Proceedings of the 7th Annual ISC Graduate Research Symposium
ISC-GRS 2013
April 24, 2013, Rolla, Missouri
Sima Das
Department of Electrical and Computer Engineering
Missouri University of Science and Technology, Rolla, MO 65409
INTEROPERABILITY AMONG HETEROGENEOUS MOBILE MULTI-DATABASES
ABSTRACT
Advances in technology allow mobile nodes to own Databases
and perform Transaction processing in a Mobile Ad-hoc
Network (MANET). The implicit heterogeneity and autonomy
of individual database permits us to view the underlying
database system as a multi-database system. Our problem
formulation is based on both mobile user and mobile Data
Servers. In this context we propose interoperability among
heterogeneous, mobile, multi-databases and achieve it with an
integration of underlying network architecture and summary
schema structure. Over semantically inter-related and possibly
replicated databases; we consider transaction management by
using semantic serializability concept over summary schema
model. Further, we analyze the effect of mobility on summary
schema structure and on transaction management.
1. INTRODUCTION
With advances in technology the mobile devices are capable of
higher storage to act as databases and enough processing ability
to process transactions. Since, data servers are mobile they need
to be connected over wireless. Thus, dynamically configurable
network commonly called mobile ad hoc network (MANET)
become part of the overall network model. A wide range of
applications confers to this model. In this environment the
nodes or mobile hosts act as database servers. Taking into
account highly mobile database servers, we consider
(manned/un-manned) vehicular network as our suitable
application domain for this paper. Though, the application can
be set of communicating and collaborating drones in
performing certain tasks; to ground search, rescue and critical
mission; with the later one having lesser mobility of individual
nodes.
The implicit heterogeneity and autonomy of the underlying
distributed database servers permit us to view it as a mobile
multi-database system, where each mobile host can access
multiple databases to process global transactions. This requires
inter-operability among multiple heterogeneous, autonomous
mobile databases. Further, to achieve higher performance we
need suitable transaction management techniques over mobile
multi-databases.
Transaction management over static database servers and
mobile hosts is studied in [1]. Here Ongtang et al., considered
mobile agents on summary schema model; to mange
transactions over static multi-database. Corresponding to each
submitted transaction a global agent is created to take care of
the actions. Agents here act as an abstraction over the
underlying network and mapping among schema hierarchy.
Xing et al., [2,3] proposed an optimistic concurrency
control algorithm called sequential order dynamic adjustment
(SODA) over centralized and distributed mobile ad hoc
network databases. In SODA a history of committed
transactions is maintained to validate committing transactions.
Further, the list is dynamically adjusted during validation
process to avoid unnecessary aborts. This gives a sequential
ordering among the transactions. In [3] the authors considered
energy efficient dynamic cluster construction algorithm over
MANET, which is also the basis behind many (mobile) wireless
sensor network due to their energy constraints. In MANET
energy may not always be a constraint and is dependent on
specific application, for example in applications like vehicular
network and co-operating drones accomplishing some specific
task there is no energy constraint; where as, ground search,
rescue, and critical mission may have energy constraint.
Further, in energy constrained mobile database node it is not
always practical to execute time-consuming transactions,
irrespective of dynamic selection of cluster head based on
criteria of energy consumption rate or remaining energy.
Brayner et al., [4] proposed semantic serializability based
concurrency control over MANET databases. This has the
intrinsic assumption that databases are disjoint and updates on a
database only depend on values of data in the same database.
Though, in many practical applications databases can be interdependent based on corresponding organizational structure, and
the assumptions of semantic serializability does not hold. In
this approach, global transactions are serialized at each site
using strict 2PL, while each site must maintain the consistency
of its own local database at the same time. Since, the global
serializability is relaxed and there is no co-ordination among
servers, and the locks held by the sub-transactions of the global
transaction are released once they are completed at the local
sites, the limited bandwidth is saved and transaction execution
time is reduced. Ongtang et al., [1] used the summary schema
model for transaction management in static multi-databases.
Since, summary schema model represent semantic abstraction
over databases, parsing the transaction over the schema
hierarchy is one way of achieving semantic serializability. Here
1
the transaction commits only when all its sub-transactions are
ready to commit (similar to strict 2PL scheme) and the issue of
indirect conflict is addressed, as global order is always
maintained. Still, the work in [1] has the implicit assumption of
disjoint databases and that updates in a database only depend
on values of data in that database. Situation involving update
operation of a transaction over some attributes in one database
that is inter-related with other databases over the same
attribute(s), or, replications is not discussed.
The research in [1-4] does not consider the effect of
underlying network architecture on transaction management. In
this context we consider interoperability among heterogeneous
mobile multi-databases as an integration of network
architecture and database concepts for better transaction
management. For our study, we take (manned/unmanned)
vehicular network as the standard application domain. In
section 2 we consider the underlying network architecture. In
section 3 we discuss interoperability among heterogeneous
mobile multi-databases. Section 4 considers transaction
management over summary schema model. Section 5 discuses
the effect of mobility on schema hierarchy and transaction
2. UNDERLYING ARCHITECTURE
Vehicular communication network (manned/unmanned)
consists of two types of communication. In Vehicle –to-Vehicle
(V2V) communication vehicles communicate directly with each
other over WLAN using IEEE 802.11p standard and resulting
in a vehicular ad hoc network. Thus, V2V network is selforganized, restricted to local level (communication range of
300 meters), and have high data rate. Large-scale
Implementation in WLAN leads to congestion and frequent
disconnection.
In
Vehicle-to-Infrastructure
(V2I)
communication
vehicles
communicate
directly
with
infrastructure (base station) over WWAN using say 3G. The
powerful communication device in base stations gives wider
range (usually 20 miles), but WWANs have lower data rates
and give limited bandwidth to vehicles. Vehicles or mobile
nodes are considered to have data servers (possibly
heterogeneous), and can both initiate and process transactions.
Vehicles move along pre-existing roads, have no energy
constraint, relatively lesser computing power than base stations,
and have both 802.11 and 3G interfaces. Thus, V2V network is
a specific class of MANET. In contrast, base stations
considered to be fixed, have servers associated with them, have
larger storage, and computing power, and can communicate
with each other over optical fiber or wireless. We consider the
server associated with each base station to have an auxiliary
database. We refer to the area under each base station as zone.
If each mobile node uses V2I communication, then limited-3G
bandwidth will give rise to congestion, as base stations serve
over a much wider transmission range. Thus, we consider the
combination of these two types of communication model called
hybrid model.
Over this hybrid model clusters are formed involving
vehicles, and base stations to give a hierarchical structure that
fits into the network constraints. Here, we visualize three kinds
of clusters: one involving subset of vehicles where,
communication takes place over WLAN; another involving
each base station and the set of cluster heads within its zone;
the third static cluster involving only bases stations. Since,
there is no energy constraint the best possible way to form the
clusters is to use relative mobility and signal strength as
parameters, as the network mobility changes frequently. The
corresponding cluster head is selected based on some
predefined heuristics (like ratio of total number of mobile nodes
within the communication range to the number of mobile nodes
with above average signal level, relative displacement). In
literature there exists a number of clustering and cluster head
election algorithms for VANET [5,6]. Further, in the MANET
domain the dynamic clustering and cluster head algorithms
need to consider energy constraint in the algorithms [7]. Fig. 1
represents this basic hierarchical network structure.
In the following section we consider the needed database
architecture over the discussed network hierarchy to facilitate
Interoperability over mobile multi-databases.
3.
INTEROPERABILITY AMONG HETEROGENEOUS
MOBILE MULTI-DATABASES
In order to consider interoperability among heterogeneous
mobile multi-databases, we use the Summary Schema Model.
Ceri et al., [8] proposed Global Schema Model to distributed
databases. Further, Batini et al., [9] considered database schema
integration methodologies. Extending the global schema
structure to multi-databases Bright et al., considered summary
schema model in [10].
3.1.Underlying Summary Schema Structure
Here we consider two specific types of summary schema
structures to achieve Interoperability. Summary schema
structure represents a semantic and structural abstraction of the
underlying schema.
(1)
One summary schema model is over the
autonomous, heterogeneous, mobile multidatabases.
(2)
Another summary schema is over the geographic
regions of the road network.
Since, the application domain is a vehicular network, the
summary schema model over the geographic regions will
contain a hierarchy of (Inter, Intra) state highways, local roads,
corresponding exits, significant places etc., with the
corresponding base station’s subnet IP address at the lowest
level of the schema hierarchy.
We assume the database designer/ linguist has already
defined the semantic and structural relationship among
attributes for a specific application domain (here vehicular
network). On this an automatic, dynamic schema
summarization algorithm is used to generate the summary
schema hierarchy or to map the incoming transaction to
semantically and structurally similar schema.
Further, we consider the summery schema model being
built up at four levels.
2
(a)
(b)
(c)
(d)
WLAN-Summary Schema: Each vehicle or mobile
node has a database and its corresponding local
schema. When a local cluster (WLAN) is formed
and a cluster head is elected, all the local schemas
within it are used to generate the corresponding
WLAN-summary schema at the cluster head. This
summary schema is built automatically and
dynamically (as mobile nodes can change) using
schema summarization algorithm. The lowest
level cells of the schema table points to the IP
address of the respective vehicles with matched
semantic and structural attribute, and the cells of
upper level schema table points to the
corresponding matched lower level schema table.
WWAN-Summary Schema/ Intra-Zonal Summary
Schema: Each cluster head in a zone communicate
with the corresponding base station. The WLANsummary schemas are used to build the intra-zonal
summary schema at the base station, using the
automatic schema summarization algorithm. The
intra-zonal summary schema is build over all the
mobile nodes of the respective zone. As in the
WLAN–summary schema, the cells of the lowest
level schema table here points to the IP address of
the respective mobile node and of the upper level
schema tables points to the lower level schema
table based on semantic and structural matching.
Auxiliary Summary Schema: This is either a
complete or partial replica of the intra-summary
schema model. This is built (replicated) when a
mobile node leaves a zone, and its corresponding
data is copied to the auxiliary database. The
auxiliary database and the corresponding schema
is used to parse the transaction when there is no
matched schema is found in the intra-zonal
summary schema and the transaction refers to that
zone only. One such situation is, suppose a zone is
completely empty and a transaction is directed to
that zone to gather information about road
condition; in this case the auxiliary summary
schema is used to scan the auxiliary database for
any possible information. The auxiliary DB in any
case contains the most updated information
corresponding to any set of attributes, when there
is no vehicle with the matched schema attributes.
This is because, leaving vehicles update the
auxiliary database if there is no other node with
matching schema attributes and there is read or
update operation being performed on it. In case of
update operation the updated value is kept on the
auxiliary database for future use. We will consider
the details of the use of auxiliary schema and
auxiliary database in Section 3
Inter-Zonal Summary Schema: This summary
schema is static one and can be built directly by
the database designer, owing to the fixed base
stations. This is built over geographic road
network and corresponding base station’s subnet
IP address, which we mentioned earlier. This is
used to find either destination base stations for a
geographic region or gateway base stations to the
destination. This acts as a DNS server in network.
The minute details of the above summary schema structure
falls under the domain of schema summarization process.
Inter-zonal summary gives user flexibility in submitting its
transaction, without worrying about its current zone. If the
destination for transaction processing is outside the current
zone, then the specific route to the destination is taken; without
broadcasting through all adjacent base stations.
We will see the use of these above schema structures in
providing interoperability during transaction execution.
3.2.Interoperability with Multiple Summary Schema
Structure
The multiple layers of summary schema structure allow
transactions to be processed under their most precise domain.
Suitable domain for processing transactions can be determined
inn the following way using summary schema:
Once a transaction is issued at a node:
(1) The semantic and structural matching is done to check
whether it involves own database or not. If it involves only own
database domain and
(a) A read operation, then the information is accessed
locally.
(b) An update operation, then the corresponding
attributes are updated and the WLAN summary schema is
searched for any other matching node and the attribute
value is updated at the respective nodes.
(2) If the outcome of the semantic and structural matching
on own database is NULL, then WLAN-summary schema is
searched for any matching node. If the match is found then:
(a) If it is a read operation, then the information is
extracted from one of the matching nodes.
(b) If it is an update operation then all matched nodes
are updated.
(3) If the outcome of matching with respect to WLANsummary schema is NULL, then it is passed to the bases station
via the cluster head. There it is matched with respect to the
inter-zonal summary schema to find its corresponding zone and
the base station.
(I) If it is the same base station, then the transaction is
matched against the intra-zonal summary schema node to find
the destination node to process the transaction.
(II) If it is a different base station, then the transaction is
routed to the destination subnet IP address and matched against
the intra-zonal summary schema node at the destination base
station.
Corresponding to read or update operation the suitable
steps are taken as in (2).(a) and (2).(b).
We see that, the above summary schema based approach
allows us to route the transaction to the destination in a guided
manner, rather than making an undirected broadcast. In this
3
discussion we limited our discussion to focus on
interoperability, but there is more to the transaction execution.
In the above we considered the transactions to be simple
ones, involving single nodes. In reality single transaction
processing can span multiple nodes and nodes themselves may
not be semantically and structurally disjoint. We consider the
details of this in transaction management in the following
section.
4. TRANSACTION MANAGEMENT OVER SUMMARY
SCHEMA MODEL
In order to achieve higher performance transactions are
interleaved. The concept underneath concurrent transaction
execution, while maintaining the atomicity and consistency
property is to have a sequential execution of the conflicting
transactions. By conflicting transactions we mean the set of
transactions that want to access the same data (semantically and
structurally) at the same time and one of them is an update
operation. One approach to achieve sequential order among
conflicting transactions is through semantic and structural
parsing. Since, schema represents a semantic and structural
abstraction of the underlying attributes, it is justified to parse
through the summary schema hierarchy of [10]. Further,
summary schema hierarchy provides parallelism by allowing
multiple entry points for a transaction. An incoming transaction
can be matched with summary schema nodes at all levels of the
summary schema hierarchy for possible matching. In [1] the
authors considered summary schema hierarchy, but implicitly
assumed disjoint and non-replicated database. Further, they
have used agents as an abstraction over the underlying network
and for parsing over the schema hierarchy.
Here we consider databases that can have semantically and
structurally identical attributes, and can be completely
replicated. In addition, transaction execution can span over
semantically and structurally related databases involving
common attributes. Further, we consider non-agent based
model, where transactions are passed over the network as
packets and parsed over the summary schema hierarchy by a
semantic parser. After getting parsed over the summary schema
model, transactions reach the desired destination database for
processing.
Let us consider Ri, Rj as databases placed at two distinct
nodes. Let Ri(ak) and Rj(al): attribute set of relation Ri and Rj
respectively. We consider È,Ç : semantic, and structural union
and intersection respectively. Let Ri(ak) Ç Rj(al) = {Si } ≠ Ø.
We represent Ti-Ok: operation k corresponding to a transaction
Ti. Let {am} Í {Si}.
(a) Let us consider Ti-Ok ({am}} = Write and Ti-Ol
({am}} = Read. OR, Ti-Ok ({am}} = Write and Tj-Ok’
({am}} = Read. That is the sub-transactions parsed to
different local databases, or, operations corresponding
to different transaction parsed to different databases
but involve common attributes then, Ti-Ok ({am}} <
Ti-Ol ({am}}.
(b) Let us consider Ti-Ok ({am}} = Write and Ti-Ol
({am}} = write. OR, Ti-Ok ({am}} = Write and Tj-Ok’
({am}} =write. Similar to the above situation, Ti-Ok
({am}} < Ti-Ol ({am}} or, Ti-Ol ({am}} < Ti-Ok
({am}}.
(c) If {Si} = Ri(ak) = Rj(al). Ti-Ok ({Si}} = Write = Ti-Ok
(Ri(ak)), then " Rj(al) = Ri(ak): Ti-Ok (Rj(al)) =
Write. Further, " Tj > Ti: Ti-Ok < Tj-Ok’.
The above set of constraints provides conflict
serializability for the transactions, while parsed through
summary schema hierarchy in case have inter-related and
replicated databases.
5. EFFECT OF MOBILITY ON SCHEMA HIERARCHY
AND TRANSACTION MANEGEMENT
In this section we consider two aspects of mobility: if mobility
will have any effect on performance of the summary schema
structure and how transactions will be handled in case of
mobile nodes that process and/or submit transactions. Summary
schema structure is a logical hierarchical graph based on
semantic and structural heterogeneity. The map along this graph
is a traversal from generalized to precise attribute that satisfy
the semantics and structural property of all higher-level nodes.
In our example vehicular network application, all mobile nodes
are highly mobile. In the following paragraph we consider the
different scenarios, where summary schema is built and
maintained and analyze effect of mobility on each.
Let’s first consider the WLAN. Here, the cluster head
contains the summary schema hierarchy for the nodes within its
own communication range. The cells of the lowest level SSN
points to the IP address of the vehicles. When a vehicle moves
away from the cluster and hence, out of the communication
range of the cluster head, the respective cell pointer at the
lowest level SSN is set to NULL. At the same time, there might
be other mobile nodes within the range of the cluster head with
matching semantics, so the lowest level SSN may not get
deleted. If there is no node with the matching semantic
attributes of the SSN, then the pointer to this node is set to
NULL at its upper level SSN. Thus, the deletion operation is an
outcome of the heterogeneity of semantics and structural
information of the mobile node and the number of
heterogeneous nodes moving out. In the worst case if the
cluster is dissolved that is, all the nodes move out of the cluster
(irrespective of whether heterogeneous or homogeneous) then
the graph becomes empty. An upper level SSN node is set to
NULL, iff all its lower level nodes (children) are empty. If we
observe, then each of the summary schema node is like a table
that is populated (a pointer is added) when a match is found
with its metadata fields and is eliminated when there is no
match. This hierarchical graph structure is stored in memory,
like a routing table in router that is populated and deleted as the
nodes are connected and removed in its network. If we consider
the cost of eliminating SSN nodes in the logical structure, then
in the worst case it is O (h); where h is the height of the
semantic tree. Further, in most application domains, mobile
nodes move in groups, with relative distance among nodes
4
remaining same in most part. Thus, it is less likely that there
will be changes to the cell pointer in WLAN. Similarly, when a
node is added to the cluster and a (new) cluster head is elected,
the summary schema is built. The cost (time) to build this graph
depends on the efficiency of the semantic translator. The worstcase time to add a pointer to the already existing SSN node
(table) or, to add a new SSN is O (h), where h is the height of
the schema graph. In case of a new SSN node creation, the
nodes in the upper layer need to be adjusted to accommodate
the addition. This too has worst case cost O (h), giving the
overall worst case cost to be O (h). In case of WWAN, when a
node moves out of its current zone the respective cell pointer in
the corresponding intra-zonal schema is set to NULL.
Similarly, when a node enters a zone the corresponding intrazonal summary schema is populated. The procedure and cost is
same, as in case of WLAN-summary schema structure.
For inter-zonal summary schema there is no effect of
mobility. Further, the effect of mobility on auxiliary summary
schema structure is discussed in the following paragraph, along
with transactions.
First, we consider that a transaction is allocated to the
matched node, if it has not initiated any handoff procedure.
Now, if the node initiates handoff while executing the
transaction, then the following situations can happen.
(1)
The transaction is completed before the handoff
procedure is done. In this case the result is
returned to the destination via base station and/or
cluster heads.
(2)
Transaction is yet to be completed and the hand
off procedure is initiated.
(a) The transaction is aborted over that database.
If there is additional matching nodes in the
same zone then the transaction is restarted
executing there. Further, all other allocated
transactions to the transiting node are
transferred to the suitable matched node.
(b) There is no matched node from the intrazonal summary schema. The state of the
database that is the values of the
corresponding database is stored in the
auxiliary database. The associated summary
schema model (partial) is copied/formed as
part of auxiliary summary schema structure.
The aborted transaction can now start
executing at the auxiliary database. Further,
the allocated but waiting transactions are
transferred to getting executed over the
auxiliary database.
(3)
If the transaction does not have any matching
node over the intra-zonal schema, then the
auxiliary summary schema is searched for
possible matching. If a match is found, the
transaction is transferred to auxiliary database and
is executed there.
When a node processing a transaction, moves out of a
WLAN framework, and is within the zone; it sends the result
back to the cluster head via the new cluster head and base
station. Any cluster head keeps track of the submitted but
waiting transactions till it gets the corresponding result and
delivers it to the requested node.
Thus, an auxiliary database is updated when a node leaves
a zone and there is no other node with the matched
semantic/structural information.
Now, we consider the situation where the requesting node
has moved out of its WLAN or WWAN network. In either case
the result is routed to the node via base station and cluster head.
The result is returned to the base station of the transactionprocessing node. From here, the result is forwarded to the
current location of the node.
7. CONCLUSIONS
In this paper we introduce interoperability among mobile
heterogeneous multi-databases by taking into consideration the
underlying network architecture and the summary schema
model. The summary schema approach is not suitable for
resource constrained mobile ad hoc network, neither is time
consuming transaction processing suitable over energy
constrained mobile ad hoc network. In some application
domains, like vehicular network the lack of energy constraint
and resource constraint makes it suitable for multi-database
transaction processing, where we considered summary schema
model. Further, we considered transaction management over
inter-related and replicated databases. Finally, we considered
the effect of mobility over summary schema hierarchy and
transaction management. Related to this paper we need to give
the correctness proof of the transaction management approach
over mobile multi-database. For future research in this domain,
we would like to consider novel concurrency control algorithms
that will be suitable for resource and energy constrained mobile
ad hoc networks. This algorithm should be independent of the
dynamic cluster head selection
8. ACKNOWLEDGMENTS
I am grateful to the Intelligent Systems Center for the support
for this research work.
9. REFERENCES
[1] Ongtang, M., Hurson, Ali R., and Jiao Y., 2009,
“Agent-based Infrastructure for Data and Transaction
Management in Mobile Heterogeneous Environment,”
International Conference on Communication and
Mobile Computing, CMC-Vol. 3, pp. 314-318.
[2] Xing, Z., Grunewald, L., and Phang, K. K., 2008,
“SODA: an Algorithm to Guarantee Correctness of
Concurrent Transaction Execution in Mobile P2P
Databases,” Proceedings of the 19 th International
Conference on DEXA Workshop, pp. 337-341.
[3] Xing, Z., and Grunewald, L., “An Energy-efficient
Concurrency Control Algorithm for Mobile Ad-hoc
Network Databases,” Proceedings of the 22 nd
International Conference on Database and Expert
Applications Systems, pp. 496-510 (2011).
5
Brayner, A., and Alencar, F. S., 2005, “A Semanticserializability Based Fully-Distributed Concurrency
Control Mechanism for Mobile Multi-database
Systems,” Proceedings of the 16 th International
Workshop on DEXA, pp. 1085-1089.
[5] Souza, E. D., 2010, “A New Aggregate Local Mobility
Clustering Algorithm for VANET,” IEEE International
Conference on Communications, pp. 7200 -7204.
[6] Weiwei, L., 2012, “Robust Clustering for Connected
Vehicles using Local Network,” IEEE International
Conference on Communications, pp. 7157 -7161.
[7] Bandyopadhyay, S., 2003, “An Energy Efficient
Hierarchical Clustering Algorithm for Wireless Sensor
Networks,” INFOCOMM-Vol. 3, pp. 1713-1723.
[8] Ceri, S., Pernici, B., and Wiederhold, G., 1987,
“Distributed Database Design Methodologies,” Proc.
IEEE 75, Vol.-5, pp. 533-546.
[9] Batini, C., Lenzerini, M., and Navathe, S. B., 1986, “A
Comparative Analysis of Methodologies for Database
Schema Integration,” ACM Comput. Survey, 18, Vol.4, pp. 322-364.
[10] Bright, M. W., and Hurson, A. R., 1990, “Summary
schemas in Multi-database Systems,” Computer
Engineering Tech. Rep. TR-90-076, The Pennsylvania
State Univ., University Park, Penn.
[4]
6
WWAN2
BS2
BS1
WLAN4
CH
MN
WLAN1
WLAN2
WWAN1
Fig 1: Logical clusters with communication topology
The figure is not to scale.
CH: Cluster Heads in each cluster with dotted black lines communicate with BS
MN: Mobile nodes showing grey nodes, that communicate with other nodes within each cluster
BS: the base station that communicate with cluster heads and other base stations
Dotted black lines: Shows a logical cluster (WLAN)
Solid Black links used to represent nodes within each cluster that can communicate directly with each other.
Solid green links represent communication between cluster heads and BS
The solid black rectangles represent WWAN.
The solid red line represents the communication line (can be optical fiber, or wireless) between two base stations.
7