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Abstract— Wireless sensor networks (WSNs) have gained a lot of
considerations in recent years and have significant impacts on
different application areas. Wireless sensors have been
successfully deployed in different computing environments to
measure, gather and process the raw information in the sensing
area to the observers. Sensor networks provide infinite
opportunities, but at the same time pose rough challenges due to
the sensors’ characteristics and the operating conditions of these
sensors. This paper provides an extensive study of the current
state-of-art in programming wireless sensor network, presenting
a classification of programming levels in the field and
highlighting some likely programming challenges and research
future directions.
Index Terms— Macroprogramming, Programming
Approaches, Programming Challenges, Sensor Network.
I. INTRODUCTION
Wireless sensor networks (WSNs) have gained a lot of
considerations in recent years and have significant impacts on
different application areas. They are composed of tiny
embedded 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]. Most
often, WSN is utilized for the ease of deployment and
enhanced flexibility of the network. Furthermore, it supports
low cost dense monitoring of hostile environments as well as
disaster relief, medical care and military surveillance [2].
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 significantly growing [3].
Manuscript received February 9, 2014.
A. Alajlan is with the Department of Computer Science and Engineering,
University of Bridgeport, Bridgeport, CT, USA ( phone: 2039082241; e-mail:
aalajlan@bridgeport.edu)
K. Elleithy is with the Department of Computer and Electrical
Engineering, Faculty of Engineering, University of Bridgeport, CT 06604,
USA (e-mail: elleithy@bridgeport.edu).
Several programming approaches have been proposed to
assist WSNs programming. Two broad classes of WSNs
programming models have been explored lately; local behavior
and global behavior abstraction [4]. In local behavior
abstractions, the application has to be programmed in details at
the node-level and the programmers need to synchronize the
program flow between the sensing nodes and maintain the
routing code manually. In contrast, global behavior
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 programmers. The main objective behind high-
level approach is the ability to treat a group of sensors or the
entire network as one single unit rather than programming each
node individually [5].
The main contribution of this work is to provide an
extensive survey on taxonomy of programming approaches for
wireless sensor networks. Our work also captures the
programming requirements 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
programming. Section III, provides taxonomy on programming
approaches for WSNs. An in-depth look on each level of the
programming approaches is presented in Section IV, V and VI.
Analysis and evaluation of each model is discussed in Section
VII. Section VII investigates research challenges and future
direction of programming WSNs. Conclusion in provided in
Section IX.
II. REQUIREMENTS FOR SENSOR NETWORK
PROGRAMMING
It is obvious that sensor networks can be used in multiple
applications 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 programming.
A. Scalability
Many sensor network applications deploy hundreds or even
thousands of nodes collaborating to achieve desired goal(s);
High-Level Abstractions in Wireless Sensor
Networks: Status, Taxonomy, Challenges, and
Future Directions
Abrar M. Alajlan, Khaled M. Elleithy, Member, IEEE
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thus, scalability is one of the major designing attributes in
sensor networks applications [6]. A scalable sensor network is
representing the ability of the network to maintain its
performance even when the network size has changed [7]. 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 respect to geography is defined as the ability to
perform correctly in different geographical areas under
different environmental conditions [8]. Since we cannot
predetermine the location of sensor nodes and we cannot
assure the lifetime of sensor nodes, the programming model
should help programmers in such a way to design scalable
applications that are able to deliver accurate results [7].
B. 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 unknown [9] , [10]. Thus, localization in
sensor network, the determination of the geographical
locations of sensors, is one of the important aspects for sensor
network programming [11]. Many localization techniques have
been proposed recently, either by deploying self-localized
technique or by installing a Global Positioning System (GPS)
device in each node to determine the exact location of the
sensor node.
C. Failure-Resilience
Failure –resilience or (Fault-tolerance) is one of the most
challenging requirements in programming wireless sensor
networks [12]. 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 [13].
Thus, the system should keep performing properly even
after unreliable communication, node failures, link failures, or
unavailability of the network due to misbehaving nodes
[14][15].
D. 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 [16].
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. [17].
Thus, the programming model for sensor networks should
deploy some applications that attain a proper level of energy-
efficiency and able to deliver demanded results [18].
E. Collaboration
Collaboration is another important characteristic of
wireless sensor applications. WSNs applications have been
growing recently. These applications vary in terms of size and
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 together to complete the desired tasks [19] [20].
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 functional even after one node
failed.
F. Time Synchronization
Time-synchronization between nodes is another essential
requirement for sensor programming execution. Many WSNs
applications such as tracking application and implementation
of TDM require a timer synchronization that is maintained at
each sensor node [21].
Clock synchronization is a process used to ensure an
accurate scheduling between nodes with no collision [22].
Moreover, WSNs have limited power therefore; time-
synchronization technique helps to reduce the power
consumption by passing some nodes off from time to time
[23].
III. PROGRAMMING APPROACHES FOR WSNS: A TAXONOMY
In this section we present taxonomy of the programming
high- level approaches for WSNs. Figure 1 depicts the entire
taxonomy that categorize the wireless sensor network
programming high-level approaches into group level and
network level abstractions.
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 programmer 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 abstraction 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.
3
IV. 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
(Neighborhood Based) or it can be grouped logically [24].
A. Physical Group
The notion of physical group or ―neighborhood based
group‖ is basically 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 ―localized algorithms‖ where
the interaction between participating nodes is limited to their
neighbors as in [25].
Hood
Hood is one example of a neighborhood- based
programming abstraction where a given node is limited to
communicate and share data with neighboring nodes only. This
physical closeness is determined by the physical distance or
the number of hops between the sensor nodes [26].
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.
Abstract Region
Another example of a neighborhood-based group abstraction is
Abstract Region which relies on the concept of grouping the
nodes in mesh, spanning tree or could be based on the
geographic locations of these nodes as shown on Figure 6 [27].
Abstract Regions as in Hood, cannot group nodes from
different network. Moreover, this model can be adapted within
different network conditions to attain different levels 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.
A. 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, sensor inputs, or perform the same tasks [14]. Unlike
neighborhood 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 location of nodes [28].
Logical group-based, cannot cross multiple networks at the
same time which means we cannot reuse the existing sensors
without reprogramming them [29].
EnviroTrack
One example of Logical based group is EnviroTrack. It is
an application used to track programs where a set of nodes that
detect the same event are grouped together [30].
SPIDEY
Another example of logical based group is a SPIDEY
language where a set of nodes are grouped based on their
shared properties [26]. In SPIDEY language, each node has
both static and dynamic attributes which are used to determine
the nodes logical neighbors as in [26]. 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.
V. NETWORK-LEVEL ABSTRACTION
Recently, several macro-programming abstractions have
been introduced. 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 [14].
This approach helps the programmers to emphasize on
improving the semantics of the program instead of studying the
characteristics of the programming environments [14].
One type of macroprogramming is node- dependent
abstraction where the programmers define the global behavior
Fig 1: A Taxonomy of programming global behavior approaches in wireless
sensor networks
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of the entire system as a set of nodes that can be treated
simultaneously in one program [4].
A. Node Dependent Approach
Node-dependent approach is intended to deliver more
flexibility than group level approaches. This approach allows
programmers to define the global behavior of the computation
in terms of nodes and their states [31]
Kairos
Kairos is a node-dependent abstraction where the
neighboring nodes can be computed in parallel and
communicate using common requests at specific nodes [32].
Kairos has a centralized programming environment which is
translated later by the compiler to many executable effective
nodal programs [32]. Kairos enhances the use of sensor
programming languages by providing three simple
mechanisms: node abstraction, and accessing data on a remote
node [31].
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 [32].
Regiment
Another example of node-dependent abstraction is Regiment, a
purely macroprogramming functional language that allows the
direct use of program state [2].
However, it uses what is called monads; described in more
detail elsewhere in [33]. In Regiment, programmers deploy
groups of data stream or what is called signals. These signals
used to represent the finding of each individual node.
Moreover, Regiment applies a multi-stage programming
mechanism to support the use of different programming
languages that are not maintained by the given program [34].
Also, Regiment enables the use of generics that qualify the
program to pass any data types as in C++.
VI. ANALYSIS AND EVALUATION
In this section, we focus on the most important strategies
that are used in each programming model to fulfil the
programming requirements discussed earlier.
A summary of how each level of the programming approaches
addresses these requirements is shown in the next three tables
below.
Table I 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. 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 [2]. In addition, Kairos adopts
an eventual consistency method to deliver the most accurate
result even if an internal node is not assured to be reliable.
Kairos also uses a cashing technique to reduce the
communications and power consumption.
Evaluation Factor
Network-Level
Node-Dependent
Kairos
Regiment
Scalability
No evidence for support
Purely functional language. Permit the use
of fold, map functions.
Localization
Each node is only
responsible for localizing
itself
Use Region for the purpose of localizing
sensing
Failure-Resilience
Eventual consistency
Anchor ― leader‖ is an object persists
across node failures
Energy-Efficiency
Caching
Purely functional language. Permit the use
of fold, map functions
Collaboration
Implicitly express both
distributed data flow and
control flow.
Capable of expressing groups of nodes with
geographical, and logical relationships
Time Synchronization
Automatically synchronizes
nodes when a checkpoint is
taken or restored.
Use signals to represent the finding of each
individual node.
Table I: Evaluation of macroprogramming models for sensor networks
5
Table II shows how the programming requirements
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 [14].
Caching technique is used in several programming models at
this level to reduce the communications between sensing nodes
and helps to save energy [27] [30].
Caching and abstract region are employed in Hood to
improve the communication failures by replacing the failed
data with the old cached one. [14].
However, SPIDEY utilizes redundancy mechanism to avoid
flooding the whole program and to limit the propagating of
information [32].
There are some components or functions attached to each
programming model to improve localization: Hood uses
mirror to reflect node locations or time synchronization
services [30]. In this case, abstract region starts with neighbor
discovery where each node initiates the process of discovering
the location of its neighbors [27]. As the tracked objects move
in EnviroTrack, the location of participating nodes has to be
known by using some functions like Location: avg (position)
[30]
From the previous analysis we can conclude the all these
requirements are dependent and related to each other. Indeed,
each single requirement can support or work in favor of other
requirements. For example, data sharing through multiple
nodes improves the scalability, collaboration and also energy-
efficiency.
Moreover, energy-efficiency and scalability is also can be
improved by reducing the communication between sensing
nodes.
VII. FUTURE RESEARCH DIRECTIONS AND
PROGRAMMING CHALLENGES FOR WSNS
As stated earlier, many programming approaches have
sought the development of programming support to address
the programming requirements of WSNs. In this section, we
highlight 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.
A. Reprogramming
Several programming approaches have been introduced and
discussed 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 Reprogramming
The network programming requirements might change over
time, and this change could be parameter changes or
reprogram the entire system. 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 [35].
B. Heterogeneity
Evaluation Factor
Group-Level
Physical Group
Logical Group
Hood
Abstract Region
EnviroTrack
SPIDEY
Scalability
Supported through
data sharing
Supported
through data sharing
etc.
Supported through
data sharing etc.
Supported through
data sharing etc.
Localization
Use mirrors to reflect
location.
Done at neighbor
discovery stage
As the tracked
objects move, the
location of have to
be known.
physical location of
each node should be
identified when
creating node.
Failure-Resilience
Caching :substitute the
data with old cached
data
Caching: substitute
the data with old
cached data
Dynamic group
management and
leader election
Utilize redundancy
mechanism
Energy-Efficiency
Power consumption
supported through data
sharing.
Supported through
data sharing
Caching low-level
control knobs.
through data sharing
Caching ("freshness
threshold")
Supports aggregation
at group level through
data sharing etc.
Collaboration
Asymmetric Group
definition and
operations on neighbor
Group definition and
operations on group.
Operation include
enumeration using
iterator and MPI-like
reduction
Dynamic group
definition and
operations on group
Group definition and
operations on group
Time Synchronization
Use mirror for time
synchronization
Use timeout
mechanism,
Timer handler to
executes one
iteration at time
Contains a time-
period attribute at
each node.
Table II: Evaluation of group level abstractions in WSNs
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In WSNs, the basic form of heterogeneity is deploying
multiple different 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 [36].
Heterogeneity in WSNs has two forms: physical
heterogeneity and logical heterogeneity.
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.
C. 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 designers need to balance between energy
consumed and some quality services such as accuracy and
error rates to get efficient results with a satisfying quality.
Quality is a very crucial element in designing sensor network
application since there are certain actions will be taken
according to the sensed result. [14].
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 considerable 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.
VIII. CONCLUSION
In this paper, we have provided taxonomy of programming
high-level abstractions in wireless sensor networks. Two
different levels of programming approaches have been
discussed: 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 service.
Research must carry on in all capacities of sensor networks
programming model to address these challenges and we
believe that advances in WSNs programming models will
facilitate deploying energy efficient, reliable, and accurate
applications in the WSNs domain.
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Abrar M. Alajlan: is a Ph.D. candidate in computer science and
engineering at the University of Bridgeport with research interests primarily
in wireless sensor networks. Her work focuses on programming the wireless
sensor networks and deploys them in hostile environments. Her most recent
working paper explores a new programming model to simulate wireless sensor
networks to find the best routing path. Abrar Alajlan graduated from Troy
University with 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
Dr. Khaled Elleithy is the Associate Dean for
Graduate Studies in the School of Engineering at
the University of Bridgeport. He has research
interests are in the areas of network security,
mobile communications, and formal approaches
for design and verification. 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 the 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 six years. Dr. Elleithy is the editor or co-editor of 12
books published by Springer for advances on Innovations and Advanced
Techniques in Systems, Computing Sciences and Software.
