الخميس، 29 أكتوبر 2009

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السبت، 24 أكتوبر 2009

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الخميس، 22 أكتوبر 2009

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الجمعة، 16 أكتوبر 2009

A Taxonomy of Routing Protocols in Sensor Networks

Sensors, in the sense of devices that perform the measurement of certain quantities
andtransformthemintoacomputerreadabledigitalformat,havebeenaroundforat
least a few decades. These sensors were either connected to a data collection device
or connected directly to computers, using traditional wired communication such as
a serial interface. The development of highly integrated computer devices, wireless
radios,andminiaturizationallowedthedevelopmentofwirelesssensornodes,which
are miniature computers with integrated sensing and wireless communication capa-
bilities[1,2].Thecontinuingminiaturizationeffortallowedthedevelopmentofnodes
with a physical size of several millimeters. Although many of these devices can act
as a general-purpose computer, with the ability to perform computation as well as
sensing, there are obvious limitations. First, the limited memory and computational
powerdoesnotallowustorunafull-featuredoperatingsystem.Mostofthetime,the
networking stack needs to be simplified as well. The wireless transmission range is
limited by the small size of the antennas. The power resources of the sensor nodes
are limited by the physical size of the batteries; moreover, some of the proposed
deploymentmodels donotallowthesensornodestorecharge theirbatteries.
AlgorithmsandProtocolsforWireless SensorNetworks, EditedbyAzzedineBoukerche
Copyright©2009byJohnWiley&SonsInc.
129

130 A TAXONOMY OF ROUTING PROTOCOLS IN SENSOR NETWORKS
Letusnowconsiderthedeploymentmodelsofthewirelesssensornodes.Classical
sensors are typically used in pre-engineered deployment, being placed at a carefully
chosenlocations.Forinstance,thespaceshuttleusesseveraldozentemperaturesen-
sors, carefully positioned in the structure of the spacecraft and reporting through
a wired connection to a central computer. Naturally, wireless sensor nodes can be
deployed similarly, simply by replacing the wired connection with a wireless one.
However, we can also choose a radically different deployment method: Blanket the
desired area with a large number of nodes. Instead of careful positioning, we need
to worry only about making sure that every area of interest is covered by one node
(or preferably, several). The deployed nodes might not have the transmission range
to reach the central computer, but they can transmit the collected information on a
hop-by-hop basis to collection points called sinks. We call the resulting structure a
“wireless sensor network (WSN).” In our example application, a WSN can provide
severaladvantages:Duetothelargenumberofsensors,itcancollectmoredatathan
sensorswithpre-engineereddeployment.Asseveralsensorscoverthesamearea,they
providefaulttolerance.Inaddition,havingcomputationalaswellassensingcapabil-
ities,thewirelesssensornetworkcanprovidepreliminaryprocessingofthecollected
data concomitantly with the sensingand forwarding.
Letusnowinvestigatethepropertiesofawirelesssensornetworkfromthenetwork-
ingpointofview.Firstofall,thereisnoinfrastructureavailable.Becausethesinksare
accessibleonlytoalimitedsubsetofnodes,thesensornodesneedtoparticipateinthe
forwarding of the packets. Due to the random deployment, the routing architecture
cannotbepre-established;thenetworkneedstobesetupthroughself-configuration.
Intheserespects,wirelesssensor networks aresimilar to adhocwirelessnetworks.
There are, however, several important differences. The sensor nodes have signif-
icantly lower communication and computation capabilities than do the full-featured
computersparticipatinginadhocnetworks.Theproblemofenergyresourcesisespe-
ciallydifficult.Duetotheirdeploymentmodel,theenergysourceofthesensornode
is considered nonrenewable (although some sensor nodes might be able to scavenge
resources from their environment). Routing protocols deployed in sensor networks
needtoconsider theproblemof efficient useof power resources.
Anadditionaldifferencebetweenadhocandsensornetworksreferstotheunique-
ness of the nodes. Ad hoc nodes have a hard-wired unique MAC address, which
forms the basis of node identification on the higher levels of the networking stack.
The cheap, disposable sensor nodes usually come without any pre-wired identifiers;
theyacquireauniqueidentityonlyafterdeployment,byvirtueoftheirpositioninthe
environment.
In addition to these, several other factors such as the large number of nodes in
sensor networks, the high failure rates of the sensor nodes, and the frequent use of
broadcasting in sensor networks as opposed to the typically unicast communication
in ad hoc networks [3] require new types of MAC [4, 5] and routing protocols,
specifically targetedtowardtherequirements ofWSNs.
Inthischapter,wesuccintlypresentthemajorapplicationsoftheWSNs,describe
some of the design issues associated with routing algorithms for WSN, and finally
presentasurveyofthe stateof the art inWSN routing protocols.
6.2 APPLICATIONS
Sensor networks can be deployed in a wide variety of applications. One of the main
classification criteria is whether the sensor nodes are mobile or immobile. The data
collection might be either continuous or periodic; the latter can lead to bursty traffic
patterns.Naturally,everyapplicationrequiresaspecificsetofsensortypes.Someof
themostpopularsensortypesare:light,sound,magneticfield,accelerator,tempera-
ture, humidity, chemical composition such as soil makeup, mechanical stress levels
onanobject,andmanyothers[6].Someoftheprimaryapplicationdomainsforsensor
networksare the following:
Environmental. Environmentalsensorscanbeusedtodetectandtracknaturaldis-
asterssuchasforestfiresorfloods.Theycanalsobeusedtotrackthemovement
ofbirdsandotheranimals.
Military. The sensor networks will be an integral part of the future C4ISRT sys-
tems (command, control, communications, computing, intelligence, surveil-
lance, reconnaissance, and targeting.) They can, for instance, be used to track
themovementoftheenemyinthebattlefield.ThemainadvantageofWSNsis
that they can be deployed and operated remotely, without putting human lives
at risk. Naturally, military deployments bring their own challenges of security
andconfidentiality.
Health. Sensornetworkscanbeusedinhospitalsandclinicsforpatientmonitoring
andtrackingofvarioussystemsandhumans.Sensorscanbealsousedtotrack
andmonitorthedrugdosesprescribedtopatientsandpreventsituationswhere
the drugs are administered to the wrong patient. Sensors can be deployed for
telemonitoring of patients, a promising new direction for at-home monitoring
andcarefortheelderly.
Home. The various home appliances can be sensor enabled and interconnected
witheachotherandacentralcontrolsystemofthehome.Thesesensor-enabled
sensor homes might not only offer additional conveniences, but will also be
safer andmore energy-efficient.
DESIGN ISSUES
The challenges posed by the deployment of sensor networks is a superset of those
found in wireless ad hoc networks. Sensor nodes communicate over wireless, lossy
lineswithnoinfrastructure.Anadditionalchallengeisrelatedtothelimited,usually
nonrenewableenergysupplyofthesensornodes.Inordertomaximizethelifetimeof
thenetwork,theprotocolsneedtobedesignedfromthebeginningwiththeobjective
ofefficientmanagementoftheenergyresources[3].Letusnowdiscusstheindividual
design issuesingreater detail.
Fault Tolerance. Sensor nodes are vulnerable and frequently deployed in danger-
ousenvironment.Nodescanfailduetohardwareproblemsorphysicaldamage
or by exhausting their energy supply. We expect the node failures to be much
higherthantheonenormallyconsideredinwiredorinfrastructure-basedwire-
less networks. The protocols deployed in a sensor network should be able to
detectthesefailuresassoonaspossibleandberobustenoughtohandlearela-
tivelylargenumberoffailureswhilemaintainingtheoverallfunctionalityofthe
network.Thisisespeciallyrelevanttotheroutingprotocoldesign,whichhasto
ensure that alternate paths are available for rerouting of the packets. Different
deploymentenvironments posedifferent fault tolerancerequirements.
Scalability. Sensor networks vary in scale from several nodes to potentially
severalhundredthousand.Inaddition,thedeploymentdensityisalsovariable.
For collecting high-resolution data, the node density might reach the level
where a node has several thousand neighbors in their transmission range. The
protocols deployed in sensor networks need to be scalable to these levels and
beableto maintainadequate performance.
Production Costs. Because many deployment models consider the sensor nodes
to be disposable devices, sensor networks can compete with traditional
information gathering approaches only if the individual sensor nodes can be
produced very cheaply. The target price envisioned for a sensor node should
ideallybe less than$1.
Hardware Constraints. At minimum, every sensor node needs to have a sensing
unit, a processing unit, a transmission unit, and a power supply. Optionally,
the nodes may have several built-in sensors or additional devices such as
a localization system to enable location-aware routing. However, every
additional functionality comes with additional cost and increases the power
consumption and physical size of the node. Thus, additional functionality
needstobe always balancedagainstcostandlow-powerrequirements.
Transmission Media. The communication between the nodes is normally imple-
mented using radio communication over the popular ISM bands. However,
some sensor networks use optical or infrared communication, with the latter
havingtheadvantageof being robustandvirtually interferencefree.
Power Consumption. As we have already seen, many of the challenges of
sensor networks revolve around the limited power resources. The size of the
nodes limits the size of the battery. The software and hardware design needs
to carefully consider the issues of efficient energy use. For instance, data
compression might reduce the amount of energy used for radio transmission,
but uses additional energy for computation and/or filtering. The energy policy
alsodependsontheapplication;insomeapplications,itmightbeacceptableto
turn off a subset of nodes in order to conserve energy while other applications
requireall nodes operatingsimultaneously.

الخميس، 15 أكتوبر 2009

PID Control in NCS

This section briefly discusses the properties of the PID algorithm in control of NCS and points out
some modifications that have been proposed to better tackle the network-induced delays and
packet losses. When applying the ideal PID algorithm (28) in a control loop, where varying time-
delays are present, one can show by using the concept of jitter margin (26) that with a FOTD
process the stability of the system cannot be guaranteed, at least in the sense of the jitter margin
condition, for any additional delays. In such case the jitter margin is zero. It should be noted,
though, that the jitter margin is a conservative criterion and only sufficient, so the zero jitter
margin does not necessarily imply instability for additional delays. The reason for having zero jitter
margin is that the complementary sensitivity function of the system does not have roll-off at high
frequencies and in the frequency response plot, the closed-loop magnitude crosses the bounding
curve that the jitter margin defines, see Figure 13. This can also be shown analytically, see [P7].
The problem can be easily solved by using a measurement filter, such as (34) or (59), which will
saturate the gain of the controller derivative term at high frequencies.
Figure 13 shows the advantage of using measurement filters in the PID controller from the jitter
margin point of view. It is seen in the figure that without the filter the complementary sensitivity
function of a PID controlled FOTD process hits the jitter margin bound and obviously the jitter
margin is zero. When filtering is applied the jitter margin is positive, because the complementary
sensitivity function remains below the jitter margin bound for all frequencies. Nevertheless, if the
controller is not well tuned, the lines may intersect and the required jitter margin is not achieved.

Frequency rad/s
Complementary sensitivity functions of PID controlled FOTD process,
with and without the measurement filter, and the jitter margin bound for ?max = 0.5 [P7].
Packet losses cause undesired behavior in the signals of a PID-controlled system, if the losses are
not considered in the controller design procedure. Under packet losses in communications, the D-
term of the PID controller is likely to cause spikes in the control signal [76]. This would happen
when the communications are reestablished after a period of disconnectivity. The amplitude of the
spike is determined based on how much the measured value of the process variable has evolved
between the times of successful reception of packets (before and after the packet losses). It is
pointed out in [76] that during the time of packet losses the control signal is a linearly changing
function of error, since there is no new information available of the process variable. Thus the
error is constant and the I-term integrates the error over time. The set-point and measurement
signals are static functions of error, and as a result, a linearly increasing or decreasing control signal
is obtained. This can endanger the stability of the process or cause undesired actions. It should also
be noted that during packet losses there will be no information regarding actuator saturation and
the possible anti-windup functions may fail. In addition, on controller output losses the actuator
may experience bumps in the control signal.
In [76], an enhanced PID algorithm for wireless control systems is proposed to overcome the
problems of discontinuous communications. The integral and derivative parts of the controller are
remodeled such that they are updated only on the arrival of new packets. The integrator is replaced
by a filter that receives information about the actuator position at the same time as new
measurement packets arrive. The filter output is calculated

where O(k) is the new filter output, Ok(1)- its previous value, uk(1)- the controller output for
the last execution (based on the actuator position feedback), ?T the elapsed time since a new value
was communicated, and TReset a tuning parameter (integrator resetting time). The last
communicated actuator position is used in the filter output calculation and hence this structure
should be able to automatically compensate for any loss in the output of the controller.
The derivative part of the proposed algorithm is basically an event-based version of the regular
where d(k) is the controller derivative term, kd the gain, and e(k) – ek(1)- the difference of current
and previous error. The difference with respect to the normal PID algorithm is that the sample
time is updated based on reception of measurement packets and thus the derivative action is
smaller the longer the time interval between the two last packets. Note that the control law is
updated only upon receiving new information.
Event-based PID control has also been considered in [101], but from a different perspective. The
basic idea of event-based control is to transmit sensor and command data only when needed.
Especially in bandwidth limited systems, such as networked control systems, event-based control is
desirable, because the communication medium is only used if something has happened since the
last event [26]. Although the main motivation for developing the event-based PID controller in
[101] arises from the field of embedded control systems and the problems faced with scheduling
and control co-design, a similar controller structure is very usable in networked control systems (or
even networked embedded control systems). In event-based control the execution of the control
algorithm is not time-based, but the control law is updated upon certain variables exceeding
predefined thresholds, that is, when events occur. Different criteria for event detection may be
defined based on the process and the scenario. An event could occur, for example, if a measured
variable would exceed a certain value, or relative changes of variables could be tracked. Sampling
should also be performed at reference changes. The tracking of error signal rather than the
measurement signal might be more effective, because on the sudden change of the reference
signal the error changes immediately, whereas the measured variable reacts much slower. An event
could be triggered if the absolute value of current error would deviate from the error at the
previous sample more than a predefined threshold value. It might also be useful to set a maximum
sample time after which sampling would occur even though none of the triggering events were
activated. Event-based control may be very efficient from the CPU utilization and networking
points of view, since presumably the control law is updated more rarely than in the case of time-
based control. The drawback of event-based control, however, is that the control system becomes
more difficult to analyze [101].
In the literature, the structure of the PID controller is sometimes discussed in the light of NCS and
modifications such as presented above have been proposed to the algorithm. The tuning of the
PID controller for NCS has not been comprehensively dealt with, even though it has a significant
impact on robustness properties especially with respect to varying time-delays and packet loss. The
discrete-time PID controller tuning problem is discussed in [70] in systems with random delays.
The NCS is assumed to be fully-distributed and the tuning is based on simulation based
optimization of the controller parameters for a specific process model. In [41], a dual PID
structure is proposed to overcome the effects of load disturbances and to improve the dynamic
performance and disturbance rejection in NCS. In addition, a relay auto-tuning method is applied
for obtaining the process parameters, which are then used for determining the controller gains.
The framework is based on certain simplifications of the NCS model, for example, the network
sc ca
delays (?k and ?k ) are lumped together as a single delay.
In [17], fuzzy logic is used to set the integral and derivative terms of the PID controller. For the
proportional term, the paper suggests using a fuzzy immune algorithm that is based on the ideas of
the biological immune system. The proportional term is modified so that the adaptive gain kp1 is
calculated by
kKp1 =-??1(),()?fukuk()? ??, (80)
where K is gain, ? is an adaption speed parameter, f is a selected nonlinear function, which is
approximated by a simple fuzzy logic scheme, and ?u(k) is the change of control signal u at k. The
PID controller algorithm is implemented in the incremental form.
Genetic algorithms (GA, see e.g. [12]) have been proposed to tune the PID controller, also in the
NCS setup. The GA in [40] updates the given initial PID gains based on evaluating a fitness
function that is a weighted sum of several performance criteria, including settling time, overshoot
and normalized integral of square error (ISE) cost criterion (see Section 3.5.1). The experiments
reported show that the delay in the Profibus-DP fieldbus setup that is used in the study varies
between one and four sample times. The performance of a modified Ziegler-Nichols tuning and
the proposed GA based PID tuning method is compared in the Profibus-DP fieldbus testbed. This
GA based tuning procedure resembles those proposed in [P2] and [P4], but the optimization
method is different. The idea is the same in the sense that the performance of the control system is
evaluated in the presence of varying time-delays and the performance is optimized. If the
optimization criteria are carefully chosen, these methods provide good performance and
robustness against delays.

Networked Control Systems

NCS are distributed real-time control systems consisting of the plant, sensors, controllers, actuators
and a shared data network that is used for communication between the components of the system
[60]. A general NCS layout is depicted in Figure 1. The use of data networks in closed-loop
control systems is mainly motivated by reduced system wiring, price, flexibility and modularity o
implementation, and the possibility of having plug and play devices [6], [60]. For example, in th
car industry, the CAN bus (Controller Area Network) has become very popular, because th
measurements from numerous sensors can be transmitted over a single bus instead of wiring each
sensor with its own cable. Nowadays, a car can have three CAN buses with different speeds; one fo
electronic stability control and the anti-lock braking system (high-speed), another for the driver’
information management (navigation system, radio; medium speed) and a third one for the centra
locking system and other low-speed functions. Whereas cars are small in size, industrial processe
are large and the savings in cabling can be dramatic when using a fieldbus instead of traditiona
cables and wires. Besides fieldbuses, Ethernet is being used in industrial environments fo
automation and production machine control. Ethernet provides a high-speed network and it can
be implemented with standard devices such as access points, routers and hubs that are commercia
off-the-shelf (COTS) equipment and thus inexpensive.

PID Control

The PID controller is the most common controller in control systems. For example, in the mid
1990’s the PID controller was used in over 95 % of the control loops in process control [102]. The
best features of the controller can only be achieved if the controller is well tuned. The tuning of
PID controllers has been discussed in numerous papers and books, but seldom for systems with
varying time-delays or for NCS. Some results exist, though, and [37] discusses the tuning of a
continuous-time PID controller in state-dependent delay systems, whereas the discrete-time PID
controller tuning is addressed in [70]. In [P2] and [P4], discrete-time PID controller tuning
methods are presented that optimize the closed-loop performance and improve robustness in
varying time-delay systems. [P6] and [P7] discuss the tuning of the PID controller for stable systems
with varying time-delays and novel tuning rules are proposed. [P5] considers the tuning of a
continuous-time PID controller in integrating processes with varying time-delays, whereas among
other things, [P8] discusses the performance of time and event-driven PID controllers in the NCS
framework. In addition, a PID controller tuning tool for NCS with varying time-delays is presented
in [P1]. The literature on PID tuning for NCS

Home Automation over wireless sensor networks

Introduction

Home automation deals with the specific automation requirements of homes and in the application of automation techniques for the comfort and security of its residents. This can include controlling the lights, climate control, control of doors and windows, security and surveillance systems. There are currently several products on the market that allow home owners to control these devices. This is normally controlled by a handheld remote that communicates with the devices using a mesh wireless network or a wired network. These types of devices require a unique and dedicated device to communicate with the automated products.

One of the basic systems on the market is made by iControl [9] and is easy to install and expandable. The system uses the 802.11 wireless protocol to transmit signals from the various devices to a control box which is connected to the internet. Some of the devices the company offers specifically for elderly care include: cameras, window/door sensors, motion sensors, water sensors, freeze sensors, panic pendants/wristwatches, smoke detectors, carbon monoxide detectors, lamp modules, and thermostats. All of the devices are connected wirelessly to the control box which then allows the devices to be monitored and controlled using the companies website. This system is perfect for the elderly because it is easy to use and it allows family members to monitor the house to ensure that their relative is safe, it is portable and can easily be installed in an existing home. The major drawbacks of this system are that it requires the use of several costly technologies to properly operate. The user must have internet access available as well as a router to install the control box. The user must also have a mobile device which has web access to check the status of their home. This can become expensive with the data plans mobile carriers offer today.

Another key project is ongoing at The University of Florida [10]. They have built a 500 square foot smart house that is designed assist and to provide medical care to user. The house implements devices including a microwave that recognizes entrees and automatically determines how long to cook them and devices to track the individuals location within the home. The house also uses devices to detect water on the floor and a camera that allows the person to view who is at the door and let them in using a cell phone. The smart house at the University of Florida relies on a centralized computer network to deliver electronically coordinated assistance.

This research shows the importance of implementing home automation for the elderly or disabled. The smart homes allow them to stay in their homes where they feel more comfortable and can prolong the time before having to move into costly health care facilities. Smart homes will give the disabled an opportunity for independence that they may not have had before. The goal of this project is to design a system that communicates with a mobile device such as a cell phone or PDA via Bluetooth. The application relies on the use of cell phones and inexpensive sensors and is best suited for the elderly and home-bound people. The main functions of the project are to collect signals through a wireless sensor network using the protocol Bluetooth and the analysis for data through an adaptive architecture

can we control a motor over zigbee?

A communication network for use in feed back control must satisfy requirements on the reliable and deterministic behavior.However,wireless sensor networks such as zigbee may introduce randomly varying delays into the system, resulting in the performance degradation or even the instability.

Industrial Automation

Industrial automation applications provide control, conservation, efficiency, and
safety, as follows:
Sensing applications extend existing manufacturing and process control
systems reliably.
Sensing applications improve asset management by continuous monitoring of
critical equipment.
Sensing applications reduce energy costs through optimized manufacturing
processes.
Sensing applications help identify inefficient operation or poorly performing
equipment.
Sensing applications help automate data acquisition from remote sensors to
reduce user intervention.
Sensing applications provide detailed data to improvepreventivemaintenance
programs.
Sensing applications help deploy monitoring networks to enhance employee
and public safety.
Sensing applications help streamlining data collection for improved compli-
ance reporting.
Specific applications for industrial and commercial spaces include [2.32]:
Warehouses, fleet management, factories, supermarkets, office complexes
Gas, water, and electric meters
Smoke, CO, and H2O detectors
Refrigeration cage or appliance
Equipment management services and preventive maintenance
Security services (including peel-n’-stick security sensors)
Lighting control
Assembly line and workflow and inventory
Materials processing systems (heat, gas flow, cooling, chemical)
Gateway orfieldservice links to sensors and equipment (monitored tosupport
preventive maintenance, status changes, diagnostics, energy use, etc.)
Remote monitoring from corporate headquarters of assets, billing, and energy
management
According to some observers, RFID tags are poised to become the most far-
reaching wireless technology since the cell phone [2.33]. Worldwide revenues
from RFID tags was expected to jump to $2.8 billion in 2009. During this period,
the technology will appear in many industries, with a significant impact on the effi-
ciency of business processes. In the near term, the largest RFID segment is cartons
and supply chains; the second-largest market for RFIDs is consumer products,
although this market is sensitive to privacy concerns. Some C2WSNs (e.g., sup-
ported with RFID technology) has applications for livestock and domestic pets;
humans; carton and supply chain uses; pharmaceuticals; large freight containers;
package tracking; consumer products; security, banking, purchasing and access
control; and others [2.34]. For example, Airbus’s A380 airplane is equipped with
about 10,000 RFID chips; the plane has passive RFID chips on removable parts
such as passenger seats and plane components. The benefits of RFID tagging of air-
plane parts include reducing the time it takes to generate aircraft-inspection reports
and optimizing maintenance operations.

IEEE 802.11 MAC

The IEEE 802.11 protocol (O’Hara and Petrick 1999) is, strictly speaking, intended for
wireless local area networks (LANs), rather than wireless ad hoc networks. However, it
is interesting to examine it in some detail, mainly on account of its ubiquity, and because
it uses most of the main concepts which are reused in many MAC protocols for ad hoc
networks. The protocol covers the functional areas of access control, reliable data delivery,
and security; in the following we will focus on the first two areas, as the last one (security)
is beyond the scope of this chapter.
Reliable transfer is achieved through the use of special acknowledgment (ACK) pack-
ets or frames, sent by the destination node upon successfully receiving a data packet.
Medium access is regulated in two ways, the first of which is a distributed contention-
based mechanism known as Distributed Coordination Function (DCF), which does not
require a centralized controller. The DCF, based on the CSMA protocol described above,
operates as follows. The node that wants to transmit a packet first performs the clear chan-
nel assessment procedure, i.e., it listens to the medium, for a time equal to Interframe
Space (IFS). If the medium is found to be clear (or idle) during that time, the node can
transmit its packet immediately; otherwise, i.e., if another transmission is in progress, the
node waits for another IFS period. If the medium remain idle during that period, the node
backs off for a random interval and again senses the medium. During that time (referred
to as the backoff window or contention window), if the medium becomes busy, the back-
off counter is halted; it resumes when the medium becomes idle again. When the backoff
counter expires and the medium is found to be idle, the node can transmit the packet.
A possible scenario in which this procedure is applied is shown in Figure 1.1. There are
several points worth mentioning. First, the backoff interval is chosen as a random number
from a predefined range. After each collision, the range is doubled in order to reduce the
likelihood of a repeated collision. After each successful transmission, the range is reset to
its initial value, which is typically small. This approach is known as binary exponential
backoff, or BEB (Stallings 2002). In this manner, the protocol ensures a certain level of
load smoothing in case of frequent collisions caused by heavy traffic.
Second, in order to enhance reliability and avoid the hidden/exposed terminal prob-
lems to a certain extent, the RTS/CTS handshake – well known from wired communica-
tions – may optionally be used. In this case, the node that wants to send a data packet first
sends a Request To Send (RTS) packet to the designated receiver which, if ready, responds
with a Clear To Send (CTS) packet. Both RTS and CTS packets contain information about IFS

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