Wireless sensor networks (WSNs) enable new applications and require non-conventional
paradigms for protocol design due to several constraints. Owing to the requirement for
low device complexity together with low energy consumption (i.e., long network
lifetime), a proper balance between communication and signal/data processing
Capabilities must be found. This motivates a huge effort in research activities,
standardization process, and industrial investments on this field since the last decade.
This survey paper aims at reporting an overview of WSNs technologies, main
applications and standards, features in WSNs design, and evolutions. In particular, some
peculiar applications, such as those based on environmental monitoring, are discussed
and design strategies highlighted; a case study based on a real implementation is also
reported. Trends and possible evolutions are traced. Emphasis is given to the IEEE
802.15.4 technology, which enables many applications of WSNs. Some example of
performance characteristics of 802.15.4-based networks are shown and discussed as a
function of the size of the WSN and the data type to be exchanged among nodes.
A wireless sensor network (WSN) consists of spatially
distributed autonomous sensors to monitor physical or environmental conditions, such
as temperature, sound, pressure, etc. and to cooperatively pass their data through the
network to a main location. The more modern networks are bi-directional, also
enabling control of sensor activity. The development of wireless sensor networks was
motivated by military applications such as battlefield surveillance; today such networks
are used in many industrial and consumer applications, such as industrial process
monitoring and control, machine health monitoring, and so on.
2.1 WSN TECHNOLOGY
The WSN is built of "nodes" – from a few to several hundreds or even thousands, where
each node is connected to one (or sometimes several) sensors. Each such sensor network
node has typically several parts: a radio transceiver with an internal antenna or
connection to an external antenna, a microcontroller, an electronic circuit for interfacing
with the sensors and an energy source, usually a battery or an embedded form of energy
harvesting. A sensor node might vary in size from that of a shoebox down to the size of a
grain of dust, although functioning "motes" of genuine microscopic dimensions have yet
to be created. The cost of sensor nodes is similarly variable, ranging from a few to
hundreds of dollars, depending on the complexity of the individual sensor nodes. Size
and cost constraints on sensor nodes result in corresponding constraints on resources such
as energy, memory, computational speed and communications bandwidth. The topology
of the WSNs can vary from a simple star network to an advanced multi-hop wireless
mesh network. The propagation technique between the hops of the network can be
routing or flooding.
The origins of the research on WSNs can be traced back to the Distributed Sensor
Networks(DSN) program at the Defense Advanced Research Projects Agency (DARPA)
at around 1980. By this time, the ARPANET (Advanced Research Projects Agency
Network) had been operational for a number of years, with about 200 hosts at universities
and research institutes. DSNs were assumed to have many spatially distributed low-cost
sensing nodes that collaborated with each other but operated autonomously, with
information being routed to whichever node was best able to use the information. At that
time, this was actually an ambitious program. There were no personal computers and
workstations; processing was mainly performed on minicomputers and the Ethernet was
just becoming popular. Technology components for a DSN were identified in a
Distributed Sensor Nets workshop in 1978 (Proceedings of the Distributed Sensor Nets
Workshop, 1978). these included sensors (acoustic), communication and processing
modules, and distributed software. Researchers at Carnegie Mellon University (CMU)
even developed a communication-oriented operating system called Accent (Rashid &
Robertson, 1981), which allowed flexible, transparent access to distributed resources
3. required for a fault-tolerant DSN. A demonstrative application of DSN was a helicopter
tracking system (Myers et al., 1984), using
a distributed array of acoustic microphones by means of signal abstractions and matching
techniques, developed at the Massachusetts Institute of Technology (MIT). Even though
early researchers on sensor networks had in mind the vision of a DSN, the technology
was not quite ready. More specifically, the sensors were rather large and This work was
carried out during the tenure of an ERCIM “Alain Bensoussan” Fellowship Program and
is part of the MELODY Project, which is funded by the Research Council of Norway
under the contract number 187857/S10.
In the new wave of sensor network research, networking techniques and networked
information processing suitable for highly dynamic ad hoc environments and resource
constrained sensor nodes have been the focus. Further, the sensor nodes have been much
smaller in size (i.e. pack of cards to dust particle) and much cheaper in price, and thus
many new civilian applications of sensor networks such as environment monitoring,
vehicular sensor network and body sensor network have emerged. Again, DARPA acted
as a pioneer in the new wave of sensor network research by launching an initiative
research program called SensIT. Which provided the present sensor networks with new
capabilities such as ad hoc networking, dynamic querying and tasking, reprogramming
and multitasking. At the same time, the IEEE noticed the low expense and high
capabilities that sensor networks offer. The organization has defined the IEEE 802.15.4
standard (IEEE 802.15 WPAN Task Group 4, n.d.) for low data rate wireless personal
area networks. Based on IEEE 802.15.4, ZigBee Alliance (ZigBee Alliance, n.d.) has
published the ZigBee standard which specifies a suite of high level communication
protocols which can be used by WSNs. Currently, WSN has been viewed as one of the
most important technologies for the 21st century (21 Ideas for the 21st Century,1999).
Countries such as China have involved WSNs in their national strategic research
programmer’s (Ni, 2008). The commercialization’s of WSNs are also being accelerated
by new formed companies like Crossbow Technology (Crossbow Technology, n.d.) and
2.3 WSN ARCHITECTURE
The architecture of wsn consist of sensor microcontroller unit antenna and transmitter &
receiver of the system.sens0r and control units are connected to the battery for required
power supply voltage sensor sense the physical environment and send the input to the
A/D converter to convert it into digital form and then it is send to the control unit of
microcontroller from where the o/p’s are controlled by the mechanism stored in
4. The topology of the WSNs can vary from a simple star network to an advanced multi-hop
wireless mesh network. The propagation technique between the hops of the network
can be routing or flooding.
Typical multi-hop wireless sensor network architecture
Sensors are the very important part of any sensor network it is the primary hub of
wireless sensor networks. All wireless technology is depend upon these sensors in our
general life we use many sensors ,do u know that how much sensors are working in your
system or in your mobile cell. U can not think about a network without sensor
A 'sensor' is a device that measures a physical quantity and converts it into a 'signal'
which can be read by an observer or by an instrument. For example, a mercury
thermometer converts the measured temperature into the expansion and contraction of
a liquid which can be read on a calibrated glass tube.
3.2 TYPE OF SENSOR
There are a lot of different types of sensors. Sensors are used in everyday objects.
A sensor that detects temperature. Thermal sensors are found in many laptops and
computers in order to sound an alarm when a certain temperature has been exceeded.
temperature sensors: thermometers
heat sensors: bolometer, calorimeter
An electronic device used to measure a physical quantity such as pressure or loudness
and convert it into an electronic signal of some kind (e.g. a voltage).
electrical resistance sensors: ohmmeter
6. electrical voltage sensors: voltmeter
electrical power sensors: watt-hour meter
magnetism sensors: magnetic compass
Pressure sensors: barometer
Vibration and shock sensors
A motion sensor detects physical movement in a given area.
radar gun, tachometer
3.3 The trend of sensors
Because of certain disadvantages of physical contact sensors, newer technology non-contact
sensors have become prevalent in industry, performing well in many applications.
The recent style of non-contact sensors shows that “Thin (g) is In”. Market trends show
that form and size are important. Users are looking for smaller and more accurate sensors.
New technologies for the sensing chips are breaking application barriers. For the future,
the trend will be to continue to provide smaller, more affordable sensors that have the
flexibility to fit even more applications in both industrial and commercial environments.
In spite of the diverse applications, sensor networks pose a number of unique technical
features due to the following factors:
4.1. Ad hoc deployment:
Most sensor nodes are deployed in regions which have no infrastructure at all. A typical
way of deployment in a forest would be tossing the sensor nodes from an aeroplane. In
such a situation, it is up to the nodes to identify its connectivity and distribution.
4.2 .Unattended operation:
In most cases, once deployed, sensor networks have no human intervention. Hence the
nodes themselves are responsible for reconfiguration in case of any changes.
The sensor nodes are not connected to any energy source. There is only a finite source Of
energy, which must be optimally used for processing and communication? An interesting
fact is That communication dominates processing in energy consumption. Thus, in order
to make optimal Use of energy, communication should be minimized as much as
4.4 Dynamic changes:
It is required that a sensor network system be adaptable to changing Connectivity (for
e.g., due to addition of more nodes, failure of nodes etc.) as well as changing
Environmental stimuli. Thus, unlike traditional networks, where the focus is on
maximizing channel throughput or minimizing node deployment, the major consideration
in a sensor network is to extend the system lifetime as well as the system robustness.
5. Routing Protocols for WSNs
Flooding is an old routing mechanism that may also be used in sensor networks. In
Flooding, a node sends out the received data or the management packets to its neighbors
by broadcasting, unless a maximum number of hops for that packet are reached or the
destination of the packets is arrived. here are some deficiencies for this routing technique
[ Implosion: is the case where a duplicated data or packets are sent to the same node.
Overlap: if two sensor nodes cover an overlapping measuring region, both of them will
sense/detect the same data. As a result, their neighbor nodes will receive duplicated data
or messages. Resource blindness: A WSN protocol must be energy resource-aware and
adapts its sensing, communication and computation to the state of its energy.
Gossiping protocol is an alternative to flooding mechanism. In Gossiping, nodes can
forward the incoming data/packets to randomly selected neighbor node. Once a gossiping
8. node receives the messages, it can forward the data back to that neighbor or to another
one randomly selected neighbor node. This technique assists in energy conservation by
randomization. Gossiping can solve the implosion problem.
SPIN (Sensor Protocols for Information via Negotiation) is a family of adaptive protocols
for WSNs. Their design goal is to avoid the drawbacks of flooding protocols mentioned
above by utilizing data negotiation and resource-adaptive algorithms.
Directed di_usion is another data dissemination and aggregation protocol. It is a data-centric
and application aware routing protocol for WSNs. It aims at naming all data
generated by sensor nodes by attribute-value pairs.
LEACH (Low Energy Adaptive Clustering Hierarchy) is a self-organizing, adaptive
clustering-based protocol that uses randomized rotation of cluster-heads to evenly
distribute the energy load among the sensor nodes in the network
PEGASIS (Power-E_client GAthering in Sensor Information Systems) is a greedy chain-based
power e_cient algorithm.
The key features of PEGASIS are
The BS is fixed at a far distance from the sensor nodes.
The sensor nodes are homogeneous and energy constrained with uniform energy.
No mobility of sensor nodes.
GEAR (Geographical and Energy Aware Routing) is a recursive data dissemination
protocol WSNs. It uses energy aware and geographically informed neighbor selection
Heuristics to rout a packet to the targeted region
The original motivation behind the research into WSNs was military application.
Examples of military sensor networks include large-scale acoustic ocean surveillance
systems for the detection of submarines, self-organized and randomly deployedWSNs for
battlefield surveillance and attaching microsensors to weapons for stockpile surveillance
(Pister, 2000). As the costs for sensor nodes and communication networks have been
reduced, many other potential applications including those for civilian purposes have
emerged. The following are a few examples.
9. Environmental Monitoring
Environmental monitoring (Steere et al., 2000) can be used for animal tracking, forest
surveillance, flood detection, and weather forecasting. It is a natural candidate for
applying WSNs, because the variables to be monitored, e.g. temperature, are usually
distributed over a large region. One example is that researchers from the University of
Southampton have built a glacial environment monitoring system using WSNs in Norway
(Martinez et al., 2005). They collect data from sensor nodes installed within the ice and
the sub-glacial sediment without the use of wires which could disturb the environment.
10. Health Monitoring
WSNs can be embedded into a hospital building to track and monitor patients and all
medical resources. Special kinds of sensors which can measure blood pressure, body
temperature and electrocardiograph (ECG) can even be knitted into clothes to provide
remote nursing for the elderly. When the sensors are worn or implanted for healthcare
purposes, they form a special kind of sensor network called a body sensor network
(BSN). BSN is a rich interdisciplinary area which revolutionizes the healthcare system by
allowing inexpensive, continuous
and ambulatory health monitoring with real-time updates of medical records via the
Sensor networks have been used for vehicle traffic monitoring and control for some time.
At many crossroads, there are either overhead or buried sensors to detect vehicles and to
control the traffic lights. Furthermore, video cameras are also frequently used to monitor
road segments with heavy traffic. However, the traditional communication networks used
to connect these sensors are costly, and thus traffic monitoring is usually only available at
a few critical points in a city (Chong & Kumar, 2003). WSNs will completely change the
landscape of traffic monitoring and control by installing cheap sensor nodes in the car, at
11. the parking lots, along the roadside, etc. Street line, Inc. (Street line, Inc., n.d.) is a
company which uses sensor network technology to help drivers find unoccupied parking
places and avoid traffic jams. The solutions provided by Street line can significantly
improve the city traffic management and reduce the emission of carbon dioxide.
The New York Times Building - a Smart Building
The headquarters of the New York Times is an example of how different smart building
technologies can be combined to reduce energy consumption and to increase user
comfort. Overall, the building consumes 30% less energy than traditional office
skyscrapers. Opened in November 2007 and designed by Renzo Piano, the building has a
curtain wall which serves as a sunscreen and changes color during the day. This wall
consists of ceramic rods, “a supporting structure for the screen and an insulated window
unit” (Hart, 2008).
The building is further equipped with lighting and shading control systems based on ICT
technologies. The lighting system ensures that electrical light is only used when required.
Further day lighting measures include a garden in the centre of the ground floor which is
open to the sky as well as a large area skylight. The electrical ballasts in the lighting
system are equipped with chips that allow each ballast to be controlled separately. The
shading system tracks the position of the sun and relies on a sensor network to
automatically actuate the raising and lowering of the shades. The high-tech HVAC
system is equipped with sensors that measure the temperature. It is further able to rely on
free air cooling, i.e. fresh air on cool mornings is brought into the HVAC system. An
automated building system monitors in parallel “the air conditioning, water cooling,
heating, fire alarm, and generation systems” (Siemens, 2008). The system relies on a
large-scale sensor network composed of different kinds of sensors which deliver real-time
information. Consequently, energy can be saved as only as few systems are turned
on as needed.
While the future of WSNs is very prospective, WSNs will not be successfully deployed if
security, dependability and privacy issues are not addressed adequately. These issues
become more important because WSNs are usually used for very critical applications.
Furthermore, WSNs are very vulnerable and thus attractive to attacks because of their
limited prices andhuman-unattended deployment .IT provide kee management,
authentication, intrusion detection, privacy protection which makes WSN secure.
Air pollution Monitoring:
Wireless sensor networks have been deployed in several cities (Stockholm, London or
Brisbane) to monitor the concentration of dangerous gases for citizens. These can take
advantage of the ad-hoc wireless links rather than wired installations, which also make
them more mobile for testing readings in different areas. There are various architectures
that can be used for such applications as well as different kinds of data analysis and data
mining that can be conducted.
Forest fire Detection:
A network of Sensor Nodes can be installed in a forest to detect when a fire has started.
The nodes can be equipped with sensors to measure temperature, humidity and gases
which are produced by fire in the trees or vegetation. The early detection is crucial for a
successful action of the firefighters; thanks to Wireless Sensor Networks, the fire brigade
will be able to know when a fire is started and how it is spreading.
A landslide detection system, makes use of a wireless sensor network to detect the slight
movements of soil and changes in various parameters that may occur before or during a
landslide. Through the data gathered it may be possible to know the occurrence of
landslides long before it actually happens.
Water Quality Monitoring:
Water quality monitoring involves analyzing water properties in dams, rivers, lakes &
oceans, as well as underground water reserves. The use of many wireless distributed
sensors enables the creation of a more accurate map of the water status, and allows the
permanent deployment of monitoring stations in locations of difficult access, without the
need of manual data retrieval.
7. Operating systems used in WSN
A WSN typically consists of hundreds or thousands of sensor nodes. These nodes have
the capability to communicate with each other using multi-hop communication. Typical
applications of these WSN include but not limited to monitoring, tracking, and
The basic functionality of an operating system is to hide the low-level details of the
sensor node by providing a clear interface to the external world. Processor management,
13. memory management, device management, scheduling policies, multi-threading, and
multitasking are some of the Low
Level services to be provided by an operating system.
In addition to the services mentioned above, the operating system should also provide
services like support for dynamic loading and unloading of modules, providing proper
concurrency mechanisms, Application Programming Interface (API) to access underlying
hardware, and enforce proper power management policies.
TinyOS is an open source, flexible, component based, and application-specific operating
system designed for sensor networks. TinyOS can support concurrent programs with very
low memory requirements. The OS has a footprint that fits in 400 bytes. The TinyOS
component library includes network protocols, distributed services, sensor drivers, and
data acquisition tools.
Contiki is a lightweight open source OS written in C for WSN sensor nodes. Contiki is a
highly portable OS and it is build around an event-driven kernel. Contiki provides
preemptive multitasking that can be used at the individual process level. A typical
Contiki configuration consumes 2 kilobytes of RAM and 40 kilobytes of ROM. A full
Contiki installation includes features like: multitasking kernel, preemptive
multithreading, proto-threads, TCP/IP networking, IPv6, a Graphical User Interface, a
web browser, a personal web server, a simple telnet client, a screensaver, and virtual
The MultimodAl system for NeTworks of In-situ wireless Sensors (MANTIS) provides a
new multithreaded operating system for WSNs. MANTIS is a lightweight and energy
efficient operating system. It has a footprint of 500 bytes, which includes kernel,
scheduler, and network stack. The MANTIS Operating System (MOS) key feature is that
it is portable across multiple platforms, i.e., we can test MOS applications on a PDA or a
PC. Afterwards, the application can be ported to the sensor node. MOS also supports
remote management of sensor nodes through dynamic programming. MOS is written in C
and it supports application development in C.
Nano-RK is a fixed, preemptive multitasking real-time OS for WSNs. The design goals
for Nano-RK are multitasking, support for multi-hop networking, support for priority-based
scheduling, timeliness and schedulability, extended WSN lifetime, application
resource usage limits, and small footprint. Nano-RK uses 2 Kb of RAM and 18 Kb of
ROM. Nano-RK provides support for CPU, sensors, and network bandwidth reservations.
Nano-RK supports hard and soft real-time applications by the means of different real-time
scheduling algorithms, e.g., rate monotonic scheduling and rate harmonized
scheduling. Nano-RK provides networking support through socket-like abstraction.
Nano-RK supports FireFly and MicaZ sensing platforms.
14. Lite OS:
LiteOS is a Unix-like operating system designed for WSNs at the University of Illinois at
Urbana-Champaign. The motivations behind the design of a new OS for WSN are to
provide a Unix-like OS for WSN, provide system programmers with a familiar
programming paradigm (thread-based programming mode, although it provides support
to register event handlers using callbacks), a hierarchical file system, support for object-oriented
programming in the form of LiteC++, and a Unix-like shell. The footprint of
LiteOS is small enough to run on MicaZ nodes having an 8 MHz CPU, 128 bytes of
program flash, and 4 Kbytes of RAM. LiteOS is primarily composed of three
components: LiteShell, LiteFS, and the Kernel.
EPOS (Embedded Parallel Operating System) is a component-based framework for the
generation of dedicated runtime support environments. The EPOS system framework
allows programmers to develop platform-independent applications and analysis tools
allow components to be automatically adapted to fulfill the requirements of these
particular applications. By definition, one instance of the system aggregates all the
necessary support for its dedicated application and nothing else.
Table 1. Operating Systems Summary
TinyOS Monolithic Primarily
Contiki Modular Protothrea
Layered Threads Five
but use is
g Layer is
n is free to
Monolithic Threads Rate
c and rate
LiteOS Modular Threads
ent and it
8. WSN Architecture:
This layer is specifically needed when a system is organized to access other networks.
Providing a reliable hop by hop is more energy efficient than end to end. Other protocol
used in this layer is STCP (Sensor Transmission Control Protocol) PORT (Price-Oriented
Reliable Transport Protocol) PSFQ (pump slow fetch quick).
The major function of this layer is routing. This layer has a lot of challenges
depending on the application but apparently, the major challenges are in the power
saving, limited memory and buffers, sensor does not have a global ID and have to be self
Data link layer:
Responsible for multiplexing data streams, data frame detection, MAC, and error control,
ensures reliability of point–point or point– multipoint. Errors or unreliability comes from:
Co- channel interference at the MAC layer and this problem is solved by MAC protocols.
Multipath fading and shadowing at the physical layer and this problem is solved by
forward error correction (FEC) and automatic repeat request (ARQ).
Can provide an interface to transmit a stream of bits over physical medium. Responsible
for frequency selection, carrier frequency generation, signal detection, Modulation and
Responsible for traffic management and provide software for different applications that
translate the data in an understandable form or send queries to obtain certain information.
17. Sensor networks deployed in various applications in different fields, for example;
military, medical, environment, agriculture fields.
Responsible for Channel access policies, scheduling, buffer management and error
control. In WSN we need a MAC protocol to consider energy efficiency, reliability, low
access delay and high throughput as major priorities.
In the area of WSNs, several standards are currently either ratified or under development.
The major standardization bodies are the Institute of Electrical and Electronics Engineers
(IEEE), the Internet Engineering Task Force (IETF), the International Society for
Automation (ISA) and the HART Communication Foundation, etc. These standardization
bodies have different focuses and they provide global, open standards for interoperable,
low-power wireless sensor devices. Table 1 provides the comparisons of different
standards currently available for the communication protocols of WSNs.
IEEE 802.15.1 standard, popularly known as Bluetooth, offers moderate data rates at
lower energy levels. Due to this, it is ideally suited for high end WSN applications that
require higher data rates with harder real time constraints. Bluetooth is used in star
topology because of its basic characteristics. Bluetooth devices communicate with each
other using set of standard Bluetooth profiles defined by standard body.
IEEE 802.15.4 standard, popularly known as ZigBee, offers low data rates at very low
energy levels. Due to this, it is ideally suited for applications requiring infrequent smaller
data transfers where battery life is an important issue. However, location estimation based
on narrow band DSSS can achieve accuracy only in the order of several meters.
ZigBee coordinator is responsible for managing the network and supervising network
formation; ZigBee routers have routing capabilities and they are responsible for linking
group of end devices or routers; and ZigBee end devices are simple network end points
capable of communicating with other devices in the network.
Ultra wide band is a technology for transmitting information spread over a large
bandwidth (>500 MHz) and it is ideally suited for short distance, high speed
communications with very low power budget. As it is based on wide band technology, it
can achieve very high geo-location accuracy to the sub-meter levels. UWB provides one
of the best options for WSN networking only limited by its shorter range.
Wi-Fi represents group of WLAN technologies defined under IEEE 802.11 standard
body. In addition to transmission standards like 802.11a/b/g/n, it also includes 802.11s
standard for mesh networking. Wi-Fi technologies are capable of providing very high
throughput (>100 Mbps) at longer range but required very high power budget. Also, Wi-
18. Fi can locate end point location to the accuracy of several meters only. Because of this
limitation, use of Wi-Fi is mostly restricted to devices with fixed power supply.
10. Challenges of WSN:
1. Lower speed compared to wired network.
2. Less secure because hacker's laptop can act as access point.
3. More complex to configure than wired network.
4. Can be affected by surrounding's. For example, walls(blocking),
Microwave oven (interference), far distance.
5. Sensor node has low battery power, so as battery goes down, node
Goes down and so does the whole network.
6. Like any other wireless technology, it is easy for hackers to hack WSNs. For most of the
applications security & integrity of data is most important hence we have to select
networking technology as well as security algorithms accordingly.
7. Due to limited resources and dynamic topology, it is very difficult to design a reliable
routing scheme for WSNs.
8. Quite a few applications like solar energy monitoring, irrigation and air quality
monitoring are associated with harsh environments. Independent of enclosure design,
sensors will be exposed to the outdoor conditions and it is extremely crucial to take
environmental conditions into consideration while designing WSN system.
9. Dynamic topologies and integration with internet affect factors like Quality-of-service
requirements, security, packet errors and variable- link capacity.
10. Energy conservation is a very critical part of WSN because of small battery size.
We need to look at traffic scheduling as well as remote wake-up features to optimize
19. 11. Conclusion
WSNs have been identified as one of the most prospective technologies in this century.
This chapter provides information concerning both its history and current state of the art.
In concrete terms, the authors provide an overview about the hardware, software and
networking protocol design of this important technology. The authors also discuss the
security and ongoing standardization of this technology. Depending on applications,
many other techniques such as localization, synchronization and in-network processing
can be important, which are not discussed in this chapter.