn) xt OVERLAPt ,  false, if (x ).endnt.endn x DURING t , true, if (x ).start1t.start1and(x ).end1t.endn x DURING t ,  false, if (x ).start1< t.start1or (x ).end1> t.endn Lexp: x EQl , true, if (∀(x ).rowi=l.rowi and ∀(xl).columni =l.endi),i =1,2n x EQl,  false, if (∃(x ).rowil.rowi or ∃(xl).columni l.endi),i =1,2n xl OPl, true, if (∃(xl).rowi =l.rowj and ∃(xl).columni =l.columnj),(i, j =1,2n) xl OPl,  false, if (∀(xl).rowi =l.rowj and ∀(xl).columni =l.columnj),(i, j =1,2n) x IN l , true, if (x )l x IN l ,  false, if (x )l x NORTH l , true, if (∀(x ).rowi12) e6 = BusAxl EQ CORNER1(xv > 6) Start Time 9:10:00 9:15:00 BusStaion1Time 9:29:00 9:34:00 BusStaion2 Time 9:49:00 9:54:00 e7 = BusAl EQ CORNER2 (xv > 6) e8= BusBx IN L(xv >12) There are two buses BusA and BusB start at 9:10 and 9:15 respectively. Their velocity curve shown as figure 4 and their distance curve shown as figure 5. The sampling frequency of the speed is 60 seconds. e9 = BusBxl EQ CORNER1(xv > 6) e10 = BusBxl EQ CORNER2 (xv > 6) (c) Buses are overtaking: the buses can not exceed the first departure of the bus. e11= (BusBx IN L1) EAST (BusAx IN L1) e12=(BusBl IN L2) NORTHWEST (BusAl IN L2) e13= (BusBx IN L3) EAST (BusAl IN L3) According to the operational semantics of STeCEQL, we reason the above complex event expresses and the results shown in the following table: Table 2: The Values of Complex Event Expresses Fig. 4. The Velocity of Two Buses Event e1 e2 e3 e4 e5 e6 e7 e8 e9 e10 e11 e12 e13 True False √ √ √ √ √ √ √ √ √ √ √ √ √ Fig. 5. The Distance of Two Buses 454 Huiyong li et. al / International Journal of Computer Networks and Communications Security, 2 (12), December 2014 The values of the event e8 and ce3 are True in many times. The value of complex event ce3 is True after time interval (9:39:30,9:40:30). The value of event e8 is True in these time intervals: (9:17:30,9:18:30),(9:19:30,9:20:30),(9:25:30, 9:26:30),(9:27:30,9:28:30),(9:36:30,9:37:30),(9 :37: 30,9:38: 30),(9:44: 30,9:45: 30),(9:50: 30,9:51: 30),(9:53: 30,9:54: 30),(9:55: 30,9:56: 30),(9:56: 30,9:57: 30),(10:00: 30,10:00: 30). From the above results, we can conclude that BusA run more standardized, BusB run more mistaken and has the overtaking behavior. These results illustrate the effectiveness of the operational semantics of the STeCEQL language. 6 CONCLUSION For the current complex event query language cannot be effectively expressed the spatio-temporal information of the internet of vehicles. In this paper, we introduce a temporal model, a novel spatial model based on the grid map and give a method to determine the relationship between the spatial position and orientation relations. Based on the temporal and spatial model, we give the syntax and operational semantics of STeCEQL. Finally, we describe the language is expressive and its operational semantics is valid by the reasoning of the data of a simulation of the bus system. As the internet of vehicles is a typical distributed real-time system, we will further study their performance analysis in the distributed real-time system. ACKNOWLEDGEMENTS This work is supported by the National Basic Research Program of China (No. 2011CB302802), the National Natural Science Foundation of China (No.61370100 and No.61021004) and Shanghai Knowledge Service Platform Project (No. ZF1213). 5 REFERENCES [1] L. Atzori, A. Lera and G. Morabito, “The internet of things: A survey”, Computer Networks,Vol. 54, No.15, 2010, pp.2787– 2805. [2] E. A. Lee, “Cyber physical systems: Design challenges”, Object Oriented Real-Time Distributed Computing (ISORC), 2008 11th IEEE International Symposium (IEEE, 2008),pp. 363–369. [3] G. Dimitrakopoulos, “Intelligent transportation systems based on internet-connected vehicles: Fundamental research areas and challenges”, ITS Telecommunications (ITST), 2011 11th International Conference (IEEE ,2011), pp. 145–151. [4] Y. Chen, “Stec: A location-triggered specification language for real-time systems”, Object/ Component/ Service-Oriented Real-Time Distributed Computing Workshops (ISORCW), 2012 15th IEEE International Symposium (IEEE ,2012), pp. 1–6. [5] J. Xingyi, L. Xiaodong and K. Ning, “Efficient complex event processing over RFID data stream”, Computer and Information Science, 2008. ICIS 08. Seventh IEEE/ACIS International Conference (IEEE ,2008), pp. 75– 81.

Tài liệu liên quan