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Sensor networks provide a number of extensive programming challenges for Wireless Sensor Networks (WSNs) application programmers. Application developers have proposed various WSNs programming models to avoid these challenges and make WSN programming much easier. In this paper we proposed a new programming model to find the best routing path in WSNs and work on the coding of actual sensor nodes to perform the desired tasks. Then we describe the initial design and the implementation of our proposed model and compare the results when applied in different network topologies with multiple routing algorithms. Finally, we present an evaluation of our model in terms of cost and accuracy. KEYWORDS Programming models, WSN, Node-dependent, eventual Consistancy, routing algorithm, cost-minimizing algorithm, heuristic algorithm, routing path.
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International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.4, July 2013
DOI : 10.5121/ijcnc.2013.5401 01
A New Programming Model to Simulate Wireless
Sensor Networks : Finding The Best Routing Path
Abrar Alajlan, Khaled Elleithy, and Varun Pande
Department of Computer Science and Engineering, University of Bridgeport, Bridgeport,
CT, USA
aalajlan@bridgeport.edu,elleithy@bridgeport.edu ,vpande@bridgeport.edu
ABSTRACT
Sensor networks provide a number of extensive programming challenges for Wireless Sensor Networks
(WSNs) application programmers. Application developers have proposed various WSNs programming
models to avoid these challenges and make WSN programming much easier. In this paper we proposed a
new programming model to find the best routing path in WSNs and work on the coding of actual sensor
nodes to perform the desired tasks. Then we describe the initial design and the implementation of our
proposed model and compare the results when applied in different network topologies with multiple routing
algorithms. Finally, we present an evaluation of our model in terms of cost and accuracy.
KEYWORDS
Programming models, WSN, Node- dependent, eventual Consistancy, routing algorithm, cost-minimizing
algorithm, heuristic algorithm, routing path.
1. INTRODUCTION
Wireless sensor networks contain a large number of inexpensive sensor nodes deployed to
different environments and can be used to detect data and deliver it to sink nodes [1]. Each sensor
node is consist of a radio transceiver which has the ability to send or receive packets, a processor
to schedule and perform tasks, and power source to provide energy [2]. A sensor network consists
of different number of sensor nodes used to transmit and forward sensing data between sensing
nodes and the sink or base station. These sensors can be deployed in many applications for
example to conduct the environmental conditions such as temperature, humidity and pressures
with low power consumption [4]. The ability to place remote sensing nodes without having to run
wires and the cost related to it is a huge gain. And as the size of the circuitry of WSN is growing
smaller along with the cost, the chances of their field of applications are growing large [3]. Most
sensors are battery powered and hence conserving the energy of these sensors is very crucial.
Thus, a new model should be applied to manage the power consumption and deliver detected data
to the base station.
Several programming approaches have been proposed to assist WSN programming in high-level
languages. Two broad classes of WSN programming models have only been introduced recently:
local behavior abstraction and global behavior abstraction [5]. The local behavior abstractions can
be done at node-level where network programmers need to synchronize the work flow between
sensing nodes and maintaining routing code manually. In opposition, global behavior abstractions
or equivalently “Microprogramming abstraction”, as we shall discuss later, has emerged as one of
the most important aspects in sensor networks. The main objective behind macroprogramming
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approach is the ability to treat the whole network as one single unit rather than working on each
node individually [6].
Since there are many programming models have been explored recently, we would focus on
coding the actual sensors to find the shortest routing path, best possible routing path and the
maximum routes in multiple wireless sensor networks topologies.
The paper is structured as follows. Section 2 identifies the requirements for sensor network
programming. Section 3 provides an overview the related work of macroprogramming model in
WSN and section 4 defines the proposed algorithm. Section 5 describes the simulation setup.
Section 6 provides the analysis of results. Section 7 discusses the results. Finally, section 8
conclusions.
2. REQUIREMENTS FOR SENSOR NETWORK PROGRAMMING
It is obvious that sensor networks can be used in different applications across multiple
environments. Moreover, it is very easy to modify the internal functionality of sensor networks to
perform different tasks and to support many sensor networks applications. However, there are
four significant requirements for sensor network programming as follow:
- Energy-efficiency
- Scalability
- Localization
- Time-Synchronization
Energy efficiency is one of the most important issues in deigning sensor networks. The overall
design of sensor networks should mainly emphasize on enhancing the performance in terms of
energy used and power consumed. The total lifetime of a battery-powered sensor networks is
limited by the battery's capacity and each sensor node is equipped with a limited computation
processor to perform its tasks [7]. Thus, programming model for sensor networks should deploy
some applications that attain a proper level of energy-efficiency and to deliver demanded results
[8].
Scalability is another important aspect in designing sensor networks applications. A scalable
sensor network is a network that able to deliver results with different number of nodes[9]. Since
we cannot predetermine the location of sensor nodes and we cannot assure the lifetime of sensor
node, programming model should help in such a way to design scalable applications that able to
deliver accurate results [9].
In some sensor network applications, nodes are scattered quite randomly in the tested area rather
than studying the location of each node. However, the location information of distributed nodes
needs to be known in order to exchange data between nodes and the sink. [10]
Localization of sensor network is one of the most important issues in programming sensor
networks[11]. Many localization techniques have been proposed recently, either by deploying
self-localized technique or by install a Global Positioning System (GPS) device in each node to
determine the exact location of sensor node. Moreover, the location of each sensor node can be
determined by calculating the distance between the selected sensor node and neighboring nodes
[12].
Time-synchronization is another essential requirement for programming sensor network. Clock
synchronization is a process used to ensure an accurate scheduling between nodes with no
International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.4, July 2013
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collision [14]. As stated above WSNs have limited power; therefore, time- synchronization
technique reduces power consumption by passing some nodes off from time to time [15].
Clock synchronization in sensing nodes is generally required for some reasons. First, to support
the coordination and collaboration between sensor nodes. Second, to manage sleep and active
state in each node [16]. Third, to avoid collisions between sensor nodes as used in TDMA [13].
There are some other requirements that we have not introduced in this paper. However, the
concentration of this work is mainly on the four requirements stated above.
3. RELATED WORK
Several macro-programming abstractions have been introduced recently. In this section, we
provide a brief classification of macroprogramming model as shown in fig.1 and introduce our
work under one selected area. Macro-programming model or equivalently “networking
abstractions” considered to be high-level WSN programming model where the whole sensor
network is treated as a one unit[17]. This approach helps to emphasize on improving the
semantics of the network instead of studying the characteristics of the programming environments
[18]. Several macro-programming models have been proposed in the past which provide an idea
about the node itself and the networking platform. Nano-CF programming framework as in [18]
enables to execute multiple applications simultaneously at the same sensor networking platform.
There are two different major classes of network-level abstractions. One is Node-dependent
abstractions and the other is Node-independent abstractions. In node-dependent approach a group
of nodes can be treated at the same time in one single code. Our proposed work as we will
introduce later, falls into this category [19]. This approach designed to allow the user to define the
distributed system performance based on the nodes and their states[21].
3.1. Node Dependent Approach
Kairos is a node-dependent abstraction where the neighboring nodes are able to communicate by
using common requests at specific nodes [22].
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This approach has a centralized programming environment which can be translated later by the
compiler to many executable effective nodal programs [23]. Kairos enhances the use of sensor
programming languages by providing three simple mechanisms. First, node abstraction, where the
programmer deploys network nodes explicitly and named each node with an integer identifier, yet
these integer identifiers do not reflect the structure of sensor node. Hence, there is no need to
specify the network structure when using Kairos [23]. Second, identification of one-hop
neighbors, where the programmers able to use what called (get neighbors function) to support
wireless communication between nodes. When get function called, a list of neighbor nodes
returned, so the calling node can select which node to communicate with. Third abstraction is
accessing data on a remote node means the capacity to access variables nodes from selected
node[20]. Thus, Kairos can be used with many well-known programming languages such as
python as in [23].
Another example of node-dependent abstraction is Regiment which is purely microprogramming
functional language that allows direct use of program state [24]. However, it uses what called
monads; it is described in more detail elsewhere in [25]. In Regiment, programmers deploy
groups of data stream or signals. These signals represent the findings of each individual node.
Regiment also provides the concept of region as in Abstract Regions [22]. Regions are used to
enhance the logical relationship between the nodes and data sharing between sensor nodes. The
compiler at Regiment converts the whole program into a form of simple readily program using
token machine technique where nodes achieve internal sensing and able to receive signals from
neighbor nodes [24].
In addition, Regiment applies multi-stage programming mechanism to support the use of different
programming languages that are not maintained by the given program [26]. Also, it approves the
use of generics to qualify the program to pass any data types as in C++. It supports three
polymorphic data types’ stream which represents the changes in nodes state, space which
represents the real space with a given value of specific type and event which represents the events
that have values and happen at a specific time. The concept behind streams and events is founded
in Functional Reactive Programming (FRP); see [27] for more details. Since Regiment is
completely functional language, the values of stream, event and space are treated as formal
parameters where they can be returned from function and passed as arguments [24].
3.2. Node-Independent Approach
In Contrast, node-independent approach or equivalently “Database approach” is another type of
high-level abstractions. This approach used to distributes nodes in a network using independent
technique [20]. For Example, TinyDB as in [27] is a query processing system which mainly
focuses on improving the energy consumption by controlling tested data. The network treated as
one database system where users are able to retrieve information by using SQL-Like queries.
This approach should obey what called homogeneous network where all nodes must have same
capabilities before testing to achieve the desire result. In TinyDB, data gathered from sensing
nodes is actually used as an input in sensor table and system user can access these entries by
using SQL-like queries [22].
Cougar is another example of node-independent abstractions. Cougar system is used to test a
query processing in sensor networks [29]. Each Cougar system is consist of three level,
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Queryproxy, Frontend Component and Graphical user Interface. Frist, Query proxy is a tiny
database element that runs in sensor nodes to track and perform system queries. Second, Frontend
element is used to setup connections between sensor nodes in one network and another nodes in
different networks. Third, graphical user interface (GUI) is used to perform queries on sensor
networks [29]. Cougar helps to retrieve the data and system behavior. However, it is too difficult
to deal with complex applications like tracking system using this technique [28]. Cougar is
founded on routing tree which used to attain energy efficiency [22].
Although Node-independent abstraction delivers very simple user interface, it is still not suitable
for applications that require a lot of control flow. Thus, the main objective in node-independent
approach is to deliver abstraction in order to enhance the sensing type of WSN applications. On
the other hand, the main objective for node-dependent approach such as Kairos and regiment is to
deliver a wide range of WSN applications that need parallel computations. We summarize the
features of each programming abstraction of related work in Table1.
Table 1. Mapping WSN macro-programming abstractions with corresponding features
4. PROPOSED SOLUTION
The main objective of this work is to introduce a new programming model that is extremely
robust to sensor nodes failures.
Although there are several models focus on node-dependent abstractions, we would be working
on coding the actual sensor nodes to find the best routing path in different networks topologies.
Our proposed work will employ a model of eventual consistency, the sensing nodes are able to
deliver the most accurate result even if an internal node is not assured to be reliable.
Therefore, adopting eventual consistency mechanism in our programming model might lead us to
new outputs. Figure 2 illustrates the flow chart of our proposed model.
Key Feature
Kairos
Regiment
Cougar
TinyDB
Node-
Dependency
Node-
dependent
Node-
dependent
Node-
independent
Node-
independent
Programming
Model
Sequential
Functional
SQL-like
Queries
SQL-like
Queries
Data Access
Sharing the
Data
Sharing the
Data
Database
Database
Communication
Space
Neighborhood
based group
Logical
group
Whole
network
Whole network
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Fig.2 A Flow Chart of Proposed model
The proposed work is summarized in the following steps:
1. Identify Node: Considering the node attributes, a test signal is sent throughout the network to
recognize the number of active, sleep and failed nodes. Failure in acknowledgement from any
of the nodes leads to the node being understood as compromised or a filed faulty node.
2. Using tri level logic: Using a sequence of if-else statements, we built a set of protocols to
check the consistency of the nodes by their optimal distance and try to identify their master
node. A positive affirmation from any of these logistics leads to the understanding of eventual
consistency in between the nodes(we already know the node location and activity from step
2)
3. Routing protocol: Considering the active and functional nodes in the network, we can start
routing protocol. The routing can be done by the implementation of the basic routing
algorithms like the Dijkstras balance and consideration of the Euclidian distance.
The proposed work can be considered as a programming model to find the best routing path in
WSNs. The steps above help one determine whether all the nodes in a network are active or
functional which allows us to develop a consistent model for the distribution of nodes. The
proposed work we have shown previously can be applied in various node architectures. Some of
which we have shown in the simulation results later in this paper.
5.SIMULATION SETUP
In this simulation, we are going to explain how the proposed programming model helps us to find
the best routing path from selected source to a selected destanition. Consider that we have
different number of nodes, which are placed randomely in a network. If one node failed, the
system still able to deliver the most accourate result. In this programming model, we can define
the number of input and output for each node and the location of source and the destination nodes.
Initially all nodes are in a sleep state and placed randomely on a screen. Then, we send a test
signal to all nodes to make sure that all the nodes are working. Once the nodes are distributed, the
option window allows to choose the various default set values or change the routing algorithms
based on the need. It allows one to choose the actual coding of network to find the shortest path,
the best possible routing path or find the maxiumu routes by running them on the same network
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topology, as shown in Figure 3 and Figure 4. After all we are working on a new programming
model where the simulation allows choosing the best routing model based on the consistency of
the nodes distrubutions.
Figure 3: Options window to choose default set values for the proposed model
In Figure 4, all nodes are distributed randomly in a network in order to find the best possible
routing path. Though this is based on one of the routing algorithms from option menu. Almost
every WSNs is immune to have a few failed nodes or compromised nodes. Keeping such
inconsistencies in mind we try to develop a new model that enable users to select the best routing
path in any network just by considering node attributes and the consistency characteristics of
sensor networks.
Figure 4: The simulation of the proposed model shown the best possible path from source node to
destination node
6. Analysis of Results
We have applied the our model that proposed in the preceding section, and we have tested it to
find the best possible routing path. As stated earlier, our model let the user to select the routing
algorithm to find the shortest path, best possible path in the same network topology. In this
section, we sketch the details of our programming model by examining it in three different well-
known network topologies star network, fully connected network and mesh network.
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6.1 Heuristic only Algorithm
Heuristic algorithm is used to find the routing path fast and easily with no promise that the best
routing will be found. It is simply minimize the number of explored nodes by estimating the
remaining cost.
Figure 5, 6, and 7 show how the algorithm work with different network topology, star, fully-
connected and mesh networks respectively. The left side shows the mechanism of finding the
routing path in some sequential steps. The right side shows the routing path from source node to
destination node.
Figure 5: Applying heuristic algorithm to star network
Figure 6: Applying heuristic algorithm to fully-connected network
Figure 7: Applying heuristic algorithm to mesh network
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As seen on the above graphs, the routing starts with exploring the cost of the nearest node and
estimates the cost for other nodes. Heuristic algorithm is used to find the routing path close to the
best one fast and easily. In some cases, the estimated cost is equal to the actual cost as shown on
table2. However, we cannot guarantee that this routing algorithm deliver the best routing path
until it is proven.
6.2 Costs- Minimization Algorithm
Cost minimizing algorithm is used to minimize cost without exploring more nodes. It is simply
find the best possible routing path in shortest time.
Figure8,9, and 10 show the cost minimizing algorithm with star, fully-connected and mesh
networks respectively. The left side of graph, shows how the model able to find the shortest
routing path without consuming too much energy. The right side shows the routing path from
source to destination.
Figure 8: Applying cost-minimizing algorithm to a star network
Figure 9: Applying cost-minimizing algorithm to fully-connected network
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Figure10: Applying cost-minimizing algorithm to mesh network
From above graphs, cost-minimizing algorithm is used to reduce the average cost
required to find the best routing path. Basically, it is aim to reduce the number of routing
steps in order to find the best possible path.
6.3 Dijkstra only Algorithm
Dijkstra’s algorithm used to find the cheapest routing path between two nodes. It simply
does not perform cost estimation, but basically consider the actual current cost of the
path from source node to destination node.
Figures 11,12, and 13 show dijkstra’s algorithm with star, fully-connected and mesh
networks respectively. The left side of the graph calculated the number of steps to find
the cheapest path from source to destination nodes, and the right side show the final
routing path.
Figure11: Applying Dijkstra’s algorithm to star network
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Figure12: Applying Dijkstra’s algorithm to fully-connected network
Figure13: Applying Dijkstra’s algorithm to mesh network
Dijkstra’s algorithm starts from source node as illustrated on above graphs and explore another
path in each repetition. Then the process will be repeated until it discovers the whole network and
calculate the sum of their costs in order to obtain the actual cost of the routing path.
7. Discussion of Results and Evaluation
The main objective of this paper is to provide a new programming model in WSNs to find the
best routing path. We adopt a model of eventual consistency; the system is able to deliver the
most accurate result even if an internal node is not assured to be reliable. Moreover, we consider
three different routing algorithms, first is heuristic algorithm which is simply minimize the
number of explored nodes by estimating the remaining cost. Second, is cost- minimizing
Algorithm which is basically minimizing the average time needed to discover the best possible
routing path without exploring more nodes. Third is Dijkstra’s algorithm which is used to find the
actual cost from source to destination nodes.
We tested our model on three different topology star, fully-connected and mesh networks for
better understanding. The proposed model can be applied in different networks topologies such as
random network as in Figure 4. However, since the random network has a random number of
nodes we cannot compare it with any other well-known topologies. Figure 14 and Table 2 show a
comparison between heuristic, cost-minimizing and Dijkstra algorithm in term of cost.
International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.4, July 2013
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Figure14. How the exploration time is changing when changing algorithm
Table 2. Number of steps needed in different topology applied in different algorithm
The above table shows how the exploration time changes with different routing algorithm. With
star network, the best routing algorithm to use is heuristic algorithm because it has the fewest
steps to discover the routing path. In mesh network, time needed to find the best routing path with
cost-minimizing algorithm and dijkstra’s algorithm are same.
8. Conclusion
In this paper, we have reported the results of our programming model to find the routing path in
WSNs and investigated the performance of our model when applied in different routing
algorithms and multiple network topologies and further scope for improvement. The goal of the
research is to provide a new programming model that can be applied in different network
topologies to deliver the most accurate result. Research must carry on in all the capacities of
WSN programming model. Still there are some qualities and features are missing to let WSN
programming model reach its best level of performance. However, we believe that WSN
programming model is a huge step towards deploy more applications in WSNs domain.
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Authors
Mrs. Abrar Alajlan: is a Ph.D. student department of Computer Science and Engineering at the
University of Bridgeport, Bridgeport, CT. She earned a master's degree in MBA with a concentration in
Information Systems (IS) from Troy University, Troy, AL in 2011. She received a BS in Computer
Science from Umm Al-Qura University, Makkah, Saudi Arabia. Abrar's interests are in Wireless Sensor
Network (WSN), Network Security, Mobile Communication.
Dr. Khaled Elleithy: is the Associate Dean for Graduate Studies in the School of
Engineering at the University of Bridgeport. His research interests are in the areas of,
network security, mobile wireless communications formal approaches for design and
verification and Mobile collaborative learning. He has published more than two
hundreds research papers in international journals and conferences in his areas of
expertise.
Dr. Elleithy is the co-chair of International Joint Conferences on Computer, Information, and Systems
Sciences, and Engineering (CISSE).CISSE is the first Engineering/ Computing and Systems Research E-
Conference in the world to be completely conducted online in real-time via the internet and was
successfully running for four years. Dr. Elleithy is the editor or co-editor of 10 books published by
Springer for advances on Innovations and Advanced Techniques in Systems, Computing Sciences and
Software.
Dr. Elleithy received the B.Sc. degree in computer science and automatic control from Alexandria
University in 1983, the MS Degree in computer networks from the same university in 1986, and the MS
and Ph.D. degrees in computer science from The Center for Advanced Computer Studies in the
University of Louisiana at Lafayette in 1988 and 1990, respectively. He received the award of
"Distinguished Professor of the Year", University of Bridgeport, during the academic year 2006-2007.
Mr. Varun Pande is a Graduate Research Assistant currently attending the University of
Bridgeport as a PhD candidate in Computer Science and Engineering. He graduated from
the University of Bridgeport with a Master in computer Science in May of 2012. He had
worked as a CSR representative at TATA Power, during his Bachelor in computer
science and Information Technology. Currently and for the past two years he has been a
Graduate Assistant and taught Labs on Wireless Sensor Communication using MICA z
Motes. His research interests are Computer Vision, Image Processing, Parallel processing and Wireless
Sensor Networks. He hopes to share my experiences, research and knowledge with other graduates and
professionals to work in a collaborative research for a Better tomorrow!
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