Data Science Case Study: Wireless sensor network mathematical Models

Describe the Evaluation ,implementation
wireless sensor network protocols. Comparison
and Evaluation of Energy Estimation Model
Evaluation
Implementation And
Comparison Of Energy
Dissipation of Routing
protocols for Wireless
Sensor Network
M Engg Project
Sandeep Sharma

Evaluation Implementation And Comparison Of Energy Dissipation of
Routing protocols for Wireless Sensor Network
P a g e | 1 Student: Sandeep Sharma [Type text]
Evaluation
Implementation
And
Comparison
Of
Energy Dissipation of
Routing protocols for
Wireless Sensor
Network
Name : Sandeep Sharma
Enrollment No: 11092297
Supervisor’s Name: Reiner Dojen

Acknowledgement Page
I would like to thank my supervisor, Reiner Dojen, for her help in
the completion of my dissertation and also for her helpful actions
when personal obstacles arose. Thanks go to the people of
Limerick City for making this an enjoyable year, and also to all
lecturer for thoroughly and attentively proofreading my thesis.
Finally I would like to thank Mr Anish, both a gentleman and a
scholar; I am surprised this man gets any work done with the
amount of time he gives to others.

Index Page
Contents
Motivation .................................................................................................................................................................4
Abstract .....................................................................................................................................................................5
SECTION 1: BACKGROUND .......................................................................................................................................5
Chapter #1: Introduction to Wireless Sensor Networks.............................................................................................6
Chapter #2 :Protocol and Technology........................................................................................................................9
Chapter #3 : Applications and Usage of WSN example ...........................................................................................12
Chapter #4 :Security Needs of WSN and estimating cost of security.......................................................................15
Chapter #5 :(Routing) Protocols for WSN ................................................................................................................17
Chapter #6 : Introduction to the estimation model PPECEM [FaZ12] and Comparison with other model of Energy
Estimation for (Routing) Protocols for WSN. ...........................................................................................................22
Chapter #7: Evaluation and implementation of PPECEM [FaZ12] the estimation model ........................................24
Chapter #8 : Comparison of protocols efficiency .....................................................................................................26
Chapter #9 :Resources/Equipment Used during experiment and procedure for project extension of project to
security algorithms (if time permits): ......................................................................................................................27
SECTION 2 :IMPLEMENTATION WITH PRIOR EXISTED TECHNIQUES ......................................................................29
Chapter 10: SPIN Protocol implementation:............................................................................................................29
Chapter 11: CTP Protocol implementation ..............................................................................................................33
Chapter 12 : Cryptography Code AES...............................................................................................................36
SECTION 3 : OWN ORIGINAL WORK .......................................................................................................................37
Chapter 13: Energy Measurement Cost of Routing and security Challenges .............................................37
Chapter 14: Enhancement/Improvement in Existing model: ..........................................................................38
ERA: Efficiency, Reliability, Availability.................................................................................................................41
Chapter 15 : ERAECEM ......................................................................................................................................45
Proposed New Energy aware Routing Algorithm ERAQP..............................................................................47

Chapter 16 Mathematical Model to study behavior of WSN...........................................................................49
Chapter 17: Incorporating Data bias: .................................................................................................................54
Fuzzy Measurement Model .................................................................................................................................54
17.3 Routing Algorithm 2 proposed: Fuzzy Rank Routing.............................................................................54
17.4 Configurable Routing Algorithm .................................................................................................................55
Data bias based on Fuzziness............................................................................................................................56
WSN Fuzzy Information Fuzzy Neural network................................................................................................56
Chapter 16: Future Direction: : sensor Cloud, Sensor and BPM, Sensor ERP, sensor security ..............57
Conclusion: ............................................................................................................................................................59
References.............................................................................................................................................................60
Motivation
Wireless sensor network (WSN) are penetrating deeper into Internet of things (IoT),Home Automation
using Zigbee stack, Sensor Web is present everywhere with project like smart dust, Sensor ERP tracking
your Bills of material to minutest detail as it moves from factory to homes, Sensor cloud present biggest
challenges to Bigdata and cloud technologies with vast amount, variety, velocity of data it produces ,
move towards ubiquity and wearable computing, to weather monitoring, space applications, inside
vehicles and aircrafts, sensing sunami to monitoring applications in office and factory Applications. As
WSN are becoming pervasive each day to enter new platforms, hardware, environments, applications and
industries we can only say new wave of EVERYWARE computing has begin to take shape and redefine
User Interfaces, devices, Human computer Interactions, experiences. Since technology which needs to
ubiquitous, mobile and pervasive have to be wireless So more of sensors would be wireless. As sensor
move into wireless and there usage expand in numbers and number of applications on them most daunting
challenge they face is Energy Conservation and Estimation due to fact that WSN mote design is
dominated by battery size to it power it needs to sustain itself without a permanent direct power
replenishment. As miniaturized WSN and far flung deployed WSN battery size limitations continue
Energy Consumption Modeling would me even more critical. Hence we here are attempting to Model this
challenge using multiple approaches of better ways to understand WSN challenges
--

Abstract
The Goal of the Project is to Assess and improve the Energy Assesment Model for Wireless Sensor
network(WSN)and suggest new methods,Energy Assesment models,routing algorithms and methodology
to Ease future development of routing algorithm like here configurable approach is suggested and suggest
models to study WSN.
In the project 4 new Models are suggested in approaching mathematical study of Wireless Sensor
Network and its 2 models for energy efficieny of routing Algorithm. In project firstly, I needed to
implement 1 routing algorithm over wireless sensor motes actually 2 existing SPIN , CTP were
implemented then secondly, take measurement of energy consumed while routing by wireless sensor
motes as mostly motes are deployed in far flung places have to conserve energy hence is most critical and
Third validate existing PPECEM Model [FaZ12] by simulating measuring and sustituting empricial
measure of energy using oscillator into PPECEM Model.
Fourth, suggest improvement in existing model for which efficieny, reliability, availability measure
introduced. Fifth, Suggest new Model for comparing energy efficiency of routing algorithm for which
new model ERAECEM Efficiency Reliability Availability Energy consumption Estimation Model was
suggested.
Sixth, ERAQP new energy aware routing algorithm. Suggest any improvement in routing algorithm using
model. Here using New energy efficieny Model for WSN a new energy aware routing algorithm has been
suggested
Seventh, Configurable routing approach
project also proposes new configurable routing algorithm approach/methodology to create future
algorithms on WSN motes whereby WSN motes algorithm is configurable to user defined QoS
parameters
Eighth, suggest mathematical model to study WSN. There are more than 4 models Leader Follower
Model, Directed Diffusion Model suggested in the project to study wireless sensor motes.
Ninth, Fuzzy Routing Algorithm
Tenth.Fuzzy Information neural network topology representation.
SECTION 1: BACKGROUND

Chapter #1: Introduction to Wireless Sensor Networks
Chapter #1
Introduction to
Wireless Sensor Networks
.
1. Introduction
A wireless sensor network (WSN) essentially ad hoc networks consists of spatially
distributed autonomous sensors to measure and track physical or environmental
conditions, such as temperature, sound, pressure, etc. and to disseminate their data
through the network to a main location. Today WSN are bi-directional enabling bothways
control of sensor activity. Military applications motivated development of wireless
sensor networks such as battlefield surveillance; today WSN are used in many space and
planet probe, security applications,industrial and consumer applications, such as
industrial process monitoring and control, machine health monitoring, and so on.
Figure 1: Wireless Sensor Network fig source [RHGS09]:

As example compare two Wireless technology for connecting devices we have to Choose based
on:
Parameters to choose from IEEE 802.11 WiFi IEEE 802.15.4g ZigBee
Power : (battery life time) 1 day 3-5 yrs
BitRate 54 mbps 250 kbs
Range 100meter 1000 meter
Access Point to Extend
Range
Gateway provide connectivity
wireless Nodes to backbone
Mesh routing some node act as
Router (which pass data end
node to gateway)
ZigBee is a popular low cost, low power WSN mesh network standard require the devices should have
battery life of 2 yrs putting constraint on Energy utilization by WSN motes.
Figure 2: Zigbee specification
WSN consist of many sensor motes web also called sensor web.Each of the WSN mote consist of:
1. a radio transceiver having internal antenna or connection to an antenna outside.
2. a microcontroller, an electronic circuit for interacting with the sensors and
3. an energy source, mostly battery sometimes embedded form of energy harvesting.
4. Sensing Hardware, sensing pressure, temperature, velocity depending on application.
Sensor module collects the data from surrounding environmental mostly in analog format such as light,
sound, shocks, etc, and converts it to the digital signal via the analog to digital converter (ADC), and then
transfers to the processing unit. The processor module interfaces between the units used in the sensor, the
interactions between other WSN nodes in the network, and may provide security by data encryption and
MAC digest generation. The WSN nodes and the Base Station (BS) interact between each other using the
wireless communication module. The Sensor unit,Transmitter/receiver antenna unit , processing unit are
powered by the power unit, which is usually composed by battery [RoM04].
Figure 2 : Distributed sensor network sensing and passing information to server through base station.

Wireless sensor motes are under Size and cost constraints resulting in constraints on resources such as
energy, memory, computational speed and communications bandwidth. Even today WSN are made to
form Wireless Personal area network (WPAN) which also will form important component of wearable
computing, home automation. Institute of Electrical and Electronics Engineers (IEEE) released the
802.15.4 low-power wireless personal area network (WPAN) standard in 2003.
Predominant standards commonly used in WSN communications include:
1. WirelessHART[Hart01]: developed by 37 HART Communications Foundation (HCF) companies
define a protocol for time synchronizing, self organizing and self healing mesh up architecture.
2. IEEE 1451: is a set of smart transducer interface standards developed by IEEE. It provides open,
common, network independent communication interface interfacing between sensors.
3. ZigBee [Zigb02] / 802.15.4: IEEE 802.15.4 is a standard specifies physical layer and media
access control for low-rate wireless personal area networks (LR-WPANs)
4. ZigBee IP (ZIP): [Zigb02] open standards based IPv6 stack for smart objects. ZIP include other
IETF standards which allow network joining procedures, service discovery, and include security
mechanisms like TLS/SSL.
5. 6LoWPAN: The internet protocol which can be used in smallest of the devices would play
important role in Internet of things (IoT),smart grids .For IPv6 over Low power Wireless Personal
Area Networks IETF has a working group. ZigBee and proprietary networking solutions that are
vertically integrated with link-layer and application profiles only resolve a small part of the
applications problem for wireless embedded networking. 6LoWPAN lowers down the barriers of
utilizing IPv6 in low-power, processing-limited embedded devices over low-bandwidth wireless
networks. They also have problems with scalability, evolvability and Internet integration.
Concept is Internet of things where the Internet Protocol could and should be applied even to the
smallest devices or embedded devices called smart Objects .Used for IP header compression and
Neighbor Discovery,and RPL(Routing Protocol for Low-power and Lossy Networks) for mesh
routing.
Wireless Sensor Network
Operating systems Contiki ,ERIKA Enterprise, Nano-RK, TinyOS ,LiteOS ,OpenTag, NanoQplus
Industry
standards
ANT, 6LoWPAN, DASH7, ONE-NET, ZigBee, Z-Wave, Wibree, WirelessHART,
802.15.4, MiWi
Programming
languages
C, LabVIEW,nesC
Hardware Iris Mote, Sun SPOT, Xbee, Arduino
Software TinyDB, TOSSIM, NS-2, OPNET, NetSim, LinuxMCE
Applications Key distribution, Location estimation, Sensor Web, Telemetry
Protocols AODV, DSR, TSMP
Used in Project OS (TinyOS), platform(Micaz), Programming language C++,Protocol(AODV,
DSR, TSMP)

Chapter #2 :Protocol and Technology
Chapter #2
Protocol and Technology
2.1 Structure of Wireless Sensor Network WSN:
2.2.1 WSN hardware: four basic components: sensing units, a processing unit, a transceiver unit
and a power unit. Sensing units are usually composed of two subunits: sensors and analog-to-
digital converters (ADCs). The analog signals generated by the sensors based on the observed
phenomenon or stimuli are converted to digital signals by the ADC and passed to the processing
unit. The processing unit, which is attached with a storage unit, handles the procedures that make
a node interact with the other nodes for carrying out the assigned sensing tasks. A transceiver
unit attaches the node to a network. Energy-scavenging or Energy harvesting tool and material
like piezoelectric material surfaces such as in solar cells are regular feature of Power Unit.
2.2.2 WSN software:
Software make interfacing between all units possible is most critical area in WSN. As Energy is
the most scare resource of WSN nodes, as determines the lifetime of WSNs motes. So algorithms
and protocols need to address the following issues:
Lifetime maximization, Robustness and fault tolerance ,Self-configuration.
2.2.3 WSN Operating system: less complex than general-purpose operating systems, more
strongly resemble embedded systems as they are deployed a particular application in mind rather
than as general platform hence and environment requirements vary too much which leads to very
few common attributes and low power and low costs leads to low-power microcontrollers
ensuring that mechanisms such as virtual memory are either unnecessary or too expensive to
implement. embedded operating systems such as eCos or uC/OS for sensor networks but OS
need real time properties for which
 TinyOS [TN01](first OS for WSN) TinyOS is developed by Stanford University and
based on event driven programming model rather than multithreading. Considered best
OS for deep embedded networking
Programs inside TinyOS have event handlers and task which run it to completion using
semantics.. When an external event occurs, such as an incoming data packet or a sensor
reading, TinyOS signals the appropriate event handler to handle the event. Event handlers
can post tasks that are scheduled by the TinyOS kernel some time later. TinyOS used in
sensor networks, ubiquitous computing, personal area networks, smart buildings, and
smart meters. programming is mostly done using language called nessC. nessC has set of
modules having interfaces. Each interface is two way functions where it can take input
and trap and event as well issue a command. It can execute certain task based on input

event and execute certain commands as desired. nessC has set of well-defined interfaces
for task such as Packet for packet creation, AMPacket for multiplexed packet to sent
through same communication channel. AM signifies Active Message. Interfaces exist for
Cryptography, sensing, Analog to digital conversion. Interface usage also depends on
what sensors are available on hardware you are using like cryptography library would
require CC2420 require RF chip.
 LiteOS [LITE01] newly developed OS for wireless sensor networks, which has UNIX-
like abstraction and can be programmed using C programming language.
 Contiki [Cont01]: is an OS which uses a simpler programming style in C while
providing advances such as 6LoWPAN and Protothreads [AOTM07] ( is a low-overhead
mechanism for concurrent programming) function as stackless(call stack),
lightweight threads providing a blocking context cheaply using minimal memory per
protothread (on the order of single bytes).
 RIOT: has microkernel architecture, multithreading, support for C/C++. RIOT supports
6LoWPAN,IPv6,RPL,TCP,UDP.
2.3 Topology:
Success of WSN depends on success of reliability of protocols being used. Wireless medium has
so many unknown obstructions in real world like interference from objects and devices so there
is high probability that data being sent may not reach destination like obstruction from thickness
of wall chair and some nay things around. So for reliability of there network star or hub topology
does not guarantee reliability. Only solution is meshUp network of wireless sensor motes
whereby each mote can communicate to each other motes thus creating many redundant paths
and hence can reach through any of these redundant paths and guarantee reliability.
Due to this random nature of selection of path the router motes aggregating data and transmitting
to the nearest Server may get drained out of power hence there is redundancy in server motes
whereby any of mote is capable to become one router mote or cluster head hence sharing the
burden.
Energy efficient protocols for interfacing with other motes are order of the day.
Localisation awareness also helps in deriving intelligence out of wireless sensor data hence
Location aware networking protocols were also developed for communication.

2.4 Platform used in Project
This analysis examines the memory requirements ,execution times and energy efficiency of thee
employed algorithms when implemented on aMicaZ sensor node (CrossBow, 2010) running
TinyOS 2.1.0 (Levis et al., 2005). The power consumption is measured using the Agilent
66321D Mobile Communications DC source and14565B Device Characterization
Software(Agilent, 2007). security challenges to WSN include on top of those that exist for
networking:
• the wireless broadcast medium is easier to tap than guided media;
• the wireless medium has limited capacity and therefore requires more efficient schemes with
less overhead;
• the self-forming, self-organization and self-healing algorithms required for ad hoc
networking,and the schemes that tackle challenges such as hidden and exposed terminals, may be
targeted to design sophisticated security attacks;
• the wireless medium is more susceptible to jamming and other denial-of-service attacks.

Chapter #3 : Applications and Usage of WSN example
Chapter #3
Applications and Usage of WSN example
3.1 WSN applications currently running in Ubiquitous computing, Smart Grid, sensor web
[DKSJ01](sensing utilizes World Wide Web),solar energy harvesting, Air Quality monitoring,
Natural disaster prevention, Industrial monitoring like machine health monitoring, network
Infrastructure management, Smart home monitoring, localization and tracking.
Figure 3: Solar powered sensor Web ,sensor Grid.
Sensor Web utilizing solar Energy harvesting synchronous and router free.Sensor Web is
the infrastructure enabling access to sensor networks and archived sensor data that can be
discovered and accessed using standard protocols and application programming interfaces.
A sensor grid [HJHM03] integrates wireless sensor networks with grid computing concepts to
enable real-time sensor data collection and the sharing of computational and storage resources
for sensor data processing and management. sensor grids are well suited for adaptive
and pervasive computing applications.

3.2 WSN in Ubiquitous computing
Figure 4: WSN is the center for Ubiquitous computing and internet of things.
3.3 Smart Dust [KJB97] —Early Event Driven Smart Dust platform made of many tiny
Microelctromechanical systems (MEMS) such as sensor, robots and other devices that can detect
light temperature ,magnetism, chemical, and vibrations and perform certain task wirelessly
proposal was given at the University of California Berkeley.
3.4 One of biggest implementation of WSN is Message Queue Telemetry Transport (MQTT).
MQTT is machine to machine connectivity protocol for Sensors is aimed at embedded devices
on non-TCP/IP networks. MQTT is extremely light weight enables the transfer of telemetry-style
data in the form of messages from pervasive devices, along high latency or constrained networks,
to a server or small message broker. Pervasive devices may range from sensors and actuators, to
mobile phones, embedded systems on vehicles, or laptops and full scale computers. The protocol
was invented by Andy Stanford-Clark of IBM, and Arlen Nipper of Cirrus Link Solutions.
MQTT [MQTT07] is also used by Facebook Messenger notices on mobile phones, Facebook
Messenger in (iPhone, Android, and Windows apps), IBM smart planet initiative, Location
Aware Messaging for Accessibility: Making information accessible, Smart Lab: Monitoring
experiments at the University of Southampton’s chemistry lab, FloodNet: Monitoring river levels
and environmental information to provide early warning of flooding.
They facilitate many application areas such as tactical surveillance by military unattended sensor
networks, elderly and patient monitoring by body area networks (BANs) and building
automation by building automation and control networks (BACnets). They are, in essence, ad
hoc networks with additional and more stringent constraints.
wireless mesh network (WMN) enable application areas such as infrastructureless networks for
developing regions, low-cost multihop wireless backhaul connections and community wireless
networks. WMNs also provide a wireless backbone for working other mesh, ad hoc or
infrastructure-based networks such as the In ternet, IEEE 802.11, IEEE 802.15, IEEE 802.16,
cellular, wireless sensor, wireless fidelity (Wi-Fi), worldwide interoperability for microwave
access (WiMAX) and WiMedia networks.

3.5 WSN market and Ubiquitous computing
Figure 5 [Zigb02]: Over the decade growth of a Wireless sensor network specifications ZigBee
influence over period of 10 year from green Energy, telecom, smart building.
Zeebee specification market alone pegged at 4.3 billion for home management equipment alone.
Zeebee allicance was launched just 10 year back Fig 3 (shows the growth year by year) now
more than 600+ products are under this platform. And whole ecosystem from System on Chip
vendor like TI to software layer vendors.
Figure 6: WSN : the Internet of things vision Source:[ZSCB11]

Chapter #4 :Security Needs of WSN and estimating cost of security
Chapter #4
Security Needs of WSN and estimating cost
of security
4.0 Security Needs of WSN and estimating cost of security
They need to be more energy efficient and scalable than conventional ad hoc networks, which
exacerbates the security challenges. The security schemes for WSANs should require less
computational power and memory because sensor nodes are tiny and have more limited capacity
than the typical ad hoc network nodes such as a personal digital assistant (PDA) or a laptop
computer.
Figure 7 : Categorizations of Security for WSN
In the present project we are dealing with only software aspect of security as we want to measure
energy efficiency of security features in WSN.we are not dealing with Hardware security of
certain hardware components may be replaced, damaged or put out of service.
Communication Security is designed to prevent classified data in transmission via
communication link being disclosed to unauthorized recipients
Emanation security deals with possibility to receive this radiation and fuse the screen shots, key
strokes and copied documents from a distance
4.1 Energy consumption in WSN
Power consumption in sensor networks can be divided into three domains: sensing,
communication and data processing. Sensing power varies with the nature of applications. Data
communication is a major reason for energy consumption. This involves both data transmission
and reception. It can be shown that for short-range communication with low radiation power,
transmission and reception energy costs are nearly the same. Another important consideration
related to data communications concerns the path loss exponent, Due to the low-lying antennae
is close to sensor networks. Therefore, routes that have more hops with shorter distances can be
more power efficient than routes that have fewer hops with longer distances.

Due to unbalanced energy consumption, WSN nodes on busy routing paths may drain their
batteries faster than other nodes. Thus, the overall networks lifetime is shortened.
Multiple routes can communicate a node and the sink. The aim of energy-aware algorithms is to
select those routes that are expected to maximize the network lifetime. To do so, the routes
composed
of nodes with higher energy resources are preferred. cluster-based routing protocols, such as
[HCB00], [MMB05], [SRS07], and [YBH10], the WSN is divided into multiple clusters, each
with one or more cluster heads (CHs). CHs are located within single-hop communication
distance away from their cluster members (CMs). CHs are responsible for forwarding messages
between their CMs and the BS. The role of CH is rotated among the CMs, to distribute the
additional power consumption caused by routing duties equally among the nodes in a network.
4.2 WSN security and QoS:
The network application business and its functionalities prompt the need for ensuring a QoS
(Quality of Service) in the data exchange. In particular, effective sample rate, delay bounded and
temporary precision are often required. Satisfying them is not possible for all the routing
protocols as the demands may be opposite to the protocol principles. For instance, a routing
protocol could be designed to extend the network lifetime while an application may demand an
effective sample rate which forces periodic transmissions and, in turn, periodic energy
consumptions. Figure below shows the relation of QoS and its dependence to the routing
protocol goal and to the routing protocol strategy.
Figure 8. Relation of QoS and Routing Protocol Goal and Strategy for WSN [Mis10],[LAAC09]

Chapter #5 :(Routing) Protocols for WSN
Chapter #5
(Routing) Protocols for WSN
5.1 Routing Protocols for Wireless Sensor Network.
The stringent power constraints and scalability requirements of sensor networks, which differ
from conventional ad hoc networks, cannot be satisfied by the routing algorithms designed for ad
hoc networks,such as AODV and DSR. Therefore, there are many routing algorithms specifically
designed for wireless sensor and actuator networks.
Routing algorithms for sensor networks can be classified as
1. data-centric: like directed diffusion, sensor protocols for information via negotiation
(SPIN) and power aware many-to-many routing fall into this category
2. cluster-based: Low-energy adaptive clustering hierarchy (LEACH) is an example of a
cluster-based sensor network routing algorithm.
3. location-based: minimum energy communication network (MECN) and geographic
adaptive fidelity (GAF) [YJD01] are location-based routing algorithms.
5.1.1 Flat routing:Sensor Protocol for Information via Negotiation (SPIN)
 Disseminate all the information at each node to every node in the network assuming that all
nodes in the network are potential base-station.
 only distribute to nodes that do not possess similar data
 address deficiencies of classic flooding by negotiation and resource adaptation
 SPIN is 3-stage protocol as sensor nodes uses three types of messages ADV, REQ and
DATA to communicate.
5.1.2 Flat routing: Directed Diffusion
 data-centric and application-aware paradigm (data generated by sensor nodes is named by
attribute-value pairs)
 combine data from different sources enroute by eliminating redundancy, minimizing the
number of transmission; thus saving network energy.
 find routes from multiple sources to a single destination that allows in-network consolidation
of redundant data
 All sensor nodes in directed diffusion-based network are application aware which can enable
energy savings by selecting empirical good paths and by caching and processing data in the
network.
5.1.3 Location based routing protocols:
 sensor nodes are addressed by their locations

 distance between neighboring nodes can be estimated on the basis of incoming signal
strengths
 relative coordinates of neighboring nodes can be obtained by exchanging information
between neighbors
 nodes can go to sleep if there is no activity.
5.1.4 Location based routing protocols: Geographic Adaptive Fidelity GAF
 energy-aware location based routing algorithm
 Network area is divided into fixed zones and form a virtual grid
 Each zone will elect one node for monitoring and reporting data to the BS on behalf of the
nodes in the zone
 Each node uses it GPS-indicated location to associate itself with a point in the virtual grid
 Three states defined in the GAF: discovery, active and sleep.
5.1.5 Hierarchical routing:
 advantages of scalability and efficient communication
 higher energy nodes used to process and send information
 lower energy nodes used to perform the sensing in the proximity of the target
 importance of creation of clusters and assigning special tasks to cluster heads.
5.1.6 Hierarchical routing: Low Energy Adaptive Clustering Hierarchy LEACH protocol
 cluster-based protocol which includes distributed cluster formation
 randomly selects few sensor nodes as cluster heads
 rotate the role of clusterheads CH to evenly distribute the energy load among the sensors in
the network
 clusterhead node compress arriving data from nodes that belong to the respective cluster and
send an aggregated packet to the base station in order to reduce the amount of information
that must be transmitted to the base station.
One of the disadvantages of fixed WSN nodes being selected as routing nodes or CHs through
the system lifetime was the fast battery drain. The dead router nodes or CHs will influence the
network topology and leave blind monitoring areas. Furthermore, the increasing number of dead
WSN nodes starts to isolate a number of WSN nodes from the network, and drastically shorten
the network lifetime.
The randomized high-energy-consumption CH role rotation strategy helps average the energy
burden to every node in the network.
5.1.7 SHRP (Simple Hierarchical Routing Protocol) :
Deal with three different aspects: battery availability, number of hops and link quality to
guarantee the arrival of messages in the sink node in a energy saving way. The SHRP protocol is
concerned with topology maintenance that is directed related to the reliability of data delivery.
To arrange this it makes use of metrics like local battery availability and link quality between
neighbor nodes in choosing the best route into the sink node.
The proposed protocol takes care of link quality, cutting off neighbor nodes from the routing
table nodes that have the average link quality indicator below a minimum threshold. This

threshold is related to the IEEE LQI indicator [SZZGHS06]. The SHRP protocol also cuts off
from the routing table neighbor nodes that have the RSSI (Received Signal Strength Indicator)
values below a minimum threshold [SLP06]. Also, the SHRP protocol cuts off nodes that do not
have battery availability, at least to execute what we call “Minimum Task Cycle” (MTC)
[PHC04].
MTC = CCA + Sensing Task + Transmission Task + Reception Task + Idle Period Task .All the
tasks of cutting off neighbor nodes, shown before, represent that if a node does not have
sufficient battery power or has a bad link quality, caused by interference, multipath or path loss it
will not participate as a route node in the choosing of the best route.
5.1.8 PEGASIS (Power-Efficient Gathering in Sensor Information Systems) [LRC02]
It is considered an optimization of the LEACH algorithm. Rather than classifying nodes in
clusters, the algorithm forms chains of the sensor nodes. Based on this structure, each node
transmits to and receives from only one closest node of its neighbors. With this purpose, the
nodes adjust the power of their transmissions [LRC02]. The node performs data aggregation and
forwards it the node in the chain that communicates with the sink. In each round, one node in the
chain is elected to communicate with the sink. The chain is constructed with a greedy algorithm.
5.1.9 TEEN (Threshold Sensitive Energy Efficient Sensor Network Protocol)
TEEN [MAD09] is other hierarchical protocol for reactive networks that responds immediately
to changes in the relevant parameters. In this protocol a clusters head (CH) sends a hard
threshold value and a soft one. The nodes sense their environment continuously. The first time a
parameter from the attribute set reaches its hard threshold value, the node switches on its
transmitter and sends its data. The nodes then transmits data in the current cluster period if the
following conditions are true: the current value of the sensed attribute is greater than the hard
threshold, and the current value of the sensed attribute differs from sensed value by an amount
equal to or greater than the soft threshold. Both strategy looks to reduce energy spend
transmitting messages.
The main drawback of this scheme is that, if the thresholds are not reached, the nodes will never
communicate; the user will not get any data from the network at all and will not come to know
even if all the nodes die. Thus, this scheme is not well suited for applications where the user
needs to get data on a regular basis.
5.1.10 DirQ (Directed Query Dissemination)
DirQ [CDH06] aims at optimizing the propagation of queries in a wireless sensor network. The
main
objective is that the queries are just propagated by the minimum number of nodes that ensure that
the queries arrive at the nodes that are able to service the query. To do so, certain information is
exchanged in the network. The periodicity of the update messages depend on the rate of variation
of the physical parameters that the network is sensing. Then, each node autonomously maintains
its own threshold (δ).
If a sensor node has a value V of a desired parameter and the next measurement period gets the
same or a similar value in the interval between (δ – V, V + δ) then it decides not to send
anything to sink.

However, if the sink does not receive any message from a specific node then it assumes that this
node has a measured value that has not changed much from what has been reported recently. To
allow a precise delivery of applications, all network nodes must be capable of storing
information which can be considered a disadvantage depending on the amount of information
stored in the topology and the number of nodes. DirQ is a protocol suitable for situations where
the number of requests is high and times of transmission of requests are known.
5.1.11 SAR (Sequential Assignment Routing)
SAR [AlK04] is one of the first protocols for wireless sensor networks that provide the notion of
QoS routing criteria. It is based on the association of a priority level to each packet. Additionally,
the links and the routes are related to a metric that characterizes their potential provision of
quality of service. This metric is based on the delay and the energy cost. Then, the algorithm
creates trees rooted at the one-hop neighbors of the sink. To do so, several parameters such as the
packet priority, the energy resources and the QoS metrics are taken into account. The protocol
must periodically recalculate the routes to be prepared in case of failure of one of the active
nodes.
5.1.12 Maximum Lifetime Routing in Wireless Sensor Networks
This algorithm combines the energy consumption optimization with the use of multiple routes
[ChTa00]. In this algorithm an active route (also called the primary route) is monitored to control
its residual energy. Meanwhile other routes can be discovered. If the residual energy of the active
route does not exceed the energy of an alternative route, the corresponding secondary route is
then used.
5.1.13 Energy Aware Routing in Wireless Sensor Networks
Once multiple paths are discovered, this algorithm associates a probability of use to each route
[ShRa02]. This probability is related to the residual energy of the nodes that form the route but it
is also considers the cost of transmitting through that route.
5.1.14 M-MPR (Mesh Multipath Routing)
This protocol presents two operation modes [DQW03]. Firstly, in the disjoint MPR (D-MPR)
with Selective Forwarding each packet is individually analyzed by the source and it is routed
through different routes. Secondly, the D-MPR with data replication is based on the
simultaneous emission of multiple copies of the same packet through different routes.
Specifically, all the known routes that communicate the source and the destination propagate the
packet. For the route discovery, information about the position of the nodes and about their
residual energy is exchanged.
WSN routing protocol should be secured once it is involved in sensitive data transmission. On
the necessity of securing the routing protocols, there is inevitable energy cost for protecting the
protocol.

Table 1. Summary of the In General Assumed characteristics of the routing protocols that will be
studied in this section. As we can observe, the combination of the optimization techniques is
usual. General Assessment of protocols: source [LAAC09]
Protocol Attribute-
based
Energy-
Efficiency
Applied
Technique
Location-
based
Multipath QoS Hierarchy
SPIN Yes
Directed
Diffusion
Yes
Rumor Yes
COUGAR Yes
ACQUIRE Yes Yes
GAF Yes
LEACH Yes Yes
PEGASIS Yes Yes Yes
TEEN Yes Yes
DirQ Yes
SHRP Yes Yes Yes Yes
SAR Yes Yes
Maximum
Lifetime
Yes Yes
Energy
Aware
Yes Yes
M-MPR Yes Yes Yes

Chapter #6 : Introduction to the estimation model PPECEM [FaZ12] and Comparison with
other model of Energy Estimation for (Routing) Protocols for WSN.
6.0 Introduction to the estimation model PPECEM.
6. Introduction to the estimation model PPECEM [FaZ12] and other model comparision.
We need to effectively and accurately evaluate the energy consumption performance of the
routing protocols as security features also consume power. WSN architects can decide the price
of adding security to their protocols or further estimate the network lifetime of their WSNs. We
can use mathematical models to estimate extra energy consumption due to security features in
WSN.
Heinzelman et al [HCB02] announced a general radio transceiver energy consumption model.
The model takes the transmission distance as one of the key factors, and has been widely used in
many papers to evaluate their announced WSN routing protocols? energy consumption
performance [OFV07],[MMB05] [You04] [MRK05]. Nowadays, more and more WSN routing
protocols are protected by security methods like encryption or MAC. Xiao found a way to
simulate the actual processor overhead while encrypting and decrypting a block of data using
AES algorithm [XCS06]. In their research, they analyzed the algorithm of AES, and deduced the
minimum number of processing cycles required for performing an AES encryption and
decryption.
Heinzelman’s model overlooked the power consumed by the microcontroller; Xiao’s model, on
the other hand, does not involve the energy consumption on the radio transceivers side.
Neglecting power consumption on either microcontroller or radio transceiver may lead to
inadequate or incorrect computational results. Thus, a more comprehensive WSN routing
protocol energy consumption estimation model is required. We want to evaluate the cost of
security in terms of energy consumption of this secure protocol will be established using energy
consumption estimation models.The secure protocol will be implemented and energy
consumption measurements will be carried out. Comparison of the estimated values against the
measurements will determine the precision of the estimation models.
A study of WSN routing protocols, both unsecured and secured, will be undertaken to evaluate
WSN characteristics and limitations as well as attacks against WSN and their countermeasures.
The cost of security in terms of energy consumption of this secure protocol will be established
using energy consumption estimation models.
Among the units in a WSN node, the processor module and the wireless communication module
are two main energy consumers in a WSN node [Kav10]. Among the two modules, the wireless
communication module consumes more energy than the processor module as transmitting and
receiving the message
packets over the air requires a large amount of energy.
Multi-path Routing and Cluster-based Routing Routing is an essential part of any WSN system
[RoM04]. Various routing strategies have been developed, each with its own virtues and
limitations. Currently, there are two main approaches to routing in WSN: multi-path routing and

cluster-based routing [Sin10]. Multi-path routing protocols, such as [LDZ10], and [Zha10],
provide alternative routing paths when the main routing path has become unavailable (cf. Figure
3.1). However, due to unbalanced energy consumption, WSN nodes on busy routing paths may
drain their batteries faster than other nodes. Thus, the overall networks lifetime is shortened.
E.g., in Figure 3.1, the node C and F would die faster than other WSN nodes as they are located
in the busiest routing path On the other hand, in cluster-based routing protocols, such as
[HCB00], [MMB05], [SRS07], and [YBH10], the WSN is divided into multiple clusters, each
with one or more cluster heads (CHs). CHs are located within single-hop communication.
The key variables in Heinzelman’s model are:
 The amount of bytes to be transmitted/received
 The communication distance.
According to the radio model discussed in [HCB02] and [MMB05], the main factors in deciding
the energy consumption are the amount of bytes to be transmitted and received, and the
transmission distance. The longer the transmission distance and the larger the message size, the
more energy is required
Assumption used
The protocols are utilized in equivalent networks, i.e. in networks using the same node hardware
and having the same distance between any two WSN nodes, the message size becomes the only
relevant factor when comparing the energy dissipation of the three protocol variants.
Need for Practical Routing Protocol Energy Consumption Estimation Model (PPECEM) [FaZ12]
To overcome come difficulties of Heinzelman and Xia as Heinzelman’s model overlooked the
power consumed by the microcontroller; Xiao’s model, on the other hand, does not involve the
energy consumption on the radio transceivers side. Neglecting power consumption on either
microcontroller or radio transceiver may lead to inadequate or incorrect computational results.
Thus, a more comprehensive WSN routing protocol energy consumption estimation model a new
WSN Practical Routing Protocol Energy Consumption Estimation Model (PPECEM) is
introduced to provide comprehensive and accurate energy consumptions estimates of WSN
protocol running on MicaZ platform.
From the current drawn by power source voltage supply, microcontrollers, radio trans
receiver in transmission, reception and listening mode Energy consumption can be calculated.

Chapter #7: Evaluation and implementation of PPECEM [FaZ12] the estimation model
Chapter #7
Evaluation and implementation of PPECEM
[FaZ12] the estimation model
7.0 Evaluation and implementation of PPECEM
PPECEM [FaZ12] estimates energy consumption of processor module and wireless communication
module assuming sensor module consume same energy for 2 different WSN routing to compared with
each other.
7.1 PPECEM [FaZ12] can be presented in brief as:
QOverall = QCPU + QRadioTrans + QRadioRcv (Eq. 1).
Thus Overall energy consumption= Energy consumption by( CPU+ Radio Transmitter + Radio Receiver).
Where QX(Y) denotes the energy consumption of the device or device X for operation Y. The devices
include the processor module’s computation (QCPU), the wireless communication modules message
transmission (QRadioTrans), and the wireless communication modules message reception (QRadioRcv).
Security computations take more CPU time then routing computations.
Processor module energy computation based on PPECEM [FaZ12]:
QCPU = PCPU * TCPU
= PCPU * (BEnc * TBEnc + BDec * TBDec +BMac * TBMac + TRadioActive) …………………(Eq.2)
Where
PX in Eq. 2 represents the power of device X.
TX means the computation time of device X
TBY denotes the per-byte time consumed for doing operation Y.
BY indicates the amount of bytes to be computed by operation Y
Enc, Dec and Mac denote the encryption, decryption, and MAC digest generation operations respectively
TRadioActive represents the radio transceiver’s active time, as the processor module remains active while the
radio chip is turned on.
PCPU can be found in the WSN node hardware specifications.
For instance, MICAZ’s PCPU = 8mA * 3V = 24mW.
TEnc and TDec are derived from Xiao’s work on [XCS06]
In Xiao’s work, a 100 MIPS processor requires 59μs to encrypt a block of data using a 128-bit key, and a
123μs for decryption. As discussed in we multiply a factor 1.5 for encryption, and 1.8 for decryption to fill
what Xiao missed [XCS06] has been missed. After applying the factors, we have according to [FaZ12].
TBEnc = (59μs * 1.5*100)/ (N-MIPS*16) ……………………………….. Eq 3. [FaZ12]
TBDec = (123μs * 1.8*100)/ (N-MIPS*16) ………………………………. Eq 4. [FaZ12]
Values of For TBEnc, TBDec for MICAZ mote hardware is equipped with an 8-MIPS processor module.
TBEnc= (59μs * 1.5*100) / (8*16) = 69.14 μs

TBDec = (123μs * 1.8*100)/ (8*16) = 144.14 μs
AES-CBC-MAC divides the message into fragments. Each block has an equal size of X bytes (X E {2, 4,
8, 16}). In case of the last fragment is less than X bytes, it will be padded before it is processed.
XOR operations is neglected as it takes hardly 1% of time.
TBMac = TBEnc * 16 / X ………………………………………………….Eq 5
For X = 8 , it will become: TBMac = TBEnc * 2
7.2 Energy Consumption Wireless Communication Module’s Transmission and Reception
QRadioTrans = PRadioTrans * (KTrans * BTrans + TStartup) ………………….. Eq 6
QRadioRcv = PRadioRcv * (KRcv * BRcv + TIdle) …………………………… Eq 7
For Both Transmission and reception :
QRadio = PRadioTrans * (KTrans * BTrans + TStartup) + PRadioRcv * (KRcv * BRcv + TIdle) from Eq6 and Eq7
Where:
wireless communication modules, like CC2420, require processing to be ready for packet transmission,
which also consumes energy and should be taken into consideration. Deducting PCPU, we can deduct
PRadioTrans and PRadioRcv according to the WSN node’s hardware specification. For MICAZ, they are:
PRadioTrans = 17.4mA * 3V = 52.5mW (for 0dB)
PRadioRcv = 19mA * 3V = 57mW
KTrans and KRcv are the factors related to the time duration for the sensor to send or receive a byte, plus
the time for the sensor to read/write a byte in/from the radio chip.
In MICAZ, they are 40μs/byte.The MICAZ’s radio chip read-write time is 20μs/byte [ZDC11] [HNL07]
[DAB08]. Thus for MICAZ mote hardware, KTrans = KRcv = 60μs/byte.
TStartup denote the processing time for the wireless communication module to be ready for transmission.
TIdle indicates the wireless communication module’s waiting time between two transmitting and / or
receiving tasks.
In general, the proposed new model can be presented as follows:
QOverall = PCPU * (BEnc *(59μs * 1.5*100)/ (N-MIPS*16) +BDec * (123μs * 1.8*100)/ (N-MIPS*16) )
+ PRadioTrans * (KTrans * BTrans + TStartup) + PRadioRcv * (KRcv * BRcv + TIdle) ………………………. Eq 8. [FaZ12]
Using Energy consumption using PPECEM [FaZ12] model are compared with empirical measurement
results to Test efficacy of model for different WSN routing protocols.

Chapter #8 : Comparison of protocols efficiency
Chapter #8
Comparison of protocols efficiency
8.0 Comparison of protocols efficiency:
In WSN routing protocol energy consumption is most critical in deciding lifetime of nodes and structure of
network hence energy estimation model is required. We want to evaluate the cost of security in terms of
energy consumption of this secure protocol will be established using energy consumption estimation
models
Hierarchical and geographic routing protocols are considered scalable solutions. Keeping a hierarchical
structure demands the coordination of nodes by means of transmitted messages. In dense networks, the
use of the cluster-based structure makes up for this cost. However, this benefit does not hold in small
networks. A similar behavior is observed for geographic approaches. When the network is composed of a
significant number of nodes in an extended area, the exchange of messages to establish the location of
neighbors becomes negligible compared to the reduction of transmissions that the geographic algorithm
achieves. In these two approaches, the topology of the network must be stable. On the contrary, the
cluster structure and the geographic information must be frequently updated which leads to additional
costs.
For instance, in stable networks, PEGASIS is usually more efficient than LEACH. However, the
construction of the chains in PEGASIS could lead to significant resource consumption in highly dynamic
topologies. Attribute-based techniques become relevant when the data sensed by the nodes are not
usually of interest to the rest of the nodes. Under these circumstances, the algorithms could greatly
reduce the network overhead. The decision about which algorithm should be selected mainly depends on
the data delivery model that the application forces. When the communication should be triggered by
events, SPIN is the most suitable attribute-based algorithm. However, Directed Diffusion, Rumor,
COUGAR and ACQUIRE are query-driven protocol. They roughly differ in how the query is propagated
and resolved in the network.
Concerning the multipath routing protocols, their main disadvantage lies on the cost of maintaining
the paths. This cost comprises memory resources as well as network overhead. Therefore, they are not
appropriate for networks critically constrained by their reduced batteries. However, they become
necessary when reliability is a strong requirement in the application business.
Identify and evaluate the energy consumption of the routing protocols for WSN. A study of WSN routing
protocols, both unsecured and secured, was undertaken that included discussing WSN characteristics
and limitations, WSN hardware composition, energy consumption and also attacks against WSN and their
countermeasures. Subsequently, a security analysis of the Protocol discussed above was performed and
several weaknesses in the protocol were revealed.

Chapter #9 :Resources/Equipment Used during experiment and procedure for project
extension of project to security algorithms (if time permits):
Chapter #9
9.0 Resources/Equipment Used during
experiment and procedure for project
extension of project to security algorithms
(if time permits):
9.1 Equipment used:
The presented analysis is performed using the widely-used MICAZ mote [GRL08] [KOK09] [XWD09]
[BSP10] as an implementation and test platform (Cf. Figure 9.1). The MICAZ mote is a 2.4 GHz,
IEEE/ZigBee 802.15.4 board used for low power wireless sensor networks, with an 8-bit ATmega128L
microcontroller, 128 KB RAM, 512 KB ROM and a 2.4 GHz Chipcon CC2420 RF transceiver. The
Chipcon CC2420 features hardware AES-128 encryption support with multiple modes: standalone mode,
counter mode (CTR), CBC-MAC authentication mode, and CCM encryption and authentication mode.
Power consumption is monitored using an Agilent 6321D Mobile
Communications DC Source and the Agilent 14565B Device Characterization Software. The Agilent
66321D can be used as a DC source for mobile devices such as MICAZ motes or cell phones. It provides
an output voltage of 0–15 V and current of 0–3 A. While supplying energy to a device, it is able to sample
output voltage and current with a time resolution ranging from 15 !s to 31,200 s. The Agilent 14565B
software has multiple functions: Controlling the Agilent 66321D to supply power with defined current or
voltage. Measuring and recording the current and voltage changes at the designated time resolution.
Generating the current/voltage graph over time.

Figure 9 source [FaZ12] : Hardware/Software Setup work.
Combining the Agilent 66321D DC Source with the 14565B Device Characterization Software, the mobile
device’s energy consumption can be monitored and recorded with high precision (resolution of 2.5 !A for
current measurements and 3 mV for voltage measurement). The MICAZ nodes run on TinyOS, which is
the first operating system designed specifically for WSN [LMP05]. It is an open-source OS that is energy
efficient and provides good power management features. For example, it automatically puts the processor
into soft-sleep mode (using about 3.5 mA for the ATmega128L) when no task is waiting in the queue. For
the analysis presented in this paper, TinyOS 2.1.0 is used. The programming language used to develop
applications for TinyOS is nesC [GLB03], which is an extension of the C language. Furthermore, TinyOS.
supports a wide range of different sensor platforms, offering interfaces to many of their specific features.
9.2 Performance and Power Consumption Measurement
Test applications have been developed to analyse execution times, memory usage and power
consumption of the key setup phase and the encryption/decryption phases of the AES implementations.
To increase the accuracy of the measurements, these applications repeat each operation 100 times. The
resulting average values are then scaled down to a single operation value. Measurements of the ROM
and RAM usages are provided by TinyOS, which provides a function to display memory usage after
successful compilation. The execution time and power consumption are measured with the Agilent
66321D and 14565B. The 66321D supplies the power to the MICAZ node and records the voltage and
current supply/usage. The 14656B processes these records and provides corresponding graphs.

Thesis work:
SECTION 2 :IMPLEMENTATION WITH PRIOR EXISTED TECHNIQUES
Chapter 10: SPIN Protocol implementation:
One of the data centric routing protocol for WSN. Disseminates every information to each node to every node in the
network.
Works in two stages
Stage 1: Negotiate with other nodes before actually sending data. Other Node confirms it received same metadata
information
Stage 2: Energy consumption must be monitored by Each node in the network.
So Each User can Query any node to get information for metadata.3 messages used in SPIN
1. ADV : is metadata used to broadcast advertisement packet to all the nodes.
2. REQ: on receiving ADV any interested node sent REQ packet to Adverisement.
3. DATA: used to send actual data to requesting Node.
Three phases :
Initiation Phase
Data collection Phase
Negotiation Phase
Spin Implementation algorithm as per IEEE paper :
1 Initiation phase
Initialization()
{
// synchronize each node to network global time
call synhNode();
// sensor application is started in each node (possibly except the sink)
call SensorApp();
// Start timer, timer rate is set so that a node periodically senses and builds a data packet
call startTimer();
// this counter value is used as Sequence number
Counter :=0
// send and forward are flags when node has data to send and forward respectively
Then set Send and forward flag as FALSE snd:=false; and fwd:=false;
}
class RoutingPacket
{
*char origin,local_node_address;
bool snd,fwd;
int seq;
}
void callIntialization()

{
// synchronize eah node to a network global time
callsynchNode();
// sensor application is started in each node (possibly except the sink)
callSensorApp();
//Start timer,timer rate is set so that a node periodically senses and build a data packet
callStartTimer();
counter=0;
// snd and fwd are flags set when node has data to send and forward respectively
snd=FALSE;
fwd=FALSE;
}
------------------------------------------------------------------------------------------------------------------------------------------
2 Data collection Phase : In this phase each node receive a DATA packet from sensor or neighbor node
or wireless sensor (WSN) mote. Structure of DATA packet will have address of origin, sequence number, a payload
containing timestamp and sensor reading. With payload stored in memory motes start negotiation phase.
void RoutingPacket::callDataCollection
{
while(true)
{
if (origin == local_node_address)
{
if (local_node_address !=0)
{
snd=TRUE;
fwd=FALSE;
callstorePacket(payload);
callsetCurrent(origin,seq);
callnegociation();
}
else
{
callDataForward();
continue;
}
}// end of top if
// on receiving data from neighbour
if (origin != local_node_address)
{
// check for expected packet
if(getDesired(origin,seq) == FAIL)
continue;
if (local_node_address !=0)
{
snd=FALSE;
fwd=TRUE;
call storePacket(payload);// save data in its memory
/*
indicate which packet is presently residing in memory

*/
callSetCurrent(origin,seq);
//update last data seen record accordingly
callUpdateHistory(origin,seq);
callNegociation();
}// end of if
else
{
callDataForward();
continue;
}
}// end of if
} //untill node is switched off or down end of repeat
}
// end of Datacollection
--------------------------------------------------------------------------------------------------------------------------------------------
3 Negotiation Phase:
ADV packet is sent for negotiation neighbors request REQ for data. And then DATA packet is sent.
Algorithm as explained below:
-----------------------------------------------------------------------------------------------------------------------------------------
void callNegociation()
{
// broadcast ADV packet
callBoroadcastADV (orign,seq,sender);
callWaitREQ();// wait for REQ from neighbours
// request for data is received
if (received_packet_type==REQ)
{
// determine received RERQ is for the presently stored data
if(getCurrent(REQ.origin, REQ.seq) == SUCESS)
{
if (fwd == TRUE)
{
// forward the DATA
mForward(storedPacket);
}
if(snd == TRUE)
{
// forward the DATA
mSend(storedPacket);
}
}// end of getcurrent()
}// end of received packet
// metadata advertisement is received
if(received_packet_type == ADV)
{
// whether it already has seen that data

if(checkHistory(ADV.origin,ADV.seq)== SUCCESS)
{
// send unicast request to the advertiser
callSendREQ(ADV.origin, ADV.seq,ADV.sender);
callSetDesired(ADV.origin,ADV.seq);
}
}
callDataCollection();
}// end of Negotiation
If data is new then mote sends a unicast REQ after receiving ADV. Every packet it intercepts and then check using
function if its desired and update its history. Then node will broadcast ADV or forward to serial port.
Message structure for header of packet.
S no. Field Name Description
1 Type Indicate type of the message e.g. ADV
2 Origin Node Source node’s id which has data about event
3 Sending Node Id of sending node
4 Seq No Packet sequence No
5 Timestamp Indicates time when event was genereated
6 Info Information about sensor Reading
7 Address Address of Source Node
RadioCount2Leds has a 4Hz counter and broadcasting its value through AM packet every time it gets updated.
RadioCount2Leds node hears a counter displays LED’s bottom three bits. Thus This is useful test for that basic AM
communication and timers work.
Tools:
After compiling, RadioCountMessage.java and RadioCountMessage.py files will be created.
RadioCountMessage.java is a Java class representing the message application sends. RadioCountMessage.py is a
Python class representing the message application sends.

Chapter 11: CTP Protocol implementation
CTP designed for low traffic rates and is a tree based collection protocol where a set of nodes
advertise as tree node. CTP is address free and does not direct packet towards a route next Hop
is automatically chosen as Root mote. Routing gradient is used to generate routes.
CTP protocol take some assumption from data link layer(DLL).
- DLL efficient local broadcast address.
- provides unicast packets with synchronous Acknowledgement.
- For multiple higher layers protocols provides with protocol dispatch field.
- Source to destination field in single hop.
Link quality estimates from neighbor provides estimates for number of transmissions required
after acknowledgement is received successfully.
CTP is Bandwidth Exhaustive like it will not pack multiple small frames into single Data link
layer packet.
Collection And CTP
CTP makes routing decisions based on Expected Transmission (ETX). At Root ETX is zero 0.
ETX of Node1 = ETX of Parent Node + ETX of link to parent. There is link level
retransmission.
Loops happen when node choose new node with ETX much higher than its its old one. If there
were decedent routes in route then loop happen.
Its discussed below in Code implementation.
Packet duplication happen when Acknowledgement is not received after packet was delivered
successfully then source retransmit packet again.
CTP Dataframe
Figure 10: A CTP data frame structure

Fields of packet are described as below:
P : Routing Pull.
C: congestion notification.
THL: time has lived.
ETX: Expected Transmission
Origin:
Seq no: sequence number.
colled_id: Higher level protocol identifier.
data: data payload of zero or more bytes.
Program Purpose
CollectionSenderC The virtualized collection sender abstraction.
TinyOS Net2-WG
CollectionC A data collection service that uses a tree routing protocol
* to deliver data to collection roots, following TEP 119.
COLLECTION_H Structure Data + Control + Debug
CollectionSenderP Structure Send , packet
CompareBit Link estimator query the routing engine this entry
Given the white bit whether should Entry be inserted into the neighbor
table. Returns "pin bit" - if true insert into the neighbor table.
Reference implementation router return true
if (path through the source)
better than
(path through at least one current neighbor).
Ctp.h Header files contains AM types, message formats, and constants for the
TinyOS
CtpCongestion Returns the current state of congestion from the provider.
Internal queue congestion may lead to Ctp congested where isCongested
is TRUE.
setClientCongested If notcongested,
Ctp try to slow down.
Ctp has an internal congested condition as well. isCongested =
( parameter set) logical OR and the internal congestion.
CtpDebug Event logged with error_t
Generic:
Events,EventSimple,and EventDbg
Specific Events :
Collection, EventRoute, and EventMsg
CtpDebugMsg Msg uid, origin, next_hop
CtpForwardEngine [OFFSET - (OFFSET + WINDOW)]
Forwarding Engine tells when to send next packet after event.
FAIL: means send fail.
NOACK: packet not acknowledged
LOOPY:Loop detected
OK: packet acknowledged

CtpForwardingEngineP ForwardingEngine does queuing and scheduling outgoing packets in
collection protocol and has a packet send queue and pool of forwarding
messages(in FIFO order).
configuration Contants are defined in ForwardingEngine.h
In CTP all packet routed to same destination
ForwadingEngine treats all packets from any source equal.
ForwardingEngine returns synchronous acknowledgement and retransmit
packet when they are not acknowledged upto max retries limit set.
ForwardingEngine (FE) identify and remove routing loops.
if gradient value of packet less than of node own a loop is detected.
To break loop advertises the gradient value to update sending node.
Prevent correlated traffic from interfering.
Immediate: FE forward immediately if not sending
Success: Sucessfully diliver and wait for next packet.
Ack Failure:FE could not get Acknowledgement.
Forwarding, success, Failure, Ack.
CtpInfo Return neighbor congestion state
,together with routing engine re-compute routes.
CtpPacket Uses message_t to construct
setter and getter for components of header structure.
CtpRoutingEngineP TreeRoutingEngine creates routes for collection in the form of trees.
And for every node find the path having least transmission.proactively
Tree is constructed by beacon sent through every node. Becons are
jittered to prevent synchronization.
Beacon has current hop count, node’s parent, cumulative path quality
metrics
Neighbor table maintains neighbor node information ,parent path
metrics.
Link Quality to parent node information added
(i) deciding a parent node (node with best path metrics)
(ii) deciding a new route.
Neighbor table is subset of link estimator table. It’s uses:
1. tree (parent, child) defines parent child links.
periodically Parent reelected before node sends beacon in
UpdateRouteTask.
2. during message forwarding time Choose next hop towards any root.
Routing Engine task:
updateRouteTask: chooses a new parent.
sendBeconTask:informs neighbor with broadcast of current route.
Any node is allowed to become Root Node and subsets of nodes will in
form of tree route messages through route node.
Through RootControl Interface set, unset, query root state.
CtpRoutingPacket Setter and getter for options in packet

LruCtpMsgCacheC LRU caches CTP packet instances.
LruCtpMsgCacheP Insertion deletion of new element in cache
Chapter 12 : Cryptography Code AES
Encryption AES encryption is used to encrypt sending packet created by interface message_t to the
target mote.Code used SplitControl and Encrypt interfaces.
CC2420 supports following security modes:
1. Counter Mode Encryption.(CTR)
2. Cipher Block chaining message authentication code (CBC-MAC)
3. Counter with CBC-MAC(CCM)802.15.4 2006 standards (
Implementation is based on IEEE [IEE15]
---------------------------------------------------------
interface Encrypt
{
// all should be 16 bytes (128 bits)
command error_t setKey(uint8_t * key);
event void setKeyDone(uint8_t * key);
command error_t clrKey(uint8_t * key);
command error_t putPlain(uint8_t * plaintext, uint8_t * ciphertext);
event void getCipher(uint8_t * plaintext, uint8_t * ciphertext);
}
----------------------------------------------------------
module AesP
{
provides {
interface Init;
interface SplitControl;
interface Encrypt;
}
uses {
interface SplitControl as CC2420Control;
interface GeneralIO as CSN;
interface Resource as SpiResource;
interface CC2420Register as SECCTRL0;
interface CC2420Register as SECCTRL1;
interface CC2420Ram as KEY0;
interface CC2420Ram as SABUF;
interface CC2420Strobe as SAES;
interface CC2420Strobe as SNOP;
}
}

SECTION 3 : OWN ORIGINAL WORK
Chapter 13: Energy Measurement Cost of Routing and security Challenges
13.1 Energy Measurement procedure
WSN motes are loaded with routing algorithm program and then post communication between
energy utilization or energy consumed by each mote is captured by device 66321D Mobile
Communications DC source and14565B Device Characterization Software(Agilent, 2007).
Using device provided software Current and voltage measurement of each battery is taken and
difference between pre-run and post run Energy gives total energy utilized during run under a
algorithmic condition like under SPIN routing protocol. These values are tabulated and
compared to validate PPECEM model of total energy consumption by WSN.
13.2 Cost of Security
These models help us in understanding the cost of security like total CPU cycles eloped during
comutation of MAC or AES encryption using Interface SecAMSenderC packet interface
providing in-line security features like AES encryption under any modes of CTR counter Mode
Encryption, Cipher Block Chaining Message Authentication Code (CBC-MAC) or Counter with
CBC-MAC.
Model defined in project help to mathematically study the cost and WSN as whole system to
suggest better methods. As data diffusion model groups node based on data usage. Depending on
data usage if different components are required on mote implied different cost.
Same is true about security being applied on each group specifications the cost of each group
will differ when we have clear segmentation of nodes as depicted by gephi graph colored nodes
we can easily estimate the cost of security. For this the log data of network is fed into Gephi for
analysis.
13.3 Security challenges:
OS memory security Issue:
Also In WSN motes the distinction between User address space in memory and kernel address
space does not exist. So any program can enter from user space into kernel address space which
can cause is huge security risk. As program enters kernel address space it is capable of any sort
of manipulation at OS level. Wireless transmission can be trapped with help of another sensor
not if not encrypted as discussed in previous section.

Chapter 14: Enhancement/Improvement in Existing model:
Reliability Measure, Efficiency of Energy Usage by Node, Availability of Nodes
14.1 Existing Model
Processor module energy computation based on PPECEM [FaZ12]:
QCPU = PCPU * TCPU
= PCPU * (BEnc * TBEnc + BDec * TBDec +BMac * TBMac + TRadioActive)
……………………………………………………………………………………………(Eq.1)
Where
PX in Eq. 2 represents the power of device X.
TX means the computation time of device X
TBY denotes the per-byte time consumed for doing operation Y.
BY indicates the amount of bytes to be computed by operation Y
Enc, Dec and Mac denote the encryption, decryption, and MAC digest generation operations
respectively
TRadioActive represents the radio transceiver’s active time, as the processor module remains active
while the radio chip is turned on.
PCPU can be found in the WSN node hardware specifications.
For instance, MICAZ’s PCPU = 8mA * 3V = 24mW.
TEnc and TDec are derived from Xiao’s work on [XCS06]
14.2 Parameters for new Model utilizing QOS for Energy Assessment Model : ERA
The Network strength can be calculated by QoS parameters. QoS like Low throughput, Dropped
packets, Errors, Latency, Jitters, Out-of-Order delivery determine how much network have work
Extra hard to achieve communication. E.g. Low throughput and out of order delivery means
delay for other packets, Errors , dropping of packet ,jitters may imply retransmission. All these
are extra cost to network.
Wireless sensor network (WSN) has 3 components transmission, reception, and cryptography.
All these 3 probability of successful transmission per node Ptr ,Probability of successful
reception Prc ,Probability of successful Cryptography Pcry define the QoS parameters for
Wireless sensor network. As these signify no retransmission if transmission, reception or
cryptography is success full.
Each transmission, reception, cryptography consumes energy. Means these probability directly
affect the energy.

Hence we define 3 measures Efficiency, Reliability and Availability based on the probability of
transmission, reception and cryptography as they directly affect energy.
To illustrate as case Even in voice communication Grade of service is used as QoS measure
Grade of Service = Number of lost call/number of offered Call
Which if we see carely is like probability of lost call to total no of offered calls.
Same way Ftr is probability of lost packet to total number of offered packet.
And Ptr probability of success transmission of packet to total no. of packet offered.
14.3 Energy Efficiency :
(i) Efficiency of the System: Eff (overall all nodes) = Ptr X Prc X Pcry … (Eq.2)
Max efficiency Can be 1: since product of probabilities cannot be greater than 1.
Ptr Probability transmission of successful transmission of packet to target.
Prc Probability reception of successful reception of packet.
Pcry Probability of crypto functions Not reworked on decryption due to re-transmission or other
contributing factors.
Y-axis probability range 0-100%. Under 1 sigma on binomial distribution curve 68% wireless
sensor mote fall with median highest range probability.
Then comes second set covering 68+27.2= 95.2% which are in 2 sigma range both sides.
Then comes 3 sigma = 95.2 + 4.2 =99.7%.
Figure 11: Ptr on Y-axis(p) over range of random number of motes(n) on X-axis.
Here P will be near to 1 hence curve will be sharp like green one. But if environment is not
conducive then probability falls sharply curve will more distributed like red one.
This was for transmission similarly for reception and for cryptography. 2 different probability

distribution over X axis of Number of motes (X-axis will remain same for 3) while the curve will
fluctuate in terms of sharpness of the curve.
While Ptr on Y-axis there would be maximum (due to nature of ad-hoc routing ) utilizing the
motes well have 68% around will fall in 1 sigma.
Based on fact the any sample population scores over random set over period of time in general
fall in binomial distribution. Center on curve is taken by median number of motes Like 10
below:
# Normal Distribution PDF
#range
x=seq(0,20,length=200)
#plot each curve
plot(x,dnorm(x,mean=0,sd=sqrt(.2)),type="l",lwd=2,col="blue",main='Normal Distribution
PDF',xlim=c(0,20),ylim=c(0,1),xlab='X',
ylab='φμ, σ²(X)')
curve(dnorm(x,mean=10,sd=1), add=TRUE,type="l",lwd=2,col="red")
curve(dnorm(x,mean=10,sd=sqrt(5)), add=TRUE,type="l",lwd=2,col="brown")
curve(dnorm(x,mean=10,sd=sqrt(.5)), add=TRUE,type="l",lwd=2,col="green")
Figure 12: Ptr on Y-axis(p) over range of random number of motes(n) on X-axis.

Here we apply Statistical process control to restrict quality of node within range LCL (Lower
control limit) and UCL Upper control Limit (ucl). To set the quality within range for
communication for transmission, reception and crypto measure.
But this will not give combined picture. Hence we see that (reliability, avaibility, efficiency) also
follows binomial distribution as well. Then How can we measure combined QoS for three
parameters.
Here is how we will use combined model to measure and statistically control this variation with
range on LCL and UCL.
14.4 Improvement in model
Efficiency of Energy Model: QEff=QCPU X Eff (improvement #1 in Zang model)
Where given my Equation 2:
QCPU = PCPU * TCPU
= PCPU * (BEnc * TBEnc + BDec * TBDec +BMac * TBMac + TRadioActive) …………Eq 3(Zang Model).
Previous model does not incorporate efficiency in transmission, reception and cryptography
process. Which we calculated in the Equation 2.
But here overall efficiency will hide various node specific variations. Hence we need to apply
measure on each node. We need to include bias these measure based on quantity of data passing
through mote which we have introduces later in topic of Data bias in thesis. Each mote/node
efficiency is multiplied with (percentage/amount) of data passing through those motes. And we
need to sum these over all the motes to arrive at combined measure.
ERA: Efficiency, Reliability, Availability
2.1 Efficiency ENode1= (Ptr X Prc X Pcry)Node1.
Summation of Each node Reliability X percentage of transmission from node.
Failure of transmission Ftr = (1-Ptr)
For probability distribution:
( ) ∑ ( )
Ftr = (1-Ptr), Frc = (1-Prc), Frc = (1-Ptr)
Total FNode1= Ftr X Frc X Fcy As multiplication of fractions always lead less than 1 so is
probability.
Total PNode1= Ptr X Prc X Pcy.

For All other nodes k=1 to N
( ) ∑ ( )
Where F represents Fnode1…n.
And P represents Pnode1…n.
This model represents in binomial distribution
Efficiency Graph for network: Probability P Y-axis over No. of nodes on X axis.
Total Reliability 1 will represent Exploitation of Nodes lifetime with minimum Energy
Consumption.
Some Nodes designated a Router Nodes will pass more traffic. If we draw curve of each
node taking up traffic in given amount of time t. It will be binomial Distribution at time t.
( ) ∑ ( ) ( )
Where ………………………………………………………………..(Eq. 5)
E is Reliability of Node given by Eq. 3
a is percentage traffic passed through Each node.
Pa = P*a for each node.
Figure 13 : Efficiency E on Y-axis(0 to Highest(PXa)) over range of random number of motes(n) on X-
axis.

Reliability of Each Node:
For Reliability Engineering one of the important technique is Failure Mode and effect analysis(
FMEA) . FMEA is used to mitigate risk either by reducing failure probability or reduction is
servity.Hence failure probability are calculated based on.
Reliability is taken multiplication of probability of transmission failure, probability of reception
failure and probability of cryptography failure.
probability of transmission failure Ftr= (1-Ptr)
probability of reception failure Frc= (1-Ptc)
probability of cryptography failure Fcy= (1-Pcy).
Reliability of Each node Rnode1 = FtrX FrcX Fcy……………………………………..Eq.3
Avg Reliability of Each node Rnode1 = (Ftr+ Frc+ Fcy)/3 ….……………………….Eq. 4
Energy used for Reliability Enode1= RNode1X QCPU node1 ………………………………..Eq. 4
Total Reliability = Summation of Each node Reliability X percentage of transmission from node.
=∑ ( )( ) ( )
Total Reliability 1 will represent Exploitation of Nodes lifetime with minimum Energy
Consumption.
Some Nodes designated a Router Nodes will pass more traffic. If we draw curve of each node
taking up traffic in given amount of time t. It will be binomial Distribution at time t.
( ) ∑ ( )
Where ……………………………………………………….(Eq. 5)
R is Reliability of Node given by Eq. 3
a is percentage traffic passed through Each node.

2.2 Availability Of System in terms of Energy:
Availability = MTBF / (MTBF+MTTR). …………………………………..Eq.6
MTBF Mean time between Failure. Time detected to retransmitted
MTTR Mean time to resolve.
In Ad-hoc routing many alternate path exists and Any Node can assume role of router means
MTTR measure depends on Algorithm re-composition. And Manual component of replacing
battery at node level cannot be Estimated as its dependent on scenario. We measure
availability.
MTBR=(1- Probability percentage machine in failure mode.) X time taken to recover.
Availability Metrics using alternate computation For Each Node.
It quantifies total availability TF to represent Total time % of traffic under failure condition.
Availability = 1-TF/(sum total time of all motes under transmission). …………………….Eq 7
Failure Node1 at transmission Ftr= (1-Ptr) X TTrans ……………………………………….Eq.8
Where Ttrans it time to re-transmit Failed packet.
Failure Node1 at Reception Frc= (1-Prc) X Trec ……………………………………………Eq. 9
Where TRec it time to re-transmit Failed of packet at reception. Like out of buffer.
Failure Node1 at Cryptography Fcry= (1-Pcry) XTrec …………………………………………Eq.10
Where Tcry it time to re-calculate Failed packet crypto.
Total Failure TFNode1 = Ftr+ Frc+Fcry ……………………….………………………………….Eq. 11
Each Failure Also consumes Extra Energy to compensate.
Total Extra Energy Consumption = TF Node1 X QCPU
Failure of All nodes Combined
( ) ∑ ( )
Where ………………………………………………………………………………………. Eq. 12
a, represents % of data routed through the Node.
TF is given by (Eq. 10).

Chapter 15 : ERAECEM
15.1 ERAECEM Model
Efficiency Reliability and Availability Energy Consumption E Model
Etotal = Average(Eff + R +A)= (E+R+A)/3
Efficiency of Energy Model: QEff=QCPU X Etotal (improvement #1 in Zang model)
15.2. Benefits of representing in new Model format :
15.2.1 We can have LCL, UCL to track variation in efficiency.
Figure 14: depicting UCL and LCL range within Mean variation in QoS
Let’s define for over control process we figured out that we want to control process within 3 σ
UCL= mean+3 σ
LCL= mean - 3 σ
It will depend on precision we want to achieve. Since normally packets transmitted for normal
actual messages/communication in field will be in millions we can apply six sigma.
Like 6 σ is 1 variation per million packet passed.
Here note defect Vs defective.
If packet is defective on count of 2 defects it will be counted as 2 defects not 1 defective packet.

We can have R-chart or S-chart to control process of efficient packet transmission, reception and
efficient application of crypto-functions on packet.
X-Bar + Range Charts (R-Chart):
Central line calculated by means and
UCL = mean + average range.
LCL= mean – average range.
X-Bar + Std deviation Charts (S- Chart).
UCL = mean + average standard deviation.
LCL= mean – average standard deviation.
Figure 15 : Process of constructing R chart and S chart.
15.2.2 We can calculate standard Error, deviation and other measure to pin point problem
where they exist.
The other measure of interest which can be calculated due to model for better Statistical
Process Control SPC.
- The mean (expected value) is np.
- The variance is .
- The standard deviation (standard error) is .
- Energy Qcpu Overall = Qcpu X Mean of distribution
=Qcpu X np

Proposed New Energy aware Routing Algorithm ERAQP
New Energy aware routing is based on QoS parameters which reflect high on energy usage in
WSN.
Algorithm take combined cumulative (with respect to time) measure of 3 critical parameters
to decide on Which node to be elected as router Node. The node with Minimum value is
selected a router each time and all rest nodes follow them. This allows for proper energy
utilization of power surplus motes while protection to power deficient WSN router motes.
As each time cumulative values of each mote are taken into consideration The motes with
minimum usage are automatically selected as router motes. Thus reliving the power drenched
existing router motes.
Problem with Existing Routing Algorithms: Existing energy aware routing algorithms Like
Maximum Lifetime only focus on available lifetime (in other words Availability) of motes
while neglecting parameters like Reliability and Efficiency measure. Measure like efficiency
of packet sent and received, reliability of or packet sent and received are discounted.
Algorithm proposed balanced view of three parameter dependent on percentage data traffic
utilized and take all motes quantized cumulative values each time thus dynamic in nature in
proposing new router mote.
Quality of Service (QoS) measurement can apply SPC Statistical Process Control using
control charts measure of lower control limit (LCL)and upper control limit (UCL) to the
Binomial Distribution to get 67% cases at 1 sigma, 90% at 2 sigma and 99.99999% 3 Sigma
both sides leading to Six Sigma.
Sigma rate limit on
Figure 15: Statistical Quality Control within UCL and LCL range.

Figure 16: Example of X-bar chart for Statistical Process control of QoS parameters.

Chapter 16 Mathematical Model to study behavior of WSN
16.1 Model Number 2 to study WSN: Leader Follower Model
When any Mote Elected as router starts routing traffic of all other motes to Base Station.
In terms of Mathematical Modeling. A well-defined model explain this behavior called Leader
follower Model.[HS07]
So Every Router is Like a Leader elected especially in ad hoc routing where every client(mote)
can become a Router(Leader) which guide rest of motes to follow the route it defined.
Leader Follower pattern in POSA is used in concurrency control scenario which can cause
deadlock between two processes wanting each other resources to complete task and waiting for
Each other to release the same. The demo leader follower model using netlogo This can be
configured to any parameters. This is In general EACH node share defined diffusion rate given
by slider control on UI which tells quantity it is diffusing with its neighbors.Since it’s a directed
graph so Node B gives data towards Node A while traffic from A towards B may be non-
existent. Histogram shows the Number of nodes Whose value fall in certain range. Now
Figure 17: Simulation run of Leader Follower Model

Leader follower model as depicted by simulation over probability set above for wireless
sensor modes utilizes standard architecture design pattern Leader Follower pattern. To naïve
user in terms of network every client follows a server. Every mote in network follows the
elected router mote also called cluster head (CH). CH aggregate there data towards base
station. Now in network topology for energy aware algorithms the dynamic election of router
mote is always random with preference given to UN-utilized motes (which were not elected
as router mote early). As router motes act a route for many motes thus become energy
deficient over period of time (due to consumption of energy is transmission and reception of
all data coming from many leaf motes).Thus leak motes data always follow towards root or
Cluster Head. Cluster head is every changing as when Energy available changes in mote act
as Leader while leaf mote acting as follower always directing there traffic towards Cluster
head.
Even at OS level for thread and synchronization issues Leader follower Model provides
efficient concurrency model by multiple thread sharing set of event sources to detect,
demultiplex, dispatch and process service request that are generated from these event
sources.
We are extending this Model to wireless sensor Network Leader Follower Model as leaf
motes always detecting new cluster head, and then sending/dispatching data towards Cluster
head and process service request generated by these leaf motes. POSA Leader follower
design pattern
16.2 Directed Diffusion Model:
Like Directed Diffusion of data traffic towards a directed Node. This is only possible
Routing algorithm following directed path and directed diffusion of data towards target mote.
Directed Diffusion demo condition directed traffic biased towards particular path and its
corresponding Histogram. Histogram Equalization. [FU08],[U999]. Mathematical model
represent diffusion of quantity towards a directed network. Helps to understand topology,
density and stability of network and a starting point for designing complex , realistic
Network Model.

Figure: 18 : Simulated run of Directed Diffusion model

Figure 19: Gifi to represent information clusters showing Graph density, modularity,
Average Clustering coefficient, Average path Length
Based on certain characteristics the modes can be grouped under set of cluster head CHs.This
could be depending on data traversed through network .Based on data network make decision
which sub-network to route and group related nodes together. We can study Between
centrality distribution, Closeness of Centrality Distribution, Eccentricity Distribution.
16.3. A broad classification of WSN routing Algorithm specific Models:
Models representing Properties Of WSN (ad-hoc routing). Here Energy Consumption by a
network represents extra cost due to these features of Wireless Sensor network.
Broad Classification and algorithm represented in Table:
Attribute based routing algorithm have total different requirement as compared with
Energy Efficiency algorithm hence some model are Attribute specific and some may be
energy efficiency specific. Previous model we have seen are generic Models. Now we look at
specific models which can be applied to one of the categorization.

Protocol Attribute-
based
Energy-
Efficiency
Applied
Technique
Location-
based
Multipath QoS Hierarchy
SPIN Yes
Directed
Diffusion
Yes
Rumor Yes
COUGAR Yes
ACQUIRE Yes Yes
GAF Yes
LEACH Yes Yes
PEGASIS Yes Yes Yes
TEEN Yes Yes
DirQ Yes
SHRP Yes Yes Yes Yes
SAR Yes Yes
Maximum
Lifetime
Yes Yes
Energy
Aware
Yes Yes
M-MPR Yes Yes Yes
Like Model 1 presented is more relevant to Energy aware category as compared to Attribute
based.

Chapter 17: Incorporating Data bias:
But here We need to bias over measurement based on the amount of data passed through mote.
Otherwise we are not giving enough weightage to those the path or nodes from where maximum
traffic is passing through.
Fuzzy Measurement Model
17.1 Algorithm 2: Fuzzy Measurement Model and Fuzzy Rank Routing:
Each Node represents a set in Universal Set (or complete network). Participation of each Node in
data dissemination/Energy Usage is represented as Fuzzy set.
For a node/Mote A represent by Fuzzy set A :
Fuzzy set A {MoteA, p(A))
Where, p(A) is probability Of Data Usage Or Percentage Load in Fraction Compared With
Global Load.
Why Fuzzy? Because Sensor adhoc network are basically fuzzy in nature where any node can
remain under high utilization or starve for data. Fuzzy denote the membership or utilization of
node as compared with global network.
17.3 Routing Algorithm 2 proposed: Fuzzy Rank Routing
Based on this Utilization p(A) nodes can be ranked in ascending order to find most data dwarfed
node at the top. Then We can apply Dijkstra's algorithm on the network to find best route based
on weight on each node represented by Rank.

Figure 20: Dijestra’s Algorithm using rank to elect route and find route.
This will also ensure availability of nodes which are getting saturated by data traffic.
As we have defined QoS parameter for Node we can rank Motes by efficiency, availability,
reliability parameter. More efficient, available, reliable mote is ranked higher. Numerically
represent lesser no and hence everytime less sum pickedup by Dijestra’s Algorithm.
17.4 Configurable Routing Algorithm
17 Configurable Routing Algorithm:
We are using here 3QoS parameters availability, reliability, efficiency as in the model about.
Based to these selected QoS we rank motes in ascending order than the one with minimum route
length or route cost is selected as route and one with minimum value elected as cluster head or
router mote.
Now here the means of selecting route for data remains dijestra’s algorithm where these ranking
are feed to decide Cluster ahead and route. Now the ranking can be given on any parameters as
the case above shows that we have ranked based on 3 QoS parameters. These parameters can be
increased, decreased, added to list, deleted in list used for ranking motes. Thus we achieve a
Configurable routing algorithm.
Here in option 2 configurable user can decided its own QoS parameter depending on business
requirements to drive routing such as prioritizing a particular request or data from source node,
can add and delete to list thus achieving configurable Algorithm requirement.
Suppose user decide q1, q2, q3 as QoS parameter algorithm rank Motes/nodes based on
combined score of these parameters. Based on this we rank we apply Dijesktra algorithm to
arrive at least path or elect Cluster head to node. Thus q1, q2, q3 can be added, deleted , changed
as per requirement by putting then as entry on this rank table.

Chapter 18.Data bias based on Fuzziness
18 Data bias of nodes and data aggregation along path weightage can be represented using
Information Fuzzy Neural Network.
In this model we try to represent total density of data path due to routing algorithm over a
topology.
As in Model 7 Each node is represented as point is space defined by fuzzy set .
Fuzzy Mote set {elementMoteA, membershipOfMoteA}
The membershipOfA is represented by %data traffic passing through Mote or in other words of
probability
membershipOfMoteA or probability of set = traffic passing through mote / total traffic of mote.
here by traffic we can take No of packets if packet size is same or total data of mote.
This fuzzy set is reflecting data bias drawn over topology of network represent WSN Fuzzy
Information Neural Network.
WSN Fuzzy Information Fuzzy Neural Network.
[JOSE]
The Whole network topology will represent under following condition Fuzzy topological spaces
in which every fuzzy point can be separated from every closed fuzzy set which does not contain
it by open sets (for a more precise definition, see [3]); clearly, every fuzzy regular space is also
fuzzy HausdorFf.
Energy Usage:
We can take QCPU of all packets from a mote and multiply with data usage defined by Fuzzy set.
To come up with combined usage by the network.
∑sum all motes {∑QCPU of all packet from node1 X P(A)}
For combined Energy Usage by all motes biased towards nodes with higher data .

Chapter 19: Future Direction: : sensor Cloud, Sensor and BPM, Sensor ERP, sensor security
Internet of Things (IoT) will make Wireless sensor mote pervasive to exist in environment it
never exited before.IoT is where sensors will start communicating with outside world based on
intelligence like within equipment like fridge alert user that milk is empty and self connect to
Internet using IPV6 (as vast amount of IP addresses will be available for devices) order milk to
nearest retailer after sending authorization workflow to user on Phone(if user authorizes then
transaction is allowed to proceed either by Phone money or bank sites). This authorization
workflow will be governed by Business Process Management (BPM) softwares like JBoss BPM,
pega systems, SAP XI/PI (Process Integration).
BPM is less flexible compared to BPEL Business Process Execution Language and BPMN.
As millions of sensors become online complex orchestration of business processes using process
modeling (BPMN) tools will become critical.
Ubiquitous and sensor web will derive intelligence from semantic web to configure agents on
sensors which will depict behavioral intelligence to user. Like IPV6 TinyOs TEP existed for long
time.IPV6 is critical to both IoT and sensor web.
Smart Dust project has proven that wireless sensor nodes can be quite Ubiquitous and
miniaturized to adjust in any environment. Sensors are everywhere but
Sensor ERP: As sensor systems becomes pervasive into tracking goods produced in factories to
Stock keeping Unit (SKU) on retail stores which will make whole lifecycle of product traceable.
Now this traceability left across different stages of production to storage then to sales and
marketing then in CRM must be traced only in Sensor ERP (Enterprise Resource planning
packages).
Sensor Cloud: Vast amount to data from sound files to images , alert messages produces by
sensors have to be stored and processed. This huge storage requirement mimics the bigdata’s
properties of 5V.
5V Volume, Velocity, Variety,.
Volume: As huge volume of data from millions of sensors have to stored and processed which
only cloud based storage can fulfill.
Variety: The variety of unstructured data text, sound, images, video surveillances etc. completely
matches big data and cloud systems constraints.
Velocity: As data generated every second like surveillance video feeds across the world is no
less than video getting posted on YouTube. Or the text messages posted on Facebook stored
using Cassandra Real time databases are no less in number than all home sensors and office
sensors alert messages.
Veracity: As huge amount of data getting loaded into storage How to make sure data is cleanse
and no junk data getting uploaded?. Junk data Leading to unnecessary storage space and
processing CPU cycles wasted. Can this data be used to develop insight such are trends and
predictive analytics.

Value: If huge data which is getting uploaded no value to en customer unnecessary storage space
wasted processing CPU cycles wasted leading to fall in importance to value of judgment derived
from data.
Open Network Stack and sensor world: Today when we configure routing rules or firewall
allow/deny we see the platform is not flexible enough as a language to write rules freely. Writing
rules as free as well speak language and interoperability making proprietary stack move towards
more open protocols like Cisco/Ericson/Nortel voice phone giving way to SIP based open
systems. Which opened world on interaction between PSTN and softphones using SIP
Gateway/servers.
Similarly Operating System giving spaces to virtualizations to interoperability to exist and
dynamic movement like hot swap of images at data centers from one platform to another.
As Open protocols and systems of networking become norm like virtual switches configurable
by languages , virtual routers technology like software defined Networking
Software defined networking will exist over sensor web and on sensor leading to protocols
becoming almost device independent. By Device independence we mean same firewall
rules,framework,protocol may run in either router OS or Server OS or Workstation or on sensor.
Configuration can be done at any point this will put some extra burden on sensor web.
Figure: virtualized open network Router/switch Software defined Networking.
SDN will make sensor modeling even more important as suppose during a attack on
infrastructure sensor needs to isolated across huge heap just like Virtual OS needs isolation for
security.
Enterprise and Home Automation: Sensor are deployed in every stages of production even robots
derive their senses from sensors and sensor network. Home every devices would be fitted with
sensors and each communicating with each other seamlessly from Air conditioning
communicating to fridge and to TV to water boilers etc.Zigbee alliance as discussed in
introduction to paper thriving to achieve this home automation across various platform of sensor

technologies to co-exist and interoperate.
But there are other breed of industrial sensors which are deployed not only to reduce cost but to
provide accurate feedback on sensing requirement which are beyond human ranges of sensing.
During manufacturing everything is tracked through sensors so nothing can be missed.
Conclusion:
As we have seen in models above the sensor modeling would be even more critical towards
understanding of system as whole , zeroing on the issues in vast sensor web, demystifying orchestration
of sensor interactions with each other leading to complex processes using scientific mathematical
approaches.
Move towards Wireless from wired make Energy modeling even more critical.
Since more sensor will become ubiquitous and pervasive they make to move from wired system toward
wireless making energy modeling even more critical as we have seen that power components of battery
occupy 60-70% of design physical space of Wireless sensor Motes despite using energy harvesting
technologies and pizeo-electric surfaces to re-cycle energy.
Sensor Security: As more sensor came to exist in every part of life sensor pervasiveness becomes norm
and move towards wireless sensors will make them more exposed in airways. The vulnerabilities which
never existed will become more exposed with billion IPV6 sensor devices. So in sensor world as in cloud
computing security is going to consume major chunk of work.
All these will make modeling sensor world most stringent requirement as never before just like data
scientist in Social network world who help predicting and catching early trends.

References
[AkYm05]
Akkaya, K.; Younis, M. A Survey on Routing Protocols for Wireless Sensor
Networks. Ad Hoc Netw. 2005, 3, 325–349
[TNI01]
TinyOS is an open source, BSD-licensed operating system designed for low-
power wireless devices. http://www.tinyos.net/
[RHGS09]
image showing application of WSN
http://blogs.dolcera.com/blog/2009/08/02/wireless-sensor-networks-a-report/
[LITE01] http://www.liteos.net/ LiteOS is an open source, interactive, UNIX-like operating
system designed for wireless sensor networks.
[Cont01] http://www.contiki-os.org open source OS for internet of things.
[AOTM07] Protothreads: Simplifying Event-Driven Programming of Memory-Constrained
Embedded Systems, Adam Dunkels., Oliver Schmidt, Thiemo Voigt., Muneeb Ali. Swedish
Institute of Computer Science http://dunkels.com/adam/dunkels06protothreads.pdf
[Cis01] application of WSN to smart grid by Paul Duffy for Cisco
http://blogs.cisco.com/news/zigbee-ip-extending-the-smart-grid-to-consumers/
[ZSCB11] 6LoWPAN: The wireless embedded Internet - Part 1: Why 6LoWPAN? Zach
Shelby and Carsten Bormann 5/23/2011 on magazine eetimes.com
http://www.eetimes.com/design/embedded-internet-design/4216240/6LoWPAN--The-wireless-
embedded-Internet---Part-1--Why-6LoWPAN-?pageNumber=1
[DKSJ01] Delin, Kevin; Shannon Jackson (2001). "The Sensor Web: A New Instrument
Concept" ,SPIE's Symposium on Integrated Optics 2001
[HJHM03] Hingne, V.; Joshi, A.; Houstis, E.; Michopoulos, J. On the Grid and Sensor
Networks. In Proc. Of International Workshop on Grid Computing. pages 166-173. Nov. 2003.
[HAR08] Survey of Hardware Systems for Wireless Sensor Networks, Mark Hempstead,
Michael J. Lyons, David Brooks, and Gu-Yeon Wei, School of Engineering and Applied
Science, Harvard University, Cambridge, MA 02138, USA Journal of Low Power Electronics,
Vol.4, 1–10, 2008. http://www.eecs.harvard.edu/~dbrooks/hempstead_JOLPE08.pdf
[NSJ13] “Secure Routing Protocol Using Cross-Layer Design and Energy Harvesting in
Wireless Sensor Networks” Nabil Ali Alrajeh, Shafiullah Khan, Jaime Lloret, and Jonathan Loo,
International Journal of Distributed Sensor Networks Volume 2013 (2013), Article ID 374796,
11 pages http://dx.doi.org/10.1155/2013/374796
[Hart01] http://www.hartcomm.org/protocol/wihart/wireless_technology.html
[OTM]Protothreads: Simplifying Event-Driven Programming of Memory-Constrained
Embedded Systems Adam Dunkels, Oliver Schmidt, Thiemo Voigt,Muneeb Ali Swedish
Institute of Computer Science, Kista, SwedenTU Delft, Mekelweg 4, 2628 CD Delft,The
Netherlands, http://dunkels.com/adam/dunkels06protothreads.pdf
[Zigb02] http://www.zigbee.org/ ZigBee alliance 10 years and millions of implementations
every year, ZigBee standards.

[KJB97] Smart Dust was a research proposal [1] to DARPA written by Kris Pister, Joe
Kahn, and Bernhard Boser.
[MQTT07] MQTT community website: http://mqtt.org/documentation
[Agi07] Agilent, "66321D Mobile Comm DC Source w/ Battery Emulation, DVM
Datasheet", Obtained through Internet:
http://www.home.agilent.com/upload/cmc_upload/All/66300series_datasheet_Jan07.pdf?&cc=IE
&lc=eng, 2007. (Accessed 16-07-2012)
[AlK04] J.N. Al-Karaki and A.E. Kamal, "Routing Techniques in Wireless Sensor
Networks: a Survey", Wireless Communications, IEEE, Vol.11(6), 2004, pp.6-28.
[ASS02] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "Wireless Sensor
Networks: a Survey", Computer Networks, ACM, Vol. 38(4), 15 March 2002, pp. 393-422
[HCB00] W. B. Heinzelman , A. Chandrakasan, and H. Balakrishnan, "Energy-efficient
[Atm03] Atmel Corporation, "AVR Application Note 109: Self Programming", San Jose,
California, 2003.
[Atm09] Atmel Corporation, "AT86RF230 Datasheet", 2009. Obtained through Internet:
http://www.atmel.com/Images/doc5131.pdf (Accessed 16-07-2012)
[Atm11] Atmel Corporation, ?ATmega128, ATmega128L Datasheet?, Obtained through
Internet: http://www.atmel.com/Images/doc2467.pdf
[Bar64] P. Baran, "On Distributed Communications Networks", Communication Systems,
IEEE, Vol.12 (1), March 1964, pp. 1-9.
[MMB05] S. D. Muruganathan, D. C. F Ma, R. I. Bhasin, and A. O. Fapojuwo, "A
centralized energy-efficient routing protocol for wireless sensor networks", Communications
Magazine, Vol.43(3), IEEE, March 2005, pp. 8-13.
[SRS07] R. Srinath, R, A.V. Reddy, and R. Srinivasan, "AC: Cluster Based Secure Routing
Protocol for WSN", Proceedings of International Conference on Networking and Services, 2007.
[YBH10] Y. Yang, E. Bai, J. Hu, and W. Wu, "MRBCH: A Multi-Path Routing Protocol
Based on Credible Cluster Heads for Wireless Sensor Networks", International Journal of
Communications, Network and System Sciences, Vol. 3(8), Scientific Research, 2010. pp. 689-
696.
[BBF03] G. Bertoni, L. Breveglieri, P. Fragneto, M. Macchetti, and S. Marchesin,
"Efficient
Software Implementation of AES on 32-Bit Platforms," Lecture Notes in Computer Science,
Vol. 2523, Springer, 2003, pp. 129-142.
[BCK96] M. Bellare, R. Canetti, and H. Krawczyk, "Keying Hash Functions for Message
Authentication", Advances in Cryptology - Crypto 96 Proceedings, Lecture Notes in Computer
Science Vol. 1109, Springer-Verlag, 1996.Fan Zhang: On the Security and Energy Consumption
of WSN Protocols 172
[Bla05] P.E. Black, Greedy Algorithm, Dictionary of Algorithms and Data Structures,
NIST, 2005. Obtained through Internet: http://xlinux.nist.gov/dads//HTML/greedyalgo.html
(Accessed 30-10-2012)
[BPV07] P. Barontib, P. Pillaia, W.C. Vince, S. Chessab, A. Gottab, and Y. F. Hua,
"Wireless Sensor Networks: A Survey on the State of the Art and the 802.15.4 and Zigbee
Standards", Computer Communications, Vol.30(7), Elsevier, 2007, pp. 1655-1695.

[BSP10] J. Borms, K. Steenhaut, K. H. Phung, and B. Lemmens, "A Traffic-Adaptive
Multi-Channel MAC Protocol for Wireless Sensor Networks", Proceedings of the International
Conference on Communications and Electronics, August, Nha Trang, Vietnam, 2010, pp. 11?13.
[CDH06] Chatterjea, S.; De Luigi, S.; Havinga, P. DirQ: A Directed Query Dissemination
Scheme for Wireless Sensor Networks. In Proceedings of the IASTED International Conference
on Wireless Sensor Networks (WSN), Banff, Alberta, Canada, July, 2006
[ChTa00] Chang, J.H.; Tassiulas, L. Energy Conserving Routing in Wireless Ad Hoc Networks.
In Proceedings of the 19th Conference of the IEEE Communications Society (INFOCOM), Tel-
Aviv Israel, March, 2000; pp. 22–31.
[Chi04] Chipcon, "CC2420 datasheet", 2004. Obtained through Internet:
http://inst.eecs.berkeley.edu/~cs150/Documents/CC2420.pdf (Accessed 16-07-2012)
[CoR08] T. Coffey and R. Dojen, "A Formal Verification Centered Design Process for
Security Protocols", Handbook of Research on Information Security and Assurance, Eds: Gupta,
J.N.D. and Sharma, S.K., IGI Global, August 2008, pp. 165-178, ISBN 978-1-59904-855-0.
[Cro12a] CrossBow, "MICAZ Datasheet", 2012. Obtained through Internet:
http://www.openautomation.net/uploadsproductos/micaz_datasheet.pdf
(Accessed 16-07-2012)
[DQW03] De, S.; Qiao, C.; Wu, H. Meshed Multipath Routing with Selective Forwarding:
an Efficient Strategy in Wireless Sensor Networks. Comput. Netw. 2003, 43, 481–497
[GLB03] D. Gay, P. Levis, R. V. Behren, M. Welsh, E. Brewer, and D. Culler, "The Nesc
Language: a Holistic Approach to Networked Embedded Systems", Proceedings of the ACM
Conference on Programming Language Design and Implementation (PLDI), May, San Diego,
CA, 2003, pp.1?11.
[GRL08] D. Galindo, R. Roman, and J. Lopez, "A Killer Application for Pairings:
Authenticated Key Establishment in Underwater Wireless Sensor Networks", Lecture Notes in
Computer Science, Vol. 5339, Springer-Verlag, 2008, pp.120?132
[Gun09] V. C. Gungor, "Industrial Wireless Sensor Networks: Challenges,Design
Principles, and Technical Approaches", Industrial Electronics, IEEE, Vol. 56(10), 2009, pp.
4258-4265
[HCB02] W. B. Heinzelman, A. Chandrakasan, and H. Balakrishnan, "An Application-
Specific Protocol Architecture for Wireless Microsensor Networks," Wireless Communication,
IEEE, Vol. 1(4), Oct. 2002, pp. 660?70.
[Hea03] S. Heath, |Embedded systems design. EDN series for design engineers (2 ed.)?.
Newnes. 2003, pp. 11?12. ISBN 9780750655460.
[FaZ12] Fan Zhang ,Security and Energy Consumption Estimation of Wireless Sensor
Network Protocols ,Department of Electronics and Computer Engineering ,University of
Limerick, Ireland
[KaS06] J. Kaps and B. Sunar, "Energy Comparison of AES and SHA-1 for Ubiquitous
[KOK09] E. Kohno, T. Ohta, and Y. Kakuda, "Secure Decentralized Data Transfer Against
Node Capture Attacks for Wireless Sensor Networks?, Proceedings of the International
Symposium on Autonomous Decentralized Systems, Athens, Greece, March, 2009. pp. 23?25.
[LAAC09] Luis Javier García Villalba *, Ana Lucila Sandoval Orozco, Alicia Triviño
Cabrera and Cláudia Jacy Barenco Abbas “Routing Protocols in Wireless Sensor Networks”

Sensors 2009, 9, 8399-8421; doi:10.3390/s91108399 www.mdpi.com/journal/sensors ISSN
1424-8220
[LKS10] J. Lee, K. Kapitanova, and S. H. Son, "The Price of Security in Wireless Sensor
Networks", Computer Networks, Vol. 5(17), ACM, Dec 2010, pp. 2967-2978
[LMP05] P. Levis, S. Madden, J. Polastre, R. Szewczyk, K. Whitehouse, A. Woo, D. Gay,
J. Hill, M.Welsh, E. Brewer, and D. Culler, "TinyOS: An Operating System for Wireless Sensor
Networks", Ambient Intelligence, Part II,Springer, 2005, pp. 115-148.
[LRC02] Lindsey, S.; Raghavendra, C.S. PEGASIS: Power-Efficient Gathering in Sensor
Information Systems. In Proceedings of the Aerospace Conference, Big Sky, MT, March, 2002;
pp. 1125–1130.
[MAD09] Manjeshwar, E.; Agrawal, D.P. TEEN: A Routing Protocol for Enhanced
Efficiency in Wireless Sensor Networks. In Proceedings of the 15th International Parallel and
Distributed Processing Symposium (IPDPS), San Francisco, CA, USA, April, 2001; pp. 2009–
2015.
[Mis10] A. M. Mishra, "Security and QoS in mobile ad hoc wireless networks", Oxford,
Wiley-Blackwell, 2010.
[MRK05] V. P. Mhatre, C. Rosenberg, D. Kofman, R. Mazumdar, and N. Shroff, "A
Minimum Cost Heterogeneous Sensor Network with a Lifetime Constraint", Mobile Computing,
Vol.4(1), IEEE,2005, pp. 4-15
[OFV07] L. B. Oliveira, A. Ferreira, M. A. Vila, H. C. Wong, M. Bern, R. D. and A. A. F.
Loureiro, "SecLEACH-On the Security of Clustered Sensor Networks", Signal Processing, Vol.
87(12), Elsevier, December 2007, pp. 2882-2895.
[PHC04] Polastre, J.; Hill, J.; Culler, D. Versatile Low Power Media Access for Wireless
Sensor Networks. In 2nd International Conference on Embedded Networked Sensor Systems
(SenSys), Baltimore, MD, USA, November, 2004.
[SZZGHS06] Shan L.; Zhang J.; Zhou, G.; Gu, L.; He, T.; Stankovic, J. A. ATPC: Adaptive
Transmission Power Control for Wireless Sensor Networks. In Proceedings of the 4th
International Conference on Embedded Networked Sensor Systems (SenSys), Bolder, CO, USA,
November, 2006.
[SLP06] Srinivasan, K.; Levis, P. RSSI is Under Appreciated. In Proceedings of the Third
Workshop on Embedded Networked Sensors (EmNets), Cambridge, MA, USA, May, 2006
[ShRa02] Shah, R.C.; Rabaey, J.M. Energy Aware Routing for Low Energy Ad Hoc Sensor
Networks. In Proceedings of the Wireless Communications and Networking Conference
(WCNC), Orlando, FL, USA, March, 2002; pp. 350–355.
[XCS06] Y. Xiao, H. Chen, B. Sun, R. Wang, S. Sethi, "MAC Security and Security
Overhead Analysis in the IEEE 802.15.4 Wireless Sensor Networks", EURASIP Journal on
Wireless Communications and Networking, Vol. 2006(2), Springer, April 2006, pp. 1-12
[XWD09] X. Xiong, D. S. Wong, and X. Deng, "TinyPairing: Computing Pairing on Sensor
Nodes with Higher Speed and Less Memory?, Proceedings of the IEEE International Symposium
on Network Computing and Applications, Cambridge, MA, July 2009.
[You04] O. Younis, "HEED: a hybrid, energy-efficient, distributed clustering approach for
ad hoc sensor networks", Mobile Computing, Vol.3 (4), IEEE, 2004, pp.366-379 Fan Zhang: On
the Security and Energy Consumption of WSN Protocols 184.

[YJD01] Y. Xu, J. Heidemann, and D. Estrin. Geography-informed energy conservation for
ad hoc routing. In Proceedings of the Seventh Annual International Conference on Mobile
Computing and Networking (MobiCom), 2001
[ZDC11] F. Zhang, R. Dojen, and T. Coffey, "Comparative performance and energy
Consumption Analysis of Different AES Implementations on a Wireless Sensor Newtork Node".
Accepted for Publication in the International Journal of Sensor Networks (IJSNET), Inderscience
(accepted 20.06.2011)
[IEE15] IEEE standard for WPAN, IEEE 802.15.4 WPAN Task group 4.
[HS07] Feedback intensity and leader-follower behavior: A mathematical description ,Ronald B.
Heady, Mark Smith Article first published online: 18 JAN 2007. Also Behavioral Science
Volume 41, Issue 1, pages 13–29, January 1996
[JOSE] Fuzzy Relation-preserving Maps and Regular Fuzzy Topological Spaces, Loki
Software, Inc. NJ.
[FU08] Stonedahl, F. and Wilensky, U. (2008). NetLogo Diffusion on a Directed Network
model. http://ccl.northwestern.edu/netlogo/models/DiffusiononaDirectedNetwork. Center for
Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex
Systems, Northwestern University, Evanston, IL.
[U999] Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/.
Center for Connected Learning and C omputer-Based Modeling, Northwestern Institute on
Complex Systems, Northwestern University, Evanston, IL.

Data Science Case Study: Wireless sensor network mathematical Models

More Related Content

What's hot

Viewers also liked

Similar to Data Science Case Study: Wireless sensor network mathematical Models

More from Sandeep Sharma IIMK Smart City,IoT,Bigdata,Cloud,BI,DW

Recently uploaded

Data Science Case Study: Wireless sensor network mathematical Models