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Programming Models for Wireless Sensor Networks: Status, Taxonomy, Challenges, and Future Directions

International Journal of Scientific & Engineering Research, Volume 6, Issue 5, May-2015
ISSN 2229-5518
Programming Models for Wireless Sensor
Networks: Status, Taxonomy, Challenges, and
Future Directions
Abrar Alajlan and Khaled Elleithy
Abstract Wireless sensor networks (WSNs) play an important role in different application areas and have been successfully deployed in different
computing environments. However, programming sensor network applications is extremely challenging as the applications becoming more complex.
Some of these challenges are due to the sensors’ characteristics and others are due to the operating conditions of these sensors. Recently, researchers
have proposed diverse programming approaches to mitigate these challenges and make WSN programming more flexible and much easier.
This paper provides an extensive survey of the state-of-art in wireless sensor network programming models, focuses on a classification of programming
levels in wireless sensor networks and capturing some likely programming challenges and research future directions.
Index Terms Sensor network,Wireless sensor network (WSN), Programming approaches, Macroprogramming, Wireless sensor networks taxonomy,
Evaluation,Programming challenges.
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ypically, wireless sensor networks are composed of tiny em-
bedded devices, each of which has radio transceiver to send
or receive packets, processor to schedule and perform tasks, and
power source to provide energy for the sensor [1]. Wireless sensor
networks applications contain a large number of sensor nodes used to
transmit and forward data between sensing nodes and the sink or
base station [2]. Most often, WSN is utilized for the ease of deploy-
ment and enhanced flexibility of the network. Furthermore, it sup-
ports low cost dense monitoring of hostile environments as well as
disaster relief, medical care and military surveillance [3].
The advantage of being able to place remote sensing nodes
without having to run wires and the cost related to it is a huge gain.
As the size of the circuitry of WSNs is becoming smaller along with
the lower cost, the chances of their field of applications are signifi-
cantly growing [4]. Most sensors, depending on the requirements, are
battery powered and hence conserving the energy of these sensors is
very crucial. Several programming approaches have been proposed to
assist WSNs programming. Two broad classes of WSNs program-
ming models have been explored lately; local behavior and global be-
havior abstraction [5]. In local behavior abstractions, the application
has to be programmed in details at the node-level and the program-
mers need to synchronize the program flow between the sensing
nodes and maintain the routing code manually. In contrast, global be-
havior abstractions or equivalently “High-level abstraction” has
emerged as one of the most important aspects in sensor networks
where it is applied to hide the internal operations from system pro-
grammers. The main objective behind high-level approach is the abil-
ity to treat a group of sensors or the entire network as one single unit
rather than programming each node individually [6].
The main contribution of this work is to provide an exten-
sive survey on taxonomy of programming approaches for wireless
sensor networks. Our work also captures the programming require-
ments and uses them to evaluate each of the programming models.
This paper also covers some open problems and challenges that need
further investigation to make wireless sensor programming reaches
its best level of performance and makes it highly usable and efficient.
Section II, identifies the requirements for sensor network pro-
gramming. Section III, provides a taxonomy on programming ap-
proaches for WSNs. An in-depth look on each level of the program-
ming approaches is presented in Section IV, V and VI. Analysis and
evaluation of each model is discussed in Section VII. Section VII in-
vestigates research challenges and future direction of programming
WSNs. Conclusion in provided in Section IX.
It is obvious that sensor networks can be used in multiple applica-
tions that can be deployed in diverse environments. Moreover, it is
very easy to modify the internal functionality of sensor networks to
perform different tasks to support many sensor network applications.
In this section we discuss important requirements for sensor network
2.1 Scalability
Many sensor network applications deploy hundreds or even thou-
sands of nodes collaborating to achieve desired goal(s); thus, scala-
bility is one of the major designing attributes in sensor networks ap-
plications [7]. A scalable sensor network is representing the ability of
the network to maintain its performance even when the network size
has changed [8]. In WSNs scalability can be defined in two terms;
size and geography. Scalability with respect to size states that if the
application works properly with a few nodes, it can perform well
with thousands of nodes. On the other hand, the scalability with re-
spect to geography is defined as the ability to perform correctly in
different geographical areas under different environmental conditions
[9]. Since we cannot predetermine the location of sensor nodes and
we cannot assure the lifetime of sensor nodes, the programming
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model should help programmers in such a way to design scalable ap-
plications that are able to deliver accurate results [8]. The location in-
formation of distributed nodes needs to be known so as to exchange
the sensed data between sensor nodes [10].
2.2 Localization
In wireless sensor network applications there are hundreds of nodes
deployed in some areas such as underwater, in the middle of desert,
or in inaccessible terrains, so their locations are random and un-
known [11] , [12]. Thus, localization in sensor network, the determi-
nation of the geographical locations of sensors, is one of the impor-
tant aspects for sensor network programming [13]. Many localization
techniques have been proposed recently, either by deploying self-lo-
calized technique or by installing a Global Positioning System (GPS)
device in each node to determine the exact location of the sensor
node. Moreover, localizing algorithms can be classified into two
Range-based algorithms: where each node is equipped with
hardware measurements, so the location of each sensor node
can be determined by calculating the distance of the selected
sensor node with its neighboring nodes [14].
Range-free algorithms: where each node should determine its
estimated location, and the ideal radio range of sensors.
Consequently, range-based algorithms provide more information
compared to range-free algorithms; however, it is more expensive
since there is some hardware measuring units attached to each sensor
2.3 Failure-Resilience
Failure –resilience or (Fault-tolerance) is one of the most challenging
requirements in programming wireless sensor networks [16]. Sensors
are usually deployed in inaccessible terrains where people cannot
reach the sensor nodes at that place. Some nodes might fail due to
the resources limitation, hardware fault or it could be an intrusion
from attackers. The failed sensors may lead to inefficient functioning
of the network [17].
Thus, the system should keep performing properly even after unreli-
able communication, node failures, link failures, or unavailability of
the network due to misbehaving nodes [18, 19]. Some techniques
should be adapted to indicate that the node is not working in a proper
way [20]. It could be done by monitoring the status of each node or
using the power control technique [17, 19].
It is a very challenging requirement for the programmers to develop a
sensor application that is resilient to failures and adaptive to the un-
expected environmental changes which is too hard to provide error
handling for every failure [18].
2.4 Energy-Efficiency
Energy efficiency is one of the most important issues in designing
sensor networks. The overall design of sensor networks should
mainly emphasize on enhancing the performance in terms of reduced
power consumption. The total lifetime of a battery-powered sensor
networks is limited by the non-rechargeable battery's capacity and
each sensor node is equipped with a limited computation processor
to perform its task [21]. Energy efficiency is very important factor in
developing WSNs applications especially for continuous monitoring
applications such as disaster monitoring, military surveillance and
remote patient monitoring, etc. [22],[23].
2.5 Collaboration
Collaboration is another important characteristic of wireless sen-
sor applications. WSNs applications have been growing recently.
These applications vary in size and the number of nodes, from large
scale networks to the small ones. All nodes in one application need
to communicate in such a way so that the data from these sensors are
gathered and analyzed. Thus, collaboration between sensor nodes is
essential for these sensors to cooperatively and effectively work to-
gether to complete the desired tasks [24] [25].
Most of wireless sensor applications can be classified into two
Data collection type: where all data is collected and sent to the
main server such as habitat and environmental monitoring
Collaborative information processing: where the main task is to
convert the data gained from multiple sensor nodes to higher-
level information such as a tracking system applications [26].
Collaboration is not an independent requirement, it can support other
requirements. For instance, collaboration between sensor nodes may
reduce the failure-resilience where the sensing process remains func-
tional even after one node failed. Moreover, collaboration inside each
sensor group may reduce data transmission which is in turn will re-
duce the consumed power [18].
2.6 Time Synchronization
Time-synchronization between nodes is another essential require-
ment for sensor programming execution. Many WSNs applications
such as tracking application and implementation of TDM requires a
timer synchronization that is maintained at each sensor node [27].
Clock synchronization is a process used to ensure an accurate sched-
uling between nodes with no collision [28]. Moreover, WSNs have
limited power as discussed earlier; therefore, time- synchronization
technique helps to reduce the power consumption by passing some
nodes off from time to time [29]. Clock synchronization in sensor
nodes is generally required for many reasons such as:
To support the coordination and collaboration between sensor
To manage the sleep and active state for each node [30],
To avoid collisions between sensor nodes as used in TDMA
(Time division multiple access) [31], and
To reduce the differences between the clocks that is attached to
each node at any time [32].
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International Journal of Scientific & Engineering Research, Volume 6, Issue 5, May-2015
ISSN 2229-5518
In this section we present a taxonomy of the programming ap-
proaches for WSNs. Figure 1 depicts the entire taxonomy that cate-
gorize the wireless sensor network programming approaches into
low-level and high-level programming models. Low-level approach
mainly focuses on the use of an existing programming language to
provide flexible controls over nodes. TinyOS with nesC as will dis-
cuss later, is one of the well-known examples that falls into this sub-
class [33], [34].
Fig. 1. Taxonomy of programming approaches for WSNs .
The virtual machine that runs on each node is one of the interesting
approaches in this subclass. It is responsible for breaking tasks and
dynamically distributing them to each node.
High-level programming approach mainly focuses on simplifying the
collaboration between sensor nodes. One approach is to divide the
whole network into a set of groups and treat each group as a single
entity which is called “Group-level abstractions”. It helps the pro-
grammer to describe collaborative algorithms easily. This approach
is further divided into physical groups and logical groups. In physical
group, the network can be grouped based on the physical location of
the node, whereas the logical group is based on the shared properties
among nodes.
The other approach of high-level abstraction is network level abstrac-
tion or “macroprogramming abstractions” where the whole network
is treated as a single entity. It is an application centric-view, thus, it
helps the programmer to focus on the programming logics rather
than programming the platforms. Macroprogramming approach is di-
vided into two subcategories; node-dependent and node-independent
and we will cover each of them in the next sections.
3.1 Node-Level Abstraction
Node-level programming approach focuses on the use of an existing
programming language and abstracting hardware to provide a flexi-
ble control over the node.
3.1.1 Programming Languages
Application development at the node level is basically relying on the
use of an existing programming language. NesC and C are the most
well-known programming languages that are used for tiny embedded
systems [18].
NesC is a C based attached with a programming model that adds
some features such as a flexible concurrency and oriented applica-
tion design. NesC programming language uses a static memory allo-
cation (variables are allocated at the compile time) to simplify the
code and obtain an accurate result [34].
TinyOS as in [35], is a simple application specific operating system
used in embedded WSNs. TinyOS applications is written in NesC
programming language to limit the hardware resources and support
operations structures needed by sensors [36]. It is one of the most
popular operating systems that support several frameworks
applications based on a tiny node connected with the microcontroller
and sensors [33]. TinyOS is a graph of the following independent
Module which provides interfaces for configuration,
Configuration which is used to connect all component
Each of which has three concepts:
Commands are basically asking a component to perform
some tasks.
Tasks are performed internally at the component such as
initiating a connection or reading data.
Events are referring to the completion of that task.
One example of commands and events when initiating a sensor
reading as in getData(). This command will cause a later signal
dataReady() when data is obtained. These two concepts command
and event - are used between components, however; the tasks are
performed internally at the component [37].
3.1.2 Middleware
The key concept behind using middleware is to support the overall
performance of applications and to connect the application layer with
hardware and operating systems as shown in figure 2 below.
Middleware in WSN supports “reprogrammability” which is the
ability to break tasks and distribute these tasks to each node
dynamically [38]. Middleware helps applications programmer to
focus on the programming logic without caring too much about the
implementation details at the lower level. Moreover, middleware
provides a reusable code, thus, the programmer can execute a new
application without using complex and inefficient methods.
Furthermore, it supports system monitoring and integration [39].
IJSER © 2015
Application Layer
Transport Layer: Operating
Physical Layer: Network
Middleware for WSN
Security services
Domain Services
Integration services
Resource Management
Data Management
Code Management
Processing Support
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ISSN 2229-5518
Fig. 2. Reference of wireless sensor
networks middleware [38]
Virtual Machine
Virtual machine is one type of middleware where the application is
written in small segments and then distributes these segments
through the network using tailored algorithms. Therefore, the size of
the code transmitted to each node is reduced and the communication
amount between the server and each node is minimized as well. [40].
Mate [41], and ASVM [42] are stack oriented virtual machines that
run on TinyOS. These interpreter-based virtual machines provide an
application specific virtual machine which is employed to enhance
the flexibility and offer efficient programming environments [18].
In Mate, the codes are broken into small fragments that are injected
later into the network to build the total program. It has a scheduler to
adopt a FIFO based queue of contexts and encloses them with their
executions. It performs the execution by fetching the next byte code
at the fragments store and attaches it to its corresponding operation.
Mate uses a Trickle algorithm to improve the broadcasting speed and
to reduce the cost when nodes propagate new data. [41]
Another example of middleware is Impala [43] which supports mod-
ularity, adaptability to rapidly change environments as well as the re-
programmability. This virtual machine is deployed to provide inde-
pendent execution platforms where the programmers can use them to
write their codes [43].
The system architecture of Impala is illustrated in Figure 3, where the
lower layer holds ZebraNet application protocols and programs.
These application protocols employ different techniques to gather en-
vironment information and send it to the base station in peer to peer
transmission. The upper layer holds three middleware agents:
Application Adapter where the application is adjusted for var-
ious conditions in order to enhance the overall performance,
and energy efficiency.
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Application Updater is used to install the software updates on
each node.
Event Filtering captures events to initiate a sequence of oper-
In Impala there are five different types of events; Timer
Event, Packet Event, Send Done Event, Data Event, and De-
vice Event. To eliminate the programming complexity of syn-
chronizing two different handlers, the system will handle
them sequentially in case they arrived at the same time. [43].
Fig. 3. system architecture of Impala
3.2 Group-Level Abstraction
The main concept behind a group-level abstraction in WSNs
is to divide the whole network into small groups and perform
computations on those groups instead of dealing with each
single node. In a group- level abstraction, the network can be
grouped based on the physical locations of the nodes (Neigh-
borhood Based) or it can be grouped logically [44].
3.2.1 Physical Group
The notion of physical group or “ neighborhood based group “ is ba-
sically a node with its neighbor’s without paying any attention to the
properties of these nodes [18]. This technique is used to hide the
communication details between the nodes and it can be used in “lo-
calized algorithms” where the interaction between participating
nodes is limited to their neighbors as in [45].
Hood is one example of a neighborhood- based programming ab-
straction where a given node is limited to communicate and share
data with neighboring nodes only. This physical closeness is deter-
mined by the physical distance or the number of hops between sensor
nodes [46].
In Hood, all nodes in each group have to be in the same network and
if one node moves to another network then it is not a member of that
group. Figure 4, describes how a node becomes a neighbor of other
node. Node A, receive the data reported by node B and C, while it re-
ports its reading to node B. Also, B is a neighbor of D but D is not in
B’s neighborhood. A node can receive data from its neighbors and its
own data is send to its co-neighbor. One node can be assigned to read
location over one group, and read the temperature over other group.
To manage the complexity of these tasks, Hood provides an interface
to read the shared values of each neighbor [47]
Fig. 4. The Definition of
Neighborhood in Hood
Abstract Region
Another example of a neigh-
borhood-based group ab-
straction is Abstract Re-
gion which relies on the
concept of grouping the
nodes in mesh, spanning tree or could be based on the geographic lo-
cations of these nodes [48]. Abstract Regions as in Hood, cannot
group nodes from different network. Moreover, this model can be
adapted within different network conditions to attain different lev-
els of energy and bandwidth usage as well as the accuracy level of
shared operations. Also, each region is separated from other regions
and requires a specific implementation. Abstract Regions can be im-
plemented by following these phases:
Discovering Neighbors
In this phase, a node starts to discover its neighbors by either
sending a broadcast message or gathering the location of each
sensor node in the network. Since sensor nodes might move
from one network to another, this phase is a continuous
process to update any change in one region. However, a node
has the ability to deactivate this process at any given time to
reduce the power consumption when sending discovery mes-
This phase is used to address all nodes in one region and re-
turn the location of each of the nodes to help the participating
nodes in one region to communicate directly.
Data sharing
At this phase, all shared variable between nodes are repre-
sented as pair of key and value.
This phase is used to cut the shared variables across nodes in
one region and it is hidden from programmers [48].
3.2.2 Logical group
A logical group abstraction can be defined as a set of nodes that
share the same properties in sensor networks such as node types, sen-
sor inputs, or perform the same tasks [18]. Unlike neighborhood
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Node Neighbors
A C ,B
C None
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based, the logical group is considered to be a dynamic group since it
is based on the shared properties and not limited by the physical lo-
cation of nodes [49].
Logical group-based, cannot cross multiple networks at the same
time which means we cannot reuse the existing sensors without re-
programming them [50].
One example of Logical based group is EnviroTrack. It is an applica-
tion used to track programs where a set of nodes that detect the same
event are grouped together [51].
Another example of logical based group is a SPIDEY language
where a set of nodes are grouped based on their shared properties
[46]. In SPIDEY language, each node has both static and dynamic
attributes which are used to determine the nodes logical neighbors as
in [46]. SPIDEY delivers communication APIs, where a broadcasted
message is sent to a logical neighborhood instead of nodes that fall in
the same communication range. This technique helps programmers
to clearly specify the communication range and which nodes to select
as a neighbor. All logical neighbors are considered to be one group
with functionally related characteristics.
Thus, a group-based abstraction makes programming sensor network
model simpler since it performs in group level instead of node level
[44]. However, this approach is mainly designed for applications that
operate in a single network since we cannot across multiple networks
at the same times [50].
3.3 Network-Level Abstraction
Several macro-programming abstractions have been introduced
recently. Macro-programming systems or equivalently “networking
abstractions” considered to be high-level WSN programming model
where the whole sensors network is treated as a single system [18].
This approach helps the programmers to emphasize on improving
the semantics of the program instead of studying the characteristics
of the programming environments [18].
There are two different major classes of network-level abstractions.
One is a node-dependent abstraction which focuses on enabling the
programmers to define the global behavior of the system as a collec-
tion of nodes that can be treated simultaneously in one program. In
contrast, node-independent approach defines the system in indepen-
dent way as single unit [5]
3.3.1 Node Dependent Approach
Node-dependent approach is intended to deliver more flexibility than
node independent. This approach allows programmers to define the
global behavior of the computation in terms of nodes and their states
Kairos is a node-dependent abstraction where the neighboring nodes
can be computed in parallel and communicate using common re-
quests at specific nodes [52]. Kairos has a centralized programming
environment which is translated later by the compiler to many exe-
cutable effective nodal programs [52]. Kairos enhances the use of
sensor programming languages by providing three simple mecha-
nisms. First, node abstraction, where the programmer deploys net-
work nodes explicitly and names each node with an integer identifier,
yet these integer identifiers do not reflect the structure of the sensor
node. Hence, there is no need for the programmer to specify the net-
work structure when using Kairos [52]. Second, is the identification
of one-hop neighbors, where the programmers are able to use get
neighbors function to support wireless communication between the
nodes. When get function is called, a list of neighbors nodes are re-
turned, so the calling node can select which node to communicate
with. Third abstraction is accessing data on a remote node which im-
plies the capacity to access variables from selected node since Kairos
does not restrict any remote access to the variable nodes [53].
Kairos implements an eventual consistency method; by adopting this
feature the program is able to deliver the most accurate result even if
an internal node is not assured to be reliable. Thus, Kairos can be
used with many well-known programming languages such as python
as in [52].
Another example of node-dependent abstraction is Regiment, a
purely microprogramming functional language that allows the direct
use of program state [3]. However, it uses what is called monads; de-
scribed in more detail elsewhere in [54]. In Regiment, programmers
deploy groups of data stream or what is called signals. These signals
used to represent the finding of each individual node. Regiment also
provides the concept of region as in Abstract Regions [55], which
can be 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 which is a very simple model to
achieve internal sensing and able to receive signals from neighbor
nodes [3].
Moreover, Regiment applies a multi-stage programming mechanism
to support the use of different programming languages that are not
maintained by the given program [55]. Also, Regiment enables the
use of generics that qualify the program to pass any data types as in
C++. It supports three polymorphic data types:
Stream which represents the rapid changes in the nodes’
Space which represents the real space with a given value of
specific type.
Event which represents the events that have values and
happen at a specific time.
The concept behind streams and event is founded in Functional Re-
active Programming (FRP); see [56] for more details. Since Regi-
ment is completely functional language, the values of stream, event
and space are treated as formal parameters where they can be re-
turned from function and passed as arguments [3].
3.3.2 Node-Independent Approach
Node-independent approach or equivalently “Database approach” is
one type of high-level abstractions for sensor network programming.
This approach distributes the nodes in a network using independent
way and does not have any obvious abstraction for nodes [53].
TinyDB as in [56], is a query processing system that mainly focuses
on improving the energy consumption by controlling the tested data.
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The network is treated as one database system where users are able to
retrieve information by using SQL-Like queries. This approach
should adhere to what is called homogeneous network where all
nodes must have same capabilities before testing to achieve the desire
result. In TinyDB system, the sensed data are actually used as an in-
put of sensor table and system user can access these entries by using
SQL-like queries [52].
Cougar is another example of node-independent abstractions that has
been proposed early at Cornell University[57]. Cougar system is used
to test for query processing in sensor networks [58]. Each Cougar
system consists of three levels:
Queryproxy, a tiny database element that runs in sensor
nodes to track and perform system queries.
Frontend element which is used to setup connections be-
tween sensor nodes in one network and other nodes in dif-
ferent networks.
Graphical user interface (GUI) which is used to enable
users to perform queries [58].
Cougar drives query to the end nodes and all the computations are
achieved at the edge level to reduce the amount of transmitted data. It
also helps the system users to retrieve the data and system behavior.
However, it is too difficult to deal with complex applications like
tracking system using this technique [59].
Although Node-independent abstraction delivers very simple user
interface, it is still not suitable for applications that require a lot of
control flow.
In this section, we focus on the most important strategies that
are used in each programming model to fulfil the programming re-
quirements discussed earlier. A summary of how each level of the
programming approaches addresses these requirements is shown in
the next three tables below. Table 1 below summarizes how a node-
level abstraction addresses the programming requirements discussed
in section 2. Programmers at this level are able to deploy some fea-
tures to enhance the scalability of the program by using low-level in-
terfaces. Even though, these interfaces are flexible, they tend to be
complex in execution operations [51].
To maintain collaboration and synchronization in TinyOS, two com-
ponents are used: configuration to connect all components together
and module to perform the synchronous method as a FIFO queue.
Impala uses timer event signals to manage the collaboration and syn-
IJSER © 2015
Evaluation Factor
Programming Language Middleware
TinyOS/NesC Mate Impala
Programmers implement each feature
by using low-level interfaces.
Flexible but tend to be complex.
Can express a wide range of
Can express a wide
range of applications
Variable locations can be statically
compiled into the program
Can be extended to perform a
localization service.
Static location of com-
municating nodes
Restrictions allow the nesC compiler
to perform whole-program analyses
such as data-race detection to im-
proves reliability
Mate is concise programs
that are resilient to failure.
Ensures the resilient to buggy
or malicious capsules.
Adaptation to device
Autonomic behavior
which increases its fault
Restrictions allow the nesC compiler
to perform whole-program analyses
such as using aggressive function in
lining to reduce resource consump-
Efficient dynamic code
update : Small interpreter
Efficient dynamic code
Eliminating duplicate
components to be trans-
mitted over network.
Use configuration to wire interfaces
from several modules together.
Supported shared variables
that managed by concurrency
Time Synchronization
Use module, a part of the TinyOS for
timer service.
A scheduler component to
adopts a FIFO based queue
Timer Event signals at-
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ISSN 2229-5518
chronization between sensing nodes, whereas Mate installs concur-
rency manager and scheduler to maintain these requirements. Mid-
dleware examples listed above, deliver efficient mechanisms for sys-
tem updates to support dynamic applications and offer a great energy
saving [60].
Table 2. shows how the programming requirements are implemented
in each programming model at the group level abstraction. Since all
the programming models listed on table II
are group based abstractions, the scalability, collaboration and data
aggregation are supported through data sharing. Caching technique is
used in several programming models at this level to reduce the com-
munications between sensing nodes and helps to save energy [48, 47,
Caching and abstract region are employed in Hood to improve the
communication failures by replacing the failed data with the old
cached one.
However, SPIDEY utilizes redundancy mechanism to avoid flooding
the whole program and to limit the propagating of information [52] .
There are some components or functions attached to each program-
ming model to improve localization: Hood uses mirror to reflect
node locations or time synchronization services [47]. In this case, ab-
stract region starts with neighbor discovery where each node initiates
the process of discovering the location of its neighbors [48]. As the
tracked objects move in EnviroTrack, the location of participating
nodes has to be known by using some functions like Location: avg
(position) [51].
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ISSN 2229-5518
Table 3. shows the evaluation of macroprogramming approach based
on the programming requirements. The main approach to satisfy
scalability is to reduce the communication between the sensor nodes.
Cougar and TinyDB are the most well-known examples of node-in-
dependent approach. They push the query selection at the edge
(nodes) so the transmission data is reduced. Moreover, Cougar and
TinyDB extend their SQL so that users can express continuous sens-
ing tasks. Regarding to localization, Regiment provides the ability to
divide the tested area to spatial regions to facilitate the localization
and communication processes. Also, Regiment is resilient to failure
where the master node or (Anchor) in each region is responsible to
cover if a node fails or loses connectivity to others [3].
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Evaluation Factor
Physical Group Logical Group
Hood Abstract Region EnviroTrack SPIDEY
Scalability Supported through
data sharing.
through data sharing
Supported through
data sharing etc.
Supported through
data sharing etc.
Localization Use mirrors to re-
flect location.
At Neighbor discovery
stage discovering the
neighboring nodes
For tracking objects.
As the tracked objects
move, the location of
have to be known.
Static physical loca-
tion of each node
should be identified
when creating node.
Failure-Resilience Caching : improves
communication fail-
ures by substituting
the data with old
cached data
improve communica-
tion failures by substi-
tuting the data with
old cached data
Dynamic group man-
agement and
leader election
Utilize redundancy
Energy-Efficiency Power consumption
supported through
data sharing.
Supported through
data sharing
Caching low-level con-
trol knobs.
through data sharing
Caching ("freshness
Supports aggregation
at group level through
data sharing etc.
Collaboration Asymmetric Group
definition and
operations on
group neighbor
definition and
operations on
Group definition and
operations on
group Context label,
dynamic group
definition and
operations on
Time Synchronization Use mirror to create
some services as
time synchroniza-
Use timeout mecha-
nism, and will fail if
not completed within a
given time.
Timer handler as an
input to executes one
iteration per invoca-
Contains a time-pe-
riod attribute when
creating each node.
International Journal of Scientific & Engineering Research, Volume 6, Issue 5, May-2015
ISSN 2229-5518
Several programming approaches have been introduced and dis-
cussed in the past decades. However, there are many programming
challenges still unresolved and need further study to make the WSNs
programming valuable and effective ; thus, in this section we list
some of them and discus the future direction of programming WSNs.
5.1 Reprogramming
The network programming requirements might change over time, and
this change could be parameter changes or reprogram the entire sys-
tem. Also, wireless sensors might move from one network to another,
but the limited resources of these sensors may result in short-lived
systems. Thus, sensing nodes should have a dynamic reconfiguration
services to keep these sensors functional for a long time [61].
In order to create a useful and effective reprogramming system,
some requirements need to be addressed. First, time and space com-
plexity of reprogramming algorithm should correspond to the capac-
ity of sensor node. Second, since the sensing nodes have limited en-
ergy resources, the reprogramming system should be energy-effi-
cient. Third, reprograming requires delivering the code entirely even
though communications over wireless network are unreliable [62].
5.2 Heterogeneity
In WSNs, the basic form of heterogeneity is deploying multiple dif-
ferent types of sensors in one application, each of which performs
different task and has different energy and resources. Heterogeneity
in a WSN is used to improve the overall reliability and lifetime of the
network [40]. Heterogeneity in WSNs has two forms: physical het-
erogeneity and logical heterogeneity.
One example of physical heterogeneity is hierarchical architecture,
IJSER © 2015
Evaluation Factor
Node-Dependent Node-Independent
Kairos Regiment TinyDB Cougar
Scalability No evidence for support Purely functional lan-
guage. Permit the use of
fold, map functions.
Network query pro-
Queries selection at
(nodes) to Reduce
transmission data
Network query pro-
Queries selection at
Reduce transmission
Localization Each node is only re-
sponsible for localizing
Use Region for the pur-
pose of localizing sens-
Each node is only re-
sponsible for localiz-
ing itself
No evidence for sup-
Failure-Resilience Eventual consistency Anchor “ leader” is an
object persists across
node failures
No evidence for sup-
No evidence for sup-
Energy-Efficiency Caching Purely functional lan-
Permit the use of fold,
map functions
Acquisitional query
processor changes
sampling rate battery
lasts for lifetime.
In network query pro-
Collaboration Describe a resource ac-
cess as a variable ac-
Implicitly express both
distributed data flow
and control flow.
Region streams
Capable of expressing
groups of nodes with
geographical, and logi-
cal relationships
Collaboration can be
defined through a
Collaboration can be
defined through a
Time Synchronization Automatically synchro-
nizes nodes when a
checkpoint is taken or
Use signals to represent
the finding of each indi-
vidual node.
Nodes run a simple
time synchronization
protocol to agree on a
global time base .
The data is appended
at time intervals speci-
fied in the query
termed as epochs.
International Journal of Scientific & Engineering Research, Volume 6, Issue 5, May-2015
ISSN 2229-5518
where the upper level sensors are more powerful and have more en-
ergy and network resources than the lower ones. Physical heterogene-
ity in WSNs has three types [62]:
Computational Heterogeneity: where some nodes have more
computational power than others.
Link Heterogeneity: where some sensors have long distance
than others.
Energy Heterogeneity: where some nodes have more energy re-
sources than other nodes.
In contrast, logical heterogeneity is the case where each sensor has to
behave in different way to perform a specific task assigned by the ap-
plication [18]. One example of the logical heterogeneity is the usage
of generic role scheme to assign one task for each sensor node. These
roles are stated by a declarative configuration language; described in
more details elsewhere in [64].
From programming point view, how to deploy heterogeneous sensors
efficiently and how to program the entire system with these sensors
are the main concerns in developing WSNs applications.
5.3 Quality of Service
Quality of service is one of the important challenges in designing
wireless sensors applications. As stated earlier, wireless sensors are
equipped with limited energy resources. Accordingly, system design-
ers need to balance between energy consumed and some quality ser-
vices such as accuracy and error rates to get efficient results with a
satisfying quality. Quality is a very crucial element in designing sen-
sor network application since there are certain actions will be taken
according to the sensed result. For example, when detecting vulcanic
eruptions or sensing earthquakes before they hit, to change the be-
havior accordingly or issue an emergency alert, lack of accuracy and
large latency would make the application useless. If the information
gained from the sensor network is inaccurate, it may ruin the entire
application. Thus, the system designers should be able to maintain
the overall efficiency level as well as the quality of collected data
The above requirements and the demanding deployment environment
of wireless sensors make sensor programming the most challenging
task in developing wireless sensors applications. In spite of the con-
siderable effort carried out to let WSN programming model reach its
best level of performance, still there are several open problems that
need further investigation to make wireless sensor programming
highly usable and efficient.
In this paper, we have provided taxonomy of different programming
levels in wireless sensor networks. Three different levels of program-
ming approaches have been discussed: node level, group level and
network level. Several examples have been covered and evaluated
based on some programming requirements for each level. Designing
efficient programming models for WSNs has many challenges to
overcome such as reprogramming, heterogeneity, and quality of ser-
vice. Still there are missing some qualities and features to let WSNs
programming model reach its best level of performance.
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IJSER © 2015
... Parallel programming for distributed memory environments [16], Mainstream parallel programming [17], Multi-core clusters [18], Evolutionary multi-agent systems in Erlang [19], [20], High-performance computing [21], [22], Heterogeneous CPU-GPU architectures [23]- [25], Using MapReduce model in Big data [26], Presenting fast solver for structured linear systems [27], Evaluating approximate computing and heterogeneity for energy efficiency [28], Replicable parallel branch and bound search [29] and Wireless sensor networks [30], [31]. ...
... In the Node-dependent approach, a group of nodes can be considered at the same time in one single code. This approach permits the user to express the performance of a distributed system based on the nodes and their states [31], [44]. ...
Full-text available
In Wireless Sensor Networks (WSNs), clustering is often used to improve communication and routing. Therefore, clustering approaches highly attract several researchers since performing clustering saves energy, and energy efficiency is a significant goal in WSN. To beneficially adopt WSN technology, efficient application development is necessary. Therefore, a user-friendly programming abstraction is required to simplify the programming chore without sacrificing efficiency. Using suitable higher-level programming abstraction, it is neither obligatory for a programmer to be an expert in most fields related to WSN nor to be distracted from the application logic by focusing on low-level system issues. To ease the development of new clustering algorithms, a prefabricated algorithmic skeleton, namely SCW, is presented which only requires two functions to be filled in, i.e., to be implemented. The rest of the work (e.g., synchronization, sensing the environment, data aggregation, nodes’ energy calculations, and routing) will be handled by the proposed framework. Hence, SCW has the capability of performing a level of optimization in the background without user interference. By considering software metrics such as Lines of Code (LoC), Halstead metrics, and McCabe complexity while employing the proposed framework, one can implement a WSN clustering algorithm with fewer source lines of code, less programming effort, and difficulty, less time to understand and implement when compared to a built-from-scratch implementation. Although this algorithmic skeleton framework is proposed for implementation, to show its efficiency in this paper, we use the simulation environment.
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