Robust and Energy Efficient Wireless Sensor Networks Routing Algorithms

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University of LeicesterDepartment of Computer Science

Robust and Energy Efficient Wireless

Sensor Networks Routing Algorithms

Author: Mohammad Hammoudeh

Student No: 059012660

Course Title: MSc Advanced Distributed Systems

Module: CO7201 Individual Project

First Supervisor: Alexander Kurz

Second Supervisor: Emilio Tuosto

Date: 29 September, 2006


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Abstract

Wireless sensor network is a new technology with a wide range of

applications such as military, environment monitoring, surveillance, etc.

Sensor networks have energy constraints, many-to-one flows, and

redundant law-rate data. There are many routing protocols proposed in

sensor networks. However, most protocols aim for achieving energy

efficiency with little or no attention for robustness and fault-tolerance. In

piece of work we consider the area of fault-tolerance in ad-hoc sensor

network routing. In particular, we regard the design and development of

robust and energy efficient routing protocols that separate between local

and large-scale traffic. A new multipath routing protocols is proposed

and simulated. This proposed protocol consider the number of hops

metric and employs a waiting time before transmitting messages to sink.


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Acknowledgments

I would like to thank all those people who made this thesis possible and

an enjoyable experience for me. First of all I wish to express my sincere

gratitude to Alexander Kurz, who guided this work and helped whenever

I was in need. A special thanks to people in the Cogent Research Group

particularly Sarah Mount.

Thank you


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Table of Content

Abstract 2Acknowledgments 3

Chapter One: Introduction and Back Ground Information 6

1.1 Sensor Networks Definition 7

1.1 Review of routing in traditional networks 9

1.1.1 Routing definition 9

1.1.2 Routing Algorithms 11

1.1.2.1 Design goals 11

1.1.3 Algorithm types 12

Chapter Two: Routing in Sensor Networks 14

2.1 Difference between sensor networks and traditional ad-hoc networks 15

2.2 Routing protocols design factors in sensor networks 16Chapter Three: A Survey on Sensor Networks Routing Protocols 22

3.1 Data-centric routing protocols 23

3.1.1 Flooding 23

3.1.2 Gossiping 24

3.1.3 Sensor Protocols for Information via Negotiation 24

3.1.4 Directed-Diffusion 26

3.1.5 Energy-Aware Routing 29

3.1.6 Rumor routing 30

3.1.7 Routing protocols with random walks 31

3.2 Gradient-based routing 31

3.2.1 CADR and IDSQ 32

3.2.2 COUGAR 33

3.2.3 ACQUIRE 34

3.3 Hierarchical protocols 35

3.3.1 LEACH 36

3.3.2 PEGASIS 38

3.3.3 TEEN 39

3.3.4 Self-organizing protocol 41

3.4 Location-based protocols 43

3.4.1 GAF 43

3.4.2 MECN 443.4.3 GEAR 46

3.5 Routing Protocols Based on Protocol Operation 47

3.5.1 Multi-path routing protocols 47

3.6 Query based routing 48

3.6.1 Negotiation based routing protocols 48

3.6.2 QoS-based routing 48

3.6.3 Sequential Assignment Routing 48

3.6.4 Maximum lifetime energy routing 49

3.6.5 Coherent and non-coherent processing 49

Chapter Four: New routing protocol 51

4.1 Description of the new routing protocol 524.1.1 Setup phase 53


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4.1.2 Data Transmission Phase 57

4.2 Data aggregation 58

4.3 Simulation 59

Conclusions and Future Work 67

References 69


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Chapter One: Introduction and Back GroundInformation

Sensor networks are wireless ad-hoc networks of a set of low-cost hosts

that are spatially distributed devices using sensors to monitor a given

phenomenon such as temperature, sound humidity, position, motion, and

others. The sensor networks are used for diverse applications areas (e.g.,

health care, agriculture, civil engineering, surveillance, and military).

Usually these devices are small in size and resources including energy,

memory, bandwidth, and computational power. Since they are

inexpensive, they are deployed in large scale within the phenomenon area

to monitor physical condition changes. Recent advancement in wireless

communications and electronics enabled the development of smaller,

cheaper, and power-efficient devices that communicate with each other

over a wireless channel. These are self organizing decentralized nodes

that communicate directly or through an arbitrary number of intermediate

nodes.


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1.1 Sensor Networks Definition

A Wireless Sensor Network (WSN) is an ad-hoc network that consists of

tiny sensor nodes with sensing, processing, and computation capabilities.

These nodes are densely deployed either inside the phenomenon or near

to it to measure ambient conditions in the sensed environment and then

transform these measurements into messages that can be communicated

through wireless links to the sink node. WSN has been recognized as one

of the most important technologies for the 21st century [3]. Due to recent

advances in wireless communications and electronics, the production of

small and inexpensive low-power multifunctional sensor nodes has

become achievable. Sensor nodes can be imagined as small special

purpose computers with sensing capabilities as input devices and with

limited computation and memory. Famous examples of sensor nodes

which are still in research stage are: Mica Mote, WINS, Smart Dust,

COTS Dust, etc. Each sensor node is typically composed of a processing

unit, memory, sensor, communication unit (usually radio transceivers or

optical in Dust nodes), and power source usually AA batteries. Figure 1

shows the block diagram for a Mica2 node [4].

Figure 1: Mica2 block diagram.

The sink node (also known as the base station) is any sensor node

connected to a standard PC interface or gateway board [4]. A sink node


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aggregate network data that can be stored or analyzed to reveal the

conditions of the observed phenomena.

Figure 2: Communication architecture of WSN

According to [5] deployment of sensor networks can be in random

fashion (e.g. dropped by airplane in the phenomena area) or regular (e.g.

manual deployment of machine monitoring sensors). Deployment ofsensors may be on ground, in the air (e.g. on board of an airplane), under

water (e.g. Columbia River estuary monitoring), in vehicles, inside

buildings, and inside human body [6]. Naturally, WSN are networks of

large number of nodes which collaborate to monitor phenomena and

report collected data over wireless links. As mentioned above, each node

has a communication unit, typically RF, to communicate with other nodesor directly to the sink node or base station. Figure 2 shows schematic

communication architecture of WSN. As Figure 2 shows, sensor nodes

are densely scattered over the sensor area where the phenomena is located

and the sensor nodes are deployed. Each node collects data via sensors

and route data either to other sensor node or to external base station.

Sensor nodes organize themselves to provide high-quality data about the

phenomena conditions, this can be done by dividing work among nodes


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to save energy by entering to sleep mode according to contribution to the

sensing coverage.

The following features enable wide and numerous applications for WSNs

[7]. A WSN has close connection with its immediate environment

without disturbance to environment, animals, plants, etc. Furthermore,

WSN is an economical method for long-term data gathering (one

deployment, much utilization) and it avoid unsafe or unwise repeated

field studies. For instance, rapid deployment, self-organization, and fault

tolerance characteristics of WSN make them of high value for military

uses including intelligence, surveillance, and targeting systems [8]. In

health, WSN also has profound effects like monitoring disabled people

and even biomedical sensors could help to help to create vision. Many

other applications exist like habitat monitoring, environmental

observation and forecasting systems (e.g. using thermal sensors to

monitor temperature in a forest), and commercial applications including

managing inventory, monitoring and controlling machines, monitoringproduct quality, safety, etc.

1.1 Review of routing in traditional networks

1.1.1 Routing definition

Routing is defined in [1] as the act of moving information across an

internetwork from a source to destination. Routing has been a hot

research issue since 1970s but it has achieved commercial popularity in

the mid 1980s with the growth of large-scale heterogeneous networks.

Routing is usually divided into two separate activities:

Determining optimal routing routes: routing protocols use routing tables

to maintain route information. Different routing algorithms maintain

different rout information such as destination and next hop that means the


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best path to a specific destination is through the next hop on the way to

the final destination. When a router receives a packet it, it checks the

destination address to see if the packet is addressed to itself otherwise it

forwards the packet to next hop. Figure 3 shows a simple routing table.

Figure 3: Destination, Cost, Next Hop routing tableRouting tables can contain other information and compare metrics to

determine the best path for a packet to travel to its destination; these

metrics differ between various algorithms. Routing protocols use

different metrics or a combination of multiple metrics called hybrid

metrics, an example of simple metrics include path length, link reliability,

delay, traffic/congestion, bandwidth, and cost. Routers maintain up to

date routing tables through the exchange of routing update messages that

consist of all or a portion of a routing table. Router can build a complete

picture of the network topology by evaluating routing updates from other

routers and through link-state advertisements.

Transporting packets through and internetwork: this is straightforward

process known as packet switching. Packet switching occurs at Layer 2

(the data link layer) of the OSI model, whereas routing occurs at Layer 3

(the network layer). Figure 4 shows a simple switching table.

Figure 4: Simple switching table


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1.1.2 Routing Algorithms

Routing algorithms can be classified into two broad classes according to

[1].

1.1.2.1Design goals

Algorithm designer goals must be implied on the operation of the

resulting routing protocol. Each routing algorithm may have one or many

design goals, these design goals include:

Optimality: is the ability to select the optimal rout to transfer message

from source to destination. The best path is calculated using weightedmetrics, for example, path length and link reliability are used by

routing algorithm where the former metric is given more weight than

the other metric.

Simplicity and low overhead: routing algorithms design must be

simple with high level of functional efficiency. Efficiency means to

do the job with minimum resource utilization and overhead.

Efficiency is with high value when routing algorithms is to run on

computers with limited physical resources such as sensor nodes.

Robustness and stability: a routing algorithm is said to be robust when

they have the ability to handle emerging or unexpected situations like

link failure and endure numerous network conditions.

Rapid convergence: convergence is part of routing table update

process. Convergence occurs when all nodes have consistent routing

tables and correct distance information, when a link or node fails, the

neighbouring nodes notice it and update their rout entries and

stimulate recalculation of optimal routes so that all routers agree on

these routes. Slow convergence has lethal consequences such as

routing loops or network outages [1, 2].


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Flexibility: refers to the ability of a routing algorithm to adapt rapidly

to different network conditions.

1.1.3 Algorithm types

Most algorithms lie in one of the following broad categories [1]:

Static versus Dynamic: static routing tables is created and maintained by

the network administrator and cant react dynamically to network changes.

Static routing algorithms best fit small and steady network environments

and they do not suit ad-hock networks. Dynamic routing the dominant

routing algorithm [1] now a days, these algorithms adjust dynamically to

changes in network conditions by exchanging routing updates that could

be sent periodically or triggered by network changes. After receiving

rout update messages, routing algorithms recalculate their entries

accordingly. Hybrid algorithms that use a combination of both static and

dynamic algorithms are also possible.

Single-Path versus Multipath: multipath routing algorithms also

known as load sharing algorithms that support one or more path to a

certain destination and use multiplexing over these paths which lead to

improved throughput/less delay and more reliability.

Flat versus Hierarchical: flat routing is simply a system where all

routers are peers. Whereas hierarchical routing systems has some

routers acting as a backbone where all packets from non-backbone

routers are forwarded towards the backbone until they arrive the last

router in the backbone which is the closest to the destination or in the

same domain as the destination. At this point autonomous routing is

used to deliver the packet to the final destination directly or through

one or more intermediary nodes. Each backbone router can only

communicate with nodes in its domain or with other routers. The

prominent advantage of hierarchical algorithm is the simplified


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organization and efficient traffic isolation within limited domains. In

addition, the router design itself could be simplified and more efficient

since it has only to know about its domain and other routers.

Intradomain versus Interdomain: Intradomain routing (e.g., Open

Shortest Path First) function within domains using routing metrics.

While, Interdeomain routing (e.g., Border Gateway Protocol) operate

between domains and is more concerned with reachability and policy.

Figure 5: Interdoamin versus Intradomain

Link-State versus Distance Vector: Link-State (shortest path first) is

the second major class of intradomain routing algorithms [2]. Link-

State algorithms operate by disseminating information about the

whole network to every node through flooding link-state packets

(LSP); these updates are small in size. Link-state protocols can

converge more rapidly and are more scalable than distance vector

protocols but require more processing and memory capabilities. In

distance vector (Bellman-Ford) algorithms, each node maintains a

vector containing distances to all other nodes and sends it only to its

neighbours. The messages send in distance vector algorithms are

large in size but sent only to direct nodes neighbours.


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2 Chapter Two: Routing in Sensor Networks

In WSNs, robust routing algorithms that improve energy and bandwidth

utilization are highly desirable. Realization of the inborn characteristics

mentioned in section one, sensor network application requires wireless ad

hoc networking techniques [8]. However, the existing protocols and

algorithms proposed for traditional wireless ad hoc networks like cellular

networks. Due to their unique features and applications, routing in WSNs

is a very challenging and difficult problem. In this chapter we study the

difference in routing protocols between traditional networks and sensor

networks. We also study the design factors of routing protocols in sensor

networks.


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2.1 Difference between sensor networks and traditional ad-

hoc networks

In the following the differences between sensor networks and ad hocnetworks are listed:

1. It is quite difficult to adopt a global identification scheme due to the

large number of sensor nodes and the high overhead cost required for

ID maintenance. In contrast to sensor networks, traditional networks

use IP address as global identification thus IP-based protocols can not

be applied to WSN. In some cases, getting the data is more important

than the sender node ID [5].

2. Unlike traditional communication networks, sensor networks mainly

use the broadcast communication paradigm typically to transmit

sensed data from multiple sources to the sink node. Whereas most ad

hoc networks are based on multicast or peer-to-peer communications.

3. Sensor nodes require careful resource management as they have

limited energy, low bandwidth radio, limited processing capabilities,

and storage.

4. Sensor nodes can be mobile which results unpredictable and frequent

topological changes. Topological changes can also be caused by

nodes failure or entering inactive state.

5. In WSNs routing is application dependent and design goals vary

among different applications. For example, in military applications

low energy is higher weighted than robustness whereas in emergency

rescue and response the opposite is true.

6. Position awareness of sensor nodes is necessary since data is collected

on location bases [5]. Sensor networks are deployed in ad hoc fashion

and they operate in unattended mode. As a result, nodes need to

discover neighbours and form connections. Many solutions were


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proposed for finding position like triangulation based algorithm

proposed in [original]. Algorithms based on triangulation

approximate their position using radio strength from few nodes who

know their position using Global Positioning Systems (GPS).

7. WSNs are data-centric networks since data is collected based on a

specific attribute or query (e.g., only nodes that sense temperature

over 40 c need to response for query of the form >40c). Since many

nodes may response with same data about the same phenomena, the

generated data will contain significant redundancy. This redundancy

could be used in positive way by routing protocols to improve energy

and bandwidth utilization or even error elimination.

8. The number of sensor nodes in a sensor network can be several orders

of magnitude higher than the nodes in an ad hoc network [8].

9. Sensor nodes are densely deployed (from tens to thousands).

10.Sensor networks are fault-prone (due to physical damage or

environmental interference) and since their on-site maintenance isinfeasible, scalable self-healing is crucial for enabling the deployment

of large-scale sensor network applications.

11.Unreliable communication channels (harsh environment or battery

depletion).

2.2 Routing protocols design factors in sensor networks

Many researchers are currently working on developing new algorithms or

protocols that take into consideration the differences between sensor

networks and ad hoc networks along with applications and architectural

requirements. Since in sensor networks topological changes are very

frequent, network routes need frequent updates as well. Maintaining up

to date routing tables is challenging process due to energy restrictions and

other factors. Most of the existing protocols focus on reducing energy


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consumption and ignore other important design factors such as fault

tolerance, scalability, etc. To reduce energy expenditure, routing

protocols proposed in the literature for sensor networks adopt existing

well known routing strategies as well as new strategies developed

specially to meet the inherent properties of sensor networks. Almost all

of the routing protocols can be broken down based on the routing

techniques as flooding, gradient, clustering, and geographic. Moreover,

these protocols can be classified according to the network structure as

flat, hierarchal, or location-based. In flooding protocols data is

broadcasted to all neighbouring nodes, while in gradient protocols the

number of hops is memorized when the data is disseminated through the

whole network [9]. In clustering protocols, cluster-heads nodes are

selected so the high energy dissipation in communicating with the sink

node is spread to all sensor nodes in the network [10]. The last category

of protocols, geographic, employs position information to deliver the data

to specific area rather than the whole network [11]. In the following wereview the most prominent factors that have bearing on routing protocols

design.

1. Fault Tolerance: Loosely speaking, fault tolerance means reliability

and availability. Sensor nodes may fail due to software errors (e.g.,

timing failure [12]) or hardware errors (e.g. physical damage, lack of

power, or environmental interference). The failure of sensor nodesshould not block the whole network, instead routing protocols must

find alternative routes to the data collection sink and sustain the

overall function without any interruption due to sensor node failures

[8]. This is known as the reliability or fault tolerance issue. Fault

tolerance requires signalling rates and dynamic transmit power

adjustments or forward packets through nodes with higher energy.


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Consequently, redundancy could be useful in a fault tolerant sensor

network.

2. Scalability: Scalability is one of the features of a good network

design. Data-link and physical-layer technologies may change quite

often but the network control plane, of which routing is the corner-

stone, must last many generations of underlying technology. Sensor

networks consist of large number of sensor nodes ranging from

hundreds to thousands depending on the application. Routing

protocols must be able to operate correctly with such large number of

nodes and exploit the density of sensor networks in saving energy,

distributing load, and fault correction. Furthermore, routing protocols

must be able to dynamically interact with environmental events.

When the phenomena conditions are stable some nodes enter into

sleep mode while other nodes keep the network functioning with high

performance. When important events occur sleeping nodes

automatically change state to active and start operating again.3. Node cost: The number of sensor nodes used to monitor a

phenomenon may reach thousands of nodes. Because of this large

number of node, the cost of a single node has to be kept low. If the

cost of the network is more expensive than deploying traditional

sensor, the sensor network is not cost justified [8].

4. Hardware constraints: Typical sensor node is made of power unit,processing unit, sensing unit, and a transceiver [13]. They also have

application specific components like power generator, mobilizer, and

location finding system. Sensing unit is composed of sensors that can

sense a variety of phenomena attributes (e.g. temperature, pressure,

etc) and ADC unit that convert analogy inputs into digital signals

that can be processed by the processing unit. The processor is

normally coupled with small memory to carry out functions that make


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sensor node collaborate with other nodes to achieve desired task. The

transceiver is the communication unit that enable sensor node to

interact with peer sensors or directly with the sink. The power unit is

also a major unit that support node with energy; some times it is also

associated with power generator (e.g. solar cell). Other application

specific components include mobilizer to handle node mobility and

location finding system to find the location of a sensor node.

5. Transmission media: In sensor networks wireless communication

medium takes many forms including radio, infrared, or optical media.

The AMPS sensor node [14] uses a Bluetooth 2.4 GHz transceiver.

In [15] a wireless sensor node uses a single channel RF transceiver

operating at 916 MHz. The Wireless Integrated Network Sensor

(WINS) [16] is based on radio links for communication. Optical

media is another important transmission media that the Smart Dust

mote [17] uses for transmission. Infrared is a license free

communication media used by sensor nodes deployed in highinterference environments. Similar to optical media, infrared require

line of sight between two communication endpoints.

6.Environment: Sensor networks operate in unattained environment

whenever they are deployed in remote areas. They may be working

under the soil, embedded in machines, under water, or in biologically

contaminated field, in a home, or building.7. Sensor network Topology: Hundreds to several thousands of nodes

are deployed throughout the sensor field [8]. They are deployed in

high density as 20 nodes/m3 [14], this high density require careful

handling of topology maintenance. In [8], topology maintenance and

change is viewed as three phases:

a. Pre-deployment and deployment phase: deployment of sensor

nodes in the sensor field can take several forms:


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i. Random deployment: place sensor in remote or

dangerous environments without any communication

lines. This could be done by dropping nodes from an

airplane, rocket, etc. This type of deployment is

common in disaster management and military

application.

ii. Manual deployment: nodes are placed one by one in the

sensor field. This can be done by human or robot. This

type of deployment is widely used in agricultural (e.g.,

planted underground) applications.

b. Post-deployment phase: after deployment, topological changes

occur due to many factors such as coverage, interference, power

management, task, etc.

c. Redeployment of additional nodes phase: to compensate for

nodes that fail due to energy reasons or physical damage, a

redeployment of new sensors may be necessary to keep networkfunctioning.

8. Power: Wireless sensor nodes are usually powered by batteries which

make sensor node life time directly dependent on battery life time.

Sensor node collects data about the environment and performs a quick

local processing on sensed data before transmission. Thus, power

consumption is distributed over the three tasks: sensing, processing,and transmission. In addition, nodes also can act as a router of

intermediate node that reroute other nodes data. As nodes fail,

possibly due to power failure, routing paths need to be updated. Due

to the aforementioned reasons, power conservation is acquiring more

importance and researchers are paying more attention on designing of

power aware protocols for sensor networks.


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9. Data Aggregation: Since sensor nodes may generate significant

redundant data, similar packets from multiple nodes can be aggregated

to reduce the number of transmissions [18]. A variety of aggregation

functions can be sued to combine data from different sources such as

duplicate suppression, minima, maxima, and average [19]. If sensor

nodes were allowed to in-network data reduction, some of the

mentioned aggregation functions can be applied fully or partially in

each sensor node [20]. According to [18], communication consumes

more energy than processing would take. For this reason, this

technique has been applied in many routing protocols to achieve

energy efficiency and data transmission [10]. Signal processing

techniques can also use aggregation. In this case its is referred to as

data fusion where node use techniques like beam-forming to combine

signals and reducing noise in these signals to produce accurate output

signal.

10.Data Delivery Method: Data delivery to the sink can be classified astime-driven, event driven, or hybrid method using a combination of

any of these categories depending on the application. The time-driven

is used in applications that transmit data periodically. In event-driven,

data transmission occurs as a response to drastic events or responds to

query generated by the sink or other node. Data delivery method has

significant impact on routing protocols in terms of energyminimization and route convergence.


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3 Chapter Three: A Survey on Sensor NetworksRouting Protocols

In this section we focus our study to the network layer of sensor networks

describing and classifying different routing protocols. A routing protocol

is said to be adaptive if it has the ability to change dynamically to adapt

to current network and node conditions such as energy available. Routing

in sensor networks can be categorized based on the network structure into

flat-based routing, hierarchical-based routing, and location-based routing.

In flat-based routing, all nodes are peers with equal roles and

functionality. In hierarchical-based routing, nodes are distributed to

different levels where each level reflect different role in the network. In

location-based routing, data routing decisions built on location

information. Additionally, sensor networks routing protocols can be

classified depending on the protocol operation into multiplepath-based,

negotiation-based, and QoS-based, or coherent-based routing.

Furthermore, routing protocols can be categorized based on how source

find a rout to destination (normally to the sink) into proactive, reactive

and hybrid. In proactive protocols, all routes are computed and stored

into routing tables before they are needed. Whereas, in reactive protocols

routes are computed on only demand. Hybrid protocols are a mixture of

both classes. Another class of routing is called cooperative routing. In

this routing class, data is sent to a central node where it can be aggregated

or undergo processing to reduce energy consumption. Many other

classifications exist such as timing or location based classes.


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3.1 Data-centric routing protocols

Nodes address is the base of routing in traditional networks. However, in

sensor networks it is not feasible to assign global identifiers to sensornodes due to the large number of nodes. This makes it very difficult to

query a set of nodes and avoid transmitting data from all nodes in the

network which is energy inefficient. Many routing protocols were

proposed to solve this problem known as data-centric protocols. In this

section we will examine a set of these protocols in details and compare

them to each other with highlight on points of strength and points of

weakness for every protocol.

3.1.1 Flooding [20]

Originally was developed for traditional networks but can be used for

routing in sensor networks. Flooding is a reactive technique that does not

require complex routing algorithms and topology maintenance; each

sensor node upon receiving a packet, data or network traffic, forward it to

all of its neighbours. This process is repeated until packet has reached its

destination or exceeded the maximum number of hops allowed. This

algorithm has a number of deficiencies listed in [21]:

Implosion: Happens when a node receive several copies of the same

packet from several neighbours. For example if a sensor node X has N

neighbours that are also neighbours of sensor node Y, then Y will receiveN copies of the same packet.

Overlap: If many nodes observing the same area, then neighbours will

receive duplicated messages about the state of the shared area.

Resource blindness: Flooding is not energy aware. An energy efficient

protocol must adapt to the amount of energy available and change

behaviour accordingly.


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3.1.2 Gossiping [20]

Gossiping is an enhanced version of flooding in which incoming packets

forwarded to only one is randomly selected neighbour rather than

broadcast to all neighbours. Each sensor node repeats the same process

until packet is delivered to its final destination. This algorithm eliminates

the implosion problem by having only one copy of the message at any

sensor node. However, this algorithm cause propagation delays due to

the long time to send the packet to all sensor nodes.

3.1.3 Sensor Protocols for Information via Negotiation (SPIN) [21]

A family of adaptive protocols designed to overcome the deficiencies of

flooding by negotiation and resource-adaptation. The basic idea to make

sensor nodes operate more efficiently and conserve energy is to transmit

meta-data, data that describes data, instead of the actual data itself since

meta-data is shorter than the actual packets. The meta-data has one-to-

one relationship with the actual data. This means two pieces f

indistinguishable data share the same meta-data and two distinguishable

data does not share the same meta-data. The meta-data should be smaller

in size that the actual data to be beneficial for SPIN. The format of the

meta-data is application specific and not specified in SPIN. For example,

sensor nodes may use their own unique IDs to present meta-data if they

cover a certain known area. The SPIN family of protocols uses meta-data

negotiation to address the problem of flooding to conserve energy by

sending meta-data instead of sending the data itself. SPIN is a three-stage

protocol where each stage uses different type of message to

communicate. It has three types of messages, which are ADV, REQ, and

DATA. When a SPIN node has data to transmit it, it broadcast an ADV

message containing meta-data of the DATA. If a neighbour receiving

and ADV is interested in the data, it sends a REQ message for the DATA


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and sensor node originating the ADV message send data to this

neighbour. If a sensor node does not respond for an ADV message the

sender implicitly understand that the node is not interested which reduce

messaging overhead. SPIN also use a random time for REQ messages to

prevent overlap. This process is repeated at each neighbour node until

every node in the entire sensor network that is interested in the data will

get a copy of the DATA message.

The SPIN family of protocols include many protocols including [21] [23]

[24]:

SPIN-1: each node use negotiation before transmitting data to

ensure that only useful data will be transferred (no duplicate

messages or overlap).

SPIN-2: threshold-based energy aware copy of SPIN-1. Also each

node has resource manager. When energy in a node approaches a

lower energy threshold, it reduces its participation in the protocol

(e.g. it only participates when it has enough energy to complete the

three stages).

SPIN-BC: broadcast channels protocol.

SPIN-PP: designed for point-to-point communication.

SPIN-EC: similar to SPIN-PP but with an energy heuristics.

SPIN-RL: similar to SPIN-PP but with adjustments to deal with

loosely channels.

SPIN was compared experimentally to flooding and gossiping in [21].

Results gave SPIN-1 performance compared to time is same as flooding

but suing 25% of energy, whereas SPIN-2 delivers 60% more data per

unit of energy than standard flooding. Both SPIN-1 and SPIN-2 performs

gossiping and come close to ideal dissemination protocol. In terms of

mode, SPIN nodes and user can be mobile or stationary and events


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the interest was received. Each node receiving the interest stores a copy

in the cash to compare the sensed data with the interest and to prevent

loops. The interest and data propagation and aggregation are decided

locally. As the interest is propagated throughout the sensor network,

gradient from the source back to the sink are set up to draw data

satisfying the query toward the requesting node. Every node that receives

the interest setup a gradient until gradients are drawn from the source

back to the sink.

The amount of information flow depends on the strength of the gradient

toward different neighbours. For example, when the interest fit gradients,

multiple pates of information flow are formed but only the best paths are

reinforced to avoid flooding and then reduce communication overhead.

The nodes ability to do data aggregation is known as a minimum Steiner

tree problem [19]. When the sink starts to receive data from the source it

periodically retransmit the original interest message with smaller interval

of time to reinforce the source node to send data more frequently on aspecific path. This is essential because interests are not reliably

transmitted. When the path from source node to sink fails an alternative

path can be found and used.

In directed-diffusion networks, nodes are application aware which help to

save energy by selecting best-paths, cashing, and processing data locally.

Cashing is the essence of directed-diffusion which increase scalabilityand robustness of sensor networks. This paradigm is well suited to

persistent queries where sink does not expect data that satisfy a query for

a certain period of time. For the aforementioned reason, it is unsuitable

for one-time queries since it will be expensive to draw gradients for one

use only. For example, directed-diffusion can not be applied to monitor

environmental conditions because it is on-demand model which is not

beneficial in such application domain. In [5] three factors that affect the


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performance of data aggregation used in directed-diffusion were pointed:

location of the source node within the network, number of sources, and

the network topology. In directed-diffusion, the energy saved with data

aggregation is spent to improve robustness with respect to sensed

phenomena dynamics. Based on the previous discussion we know that

users and sensor nodes are stationary in this paradigm and event observed

(query) is sent back to sink by unicast or multicast while interest could be

transmitted by broadcast, multicast, or unicast.

In SPIN communication is initiated at sensor nodes by advertising the

availability of data giving way for nodes interested with advertised data

to send query request. However, in directed-diffusion communication is

started by a query sent by the sink to sensor nodes by flooding some tasks

(on-demand). In the following we list some of the strength and weakness

points in directed diffusion paradigm.

Points of strength:

Scalable, since it is based on local interaction only

Latency is minimized by selecting best path among multiple

available paths

Compared to flooding and data aggregation it has less traffic which

reduce energy consumption

It is robust due to law data rate gradients and interest

retransmission

On demand data transmission

Point of weakness:

High cost of gradient setup

It is not energy aware as the best paths might be always used

Absence of mechanisms to select alternative path and interest

retransmission


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Not suitable for applications that require continuous data delivery

to sink such as environmental monitoring

Naming schemes are application dependent and require to be

specified before deployment

Comparing data and interest might add processing overhead at the

sensor nodes

3.1.5 Energy-Aware Routing

Energy-Aware Routing [25] is a destination-initiated reactive protocol

that aims to increase network lifetime. This protocol differs fromdirected-diffusion in the sense that it uses a set of sub-optimal paths to

increase network lifetime instead of using the minimum energy path

which will deplete the nodes energy. These paths are chosen and

maintained by means of energy-consumption dependent probability

function. The protocol assumes that each node is addressable through a

class-based addressing which includes the location and types of the

nodes. The protocol can pass through three phases:

i. Setup phase: The protocol initializes routing tables and discovers

routes through localized flooding. In this phase the total energy

cost in each node is calculated. Routing tables are constructed by

assigning probability to each node neighbours corresponding to the

node cost. Paths that have a very high cost are discarded.

ii. Data communication phase: Nodes use the routing tables

constructed in the previous phase to send data to destination with

probability inversely proportional to their cost.

iii. Route maintenance: Perform localized flooding as in the

initialization phase to update routes and keep all the paths alive.

In Energy-Aware Routing approach path from source to destination is

calculated in similar way to directed-diffusion and path is randomly


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selected form a list of alternatives. However, in directed-diffusion data is

sent through multiple paths and only one of them is reinforced. The use

of single path make recovering from path failure very complicated

process compared to directed diffusion. In addition, setting up the

addressing mechanism for nodes and collecting location information add

more complexity to the approach.

3.1.6 Rumor routing

Rumor routing [26] is a variation of directed-diffusion intended for

applications where geographic routing is not possible. When there is no

geographic criterion to diffuse task, directed-diffusion floods the query to

all nodes in the network. It is not always necessary to flood the network

especially when the amount of data requested from nodes is small. An

alternative approach is to flood events if number of events is small and

the number of queries is large. In this approach, only nodes that have

sensed a particular event receive the queries rather than flooding the

entire network to retrieve information about observed events.

To flood events through the network Rumor routing nodes maintain an

event table whose entries are observed events and an agent which is a

long-lived packets. Agents traverse the network to propagate information

about observed events to distant nodes. Nodes that know the route may

respond to queries by checking their event tables. Unwanted flooding can

be avoided by that way reducing costly communication overhead. Rumor

routing keeps only a single path between communication end-points as

opposed to directed-diffusion that maintains multiple paths at low rates.

Simulation results revealed that Rumor routing performance is inversely

proportional to the number of events. It has been shown that Rumor

routing is robust and can recover from failures efficiently. In addition, it

has achieved a significant energy savings compared to classical flooding.


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When the number of events becomes very large and there is no enough

interest in these events, the cost of maintaining agents and event-tables in

every node may become very high or even infeasible. Another concern in

this approach is to use parameters such as time-to-live to manage the

overhead for queries and agents. The selection of net-hop in Rumor

routing is affected by the way the route of an event agent is defined.

3.1.7 Routing protocols with random walks

Proposed in [27] for large-scale network where nodes have limited

mobility. Random-walks-based routing paradigm aims to achieve load

balancing using multipath routing in a statistical manner. Similar to

energy-aware routing, this protocol assumes that each node has a unique

identifier but without the need of location information, it is also assumed

that each node can be turned on or off at random times. Topology can be

irregular but nodes were placed at crossing point of a regular grid on a

plane. Using the distributed asynchronous version of Bellman-Ford

algorithm to calculate to the location information, the rout from source to

destination can be identified. According to a computed probability, on

intermediate node would select the neighbouring node that is closer to the

destination as the next hop. The load balancing is achieved by carefully

calculating this probability. In conclusion this protocol is simple as nodes

need to maintain little state information and different routes are selected

between the same pair of source and destination of different times.

However, the drawback of this algorithm is the topology; network

topology may be impractical.

3.2 Gradient-based routing

Gradient-based routing (GBR) is another variant of directed-diffusion

proposed by Schurgers et al. [9]. The basic idea is to memorize thenumber of hops when interest is flooded through the network. Hence,


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each node can calculate the minimum number of hops to reach the sink.

This parameter is called the height of the node. The gradient on a

specific link between two neighbouring nodes is calculated as the

difference between a nodes height and that of its neighbour. A packet is

forwarded of a link with the largest gradient. GBR uses some auxiliary

techniques to uniformly divide the traffic over the network; such

techniques include data aggregation and traffic spreading. When a node

fall in multiple paths, acts as a relay node, it can create data combining

according to a certain function. On the other hand, three different data

spreading techniques have been presented:

1. Stochastic-based: when there are two or more next hops with equal

gradients the node chooses one among them randomly.

2. An energy-based scheme: when energy approaches a certain

threshold, a node increases its height to discourage other sensor

nodes from sending data to that node.

3. A stream-based scheme: divert new streams away from nodes thatare currently part of the path of other streams.

Data spreading contribute to achieve balanced distribution of the traffic in

the network which is the main objective of this algorithm. Also the

discussed techniques for traffic load balancing and data fusion are also

applicable to other routing protocols for enhanced performance.

Simulations results of GBR have shown outperform directed-diffusion interms of total communication energy.

3.2.1 CADR and IDSQ

Two routing techniques were proposed in [28]: Constrained anisotropic

diffusion routing (CADR) and information-driven sensor querying

(IDSQ). CADR aims to be a general form of directed-diffusion. The

main idea is to query nodes and route packets such that information gain


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is maximized while latency and bandwidth are minimized. CARD

generate queries using certain criteria to select sensor that can get the data

through acting only the sensors that are near event of concern and

dynamically adjusting data routes. The difference from directed-

diffusion is the consideration of information gain in addition to the

communication cost. In CADR, each node evaluates on information/cost

gradient and end-user requirements. Estimation theory was used to

model information utility technique. However, IDSQ does not

specifically define how the query and information are routed but it gives a

mechanism of choosing the best order of sensors for maximum

incremental information gain. Therefore, IDSQ can be viewed as a

complementary optimization procedure. Simulation results confirmed

that these techniques are more efficient than directed-diffusion where

queries are diffused in an isotropic fashion and reach closest neighbours

first.

3.2.2 COUGAR

Proposed in [29] where the network is viewed as a huge distributed

database system. The basic idea is to abstract query processing through

using declarative queries. In addition, COUGAR utilizes in-network data

aggregation to achieve more energy savings. A new abstraction layer was

added between the network and application layers to provide network-

layer independent method for data query. The architecture proposed in

COUGAR has a node called the leader that performs aggregation and

transmit the data to the sink. The leader is selected by other sensor nodes

in the distributed database system. The sink generates a query plan that

specifies the necessary information about data flow, in-network

computation for incoming query and sends it to the relevant nodes, and

describes how to select a leader for the query. This architecture provides


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in-network computation ability that can provide energy efficiently when

the generated data is large. Nevertheless, COUGAR has three prominent

drawbacks:

1. The addition of new layer, query layer, at each sensor node adds

extra energy consumption and storage overhead.

2. In-network data computation requires synchronization among

nodes prior sending any data to leader node.

3. Leader nodes require dynamic maintenance mechanism to prevent

depleting these nodes.

3.2.3 ACQUIRE

Is a brand new idea proposed by Sadagopan et al. in [30]. Active Query

forwarding In sensoR nEtworks views the network as a distributed

database where complex queries can be divided into sub-quires. The

operation mechanism can be described as follows: The query is

generated by the sink and each node receiving the query tries to respond

to the query partially using its pre-cashed information then forward it to

other sensors. The nodes whose cash is not up-to-date gather information

from their neighbours within a look-ahead of d hops. Once the query is

being resolved completely, it is sent back through either the reverse or

shortest path to the sink. Thus ACQUIRE deal with complex queries by

allowing many nodes to send response. Other data-centric protocols such

as directed-diffusion uses flooding-based query mechanism for

continuous and aggregate queries which make them impractical for

complex queries due to energy constraints. ACQUIRE algorithm provide

efficient querying by adjusting the value of the look-ahead parameter d.

Note that when d is equal to the network diameter, ACQUIRE behaves

like standard flooding and when d is very small then the query has to

travel more nodes.


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A mathematical modelling has been used to derive the optimal value of

the look-ahead d for a grid setup of sensors where each node has four

immediate neighbours. However, there is no validation results by

simulation and the reception cost have not taken into consideration during

modelling.

To select the next node for forwarding the query was addressed in CADR

[28] and Rumor Routing [26]. In CADR, query nodes use IDSQ

mechanism to determine which node can provide most useful information

by using estimation theory. Rumor routing tries to forward query to node

which knows the path to the searched event. In [30], the next hop is

either chosen randomly or based on maximum potential of query

satisfaction.

3.3 Hierarchical protocols

Also called cluster-based protocols, is a two tires protocol known for their

scalability and efficient communication. Originally was developed for

traditional wired-networks but latter was utilized to perform energy-

efficient routing in sensor networks. A single tire network can cause

congestion at the gateway especially with high sensors density. This

leads to communication delays and inadequate tracking of events in

addition to limited scalability. To overcome these problems without

degrading the service, network clustering has been proposed in some

routing approaches. In hierarchical architectures clusters are created and

special tasks are assigned to cluster-heads, this must be done with

intensive care because it has a great impact on the overall system

scalability, network lifetime, and energy consumption. In hierarchical

protocols high-energy nodes can be used to process and send information

while low-energy nodes can perform sensing. Hierarchical routing is

two-tire routing where one tire is used to elect cluster-heads and the other


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for routing. Cluster-heads reduce energy consumption by performing

data aggregation and fusion which decrease messaging towards the sink.

In this section we will review hierarchical routing protocols.

3.3.1 LEACH

Low Energy Adaptive Clustering Hierarchy (LEACH) [10] is one of the

first hierarchal routing algorithms for sensor networks. LEACH

randomly select sensor nodes as cluster-heads and rotates this role to

distribute energy load among the sensors since using the same node will

deplete its energy. The cluster heads aggregates data received from nodes

to reduce the number of messages sent to the sink. This approach is well-

suited for applications where constant monitoring is needed in which data

periodic collection is centralized. This will save energy as only cluster-

head nodes transmits to the sink rather that all sensor nodes. Based on

simulations the optimal number of cluster heads is estimated to be five

percent of the total number of nodes.

The operation of LEACH is split into two phases, the setup phase and the

steady state phase. In order to minimize overhead the duration of steady

phase is longer than the setup phase. During the setup phase, the clusters

are created and cluster heads are selected. This selection is made by the

node choosing a random number between zero and one. The sensor node

is a cluster-head if this random number is less than the threshold T(n)

calculated as the following:

Where P is the desired percentage to become a cluster head, r is the

current round, and G is the set of nodes that have been selected as a

cluster head in the last 1/P rounds. After cluster-heads are chosen they

broadcast an advertisement to the entire network that they are the new

T(n)=P/[1-P*(rmod(1/P))] if n G

0 otherwise


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cluster-heads. Every node receiving the advertisement decides to which

cluster they want to belong depending on the signal strength. The sensor

node sends a message to register with cluster-head of their choice. The

cluster-head based on a TDMA approach assigns each node registered in

its cluster a time slot when it can send data.

During the sensing phase cluster nodes can start sensing and transmitting

data to the cluster-heads. All the data processing such as data fusion and

aggregation are local to the cluster. After a certain period of time spent

on the steady phase, the network enters the setup phase again and start

anew round of selecting cluster-heads.

LEACH is able to increase the network lifetime. It achieves over a factor

of seven reduction in energy dissipation compared to direct

communication and a factor of four to eight compared to the minimum

transmission energy routing protocol. LEACH has a number of

drawbacks listed in [5]:

1. Assumes that all nodes can transmit with enough power to reachthe sink.

2. It is not applicable to networks deployed in large regions.

3. Dynamic clustering brings extra overhead, e.g. head changes,

advertisements, etc., which may diminish the gain in energy

consumption.

4. Assumes that all nodes begin with the same amount of energy anda cluster-head consumes approximately the same amount of

energy.

5. Assigns time slot to each node even it node has no data to transmit.

SPIN LEACH Directed-diffusion

Optimal route No No Yes

Network lifetime Good Very good Good

Resource awareness Yes Yes Yes

Use of meta-data Yes No YesTable 1: Comparison between SPIN, LEACH, and Directed-diffusion


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Leach with negotiation is an extension of LEACH proposed in [10]. It

use meta-data as in SPIN prior data transfer to ensure that only interesting

data is transmitted to head-clusters before being transmitted to the sink.Table 2.1 taken from [5] shows a small comparison between SPIN,

LEACH, and directed-diffusion.

3.3.2 PEGASIS

Power-Efficient GAthering in Sensor Information Systems an

enhancement over the LEACH protocol was proposed in [31]. As

opposed to LEACH, PEGASIS has no clusters, instead it creates chains

from sensor nodes so that each node communicate only with their closest

neighbours and only one node is selected from the chain to communicate

with the sink. When the round of all nodes communicating with the sink

ends, a new round starts and so on. This allows distributing energy

consumption uniformly among all nodes. PEGASIS forms near optimal

chins in a greedy way. The use of collaborative techniques increases

node and network lifetime in addition it reduce the communication

bandwidth by local coordination between close nodes.

PEGASIS nodes use signal strength to measure the distance to

neighbouring nodes. Each node aggregate data to be sent to the sink by

any node in the chain and the nodes in the chain will take turns sending to

the sink. Simulation results in [31] showed that PEGASIS outperformedLEACH about 100 to 300% for different network sizes and topologies.

This performance is achieved through the use of data aggregation and the

reduction of overhead brought by cluster formation. In order to rout its

data, nodes need to know about the energy level of its neighbours which

requires dynamic adjustments of PEGASIS topology. Such topological

adjustments, especially in high utilized networks, may introduce

significant overhead. Another important drawback is the absence of


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methods by which nodes determine its location in a network; it is

assumed that all nodes maintain a complete database of the location of all

other nodes in the network. Moreover, this protocol assumes that each

sensor node can communicate with the sink directly and does not outline

multi-hop communication to reach the sink. In addition, PEGASIS

assumes that all nodes start with the same level of energy and

consumption rates are equal. Also the single head of the chain can

become a bottleneck and distant nodes may suffer from excessive delays.

Finally, in terms of modelling, this approach also assumes that nodes are

stationary.

An extension to PEGASIS is Hierarchical-PEGASIS proposed in [32] to

decrease delay incurred for packets during transmission to the sink and

suggests energy X delay metric to solve data gathering problem.

Simultaneous data messages are utilized to reduce delay. Two

approaches were studied to avoid possible collisions and signal

interference: signal coding and spatial transmission. In the latter one onlyspatially separated modes are allowed to transmit at the same time. This

chain-based protocol with CDMA capable nodes, constructs a chin of

nodes that forms a tree like hierarchy and each selected node in a

particular level transmits data to the node in the upper level of the

hierarchy. This method ensures data transmitting in parallel and reduces

the delay significantly. Since nodes are not aware of their neighbour'senergy levels, Hierarchical-PEGASIS still require dynamic topological

adjustment. Even though, they have performed better than standard

PEGASIS by a factor of about 60.

3.3.3 TEEN

Threshold-Sensitive Energy Efficient Protocols [33] were proposed for

time-critical application in which network operate in a reactive mode.


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TEEN utilizes a hierarchical approach along with data-centric

mechanism. It is very suitable for situations where the environment is

sensed continuously but data transmission is done less frequently. The

basic idea behind this approach is the grouping of closer nodes into

clusters and this process goes on the second level until the sink is

reached. Figure 6 redrawn from [33] shows TEEN networks architecture.

Figure 6: Hierarchical Clustering in TEEN & APTEEN

Cluster-heads broadcasts to its members a head threshold (which is the

minimum possible value of the sensed attribute to be sent to cluster-head)

and a soft threshold (which is a small change in the value of the sensed

attribute that triggers the node to switch on its transmitter and transmit).

Thus, the hard threshold tries to reduce the number of transmissions by

enforcing nodes to transmit only when sensed attribute is in the range of

interest. While the soft threshold reduces the numbers of transmissions

by blocking all messages about little or no change in the sensed attribute.

As a result, the user can control the trade off between energy efficiency

and data accuracy; smaller value of the soft threshold gives more accurate


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picture of the environment but consumes more energy on the other hand.

Note that the user can change both threshold values as required but this is

impractical for applications where periodic reports are needed, since the

broadcast message may be lost consequently nodes will never

communicate.

The Adaptive Threshold sensitive Energy Efficient sensor Network

protocol (APTEEN) [34] is an enhancement version of TEEN that

changes the periodicity or threshold value used in the TEEN protocol.

The architecture is the same as TEEN but here the cluster-head broadcast

four parameters:

Attributes: set of physical parameters that user is interested with

Thresholds: hard threshold and soft threshold

Schedule: a TDMA schedule, assigning time slot to each node to

transmit

Count time: maximum time between two successive reports sent

by a node

Once a node sense a change in some attribute value equal to or greater

than the soft threshold or sense a value greater than the hard threshold it

transmits data to cluster-head. Despite the improvements over TEEN,

APTEEN has added additional complexity to implement new parameters

(Attribute, Count Time, and Schedule). On the other hand, it offers more

flexibility and combines proactive and reactive techniques. Simulationresults have shown that both TEEN and APTEEN has outperformed

LEACH [10]. In terms of energy dissipation and network lifetime, TEEN

gives the best results while APTEEN is between LEACH and TEEN.

3.3.4 Self-organizing protocol

Subramanian et al. [35] describes not only a self-organizing protocol, but

also application taxonomy. The proposed taxonomy was used to build


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architecture and infrastructure components to support heterogeneous

sensors. Some sensors, could be mobile or stationary, probe the

environment and transmit messages to a designated set of stationary

nodes that act as routers. Each node should be reachable by a router that

forms the backbone for transmitting data to more powerful sink nodes.

Also each sensing node was assumed to have unique identifier and they

can be identified by the address of the router node to which they are

connected. The routing architecture is hierarchical where groups of

nodes are formed and merge when needed. Fault tolerance is achieved by

using Local Markov Loops (LML) algorithm in broadcast trees. In this

approach router nodes keep the entire sensor connected by forming a

dominating set. The phase of self-organizing router nodes and initializing

routing tables are:

Discovery phase: discover neighbouring nodes

Organization phase: groups are formed and merged where each

node is identified by address or router node it is connected to.

Routing tables and broadcast tree and built

Maintenance phase: routing tables update messages are exchanged

and broadcast trees are maintained by LML

Self-reorganization phase: when node or partition failure occurs

Since the sensor nodes can be addressed individually in the routing

architecture, this approach is suitable for applications wherecommunication to a particular node is required. Moreover, the cost for

maintaining routing tables and keeping a balanced routing hierarchy is

minimized which is one of the several strength points in this approach.

As for broadcasting a message is less than that consumed in SPIN

protocol [22]. However, this is not on-demand protocol which adds

additional overhead in the organization phase of the algorithm.

Furthermore, in case of many cuts in the network the hierarchy forming


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will be every expensive because networks cuts increase the probability of

applying reorganization phase.

3.4 Location-based protocols

In this category of protocols nodes are addressed by their location. The

distance between nodes is measured on the basis of incoming signal

strengths and used to estimate energy consumption. By estimating

signals strengths, nodes can calculate relative coordinates of

neighbouring nodes that can be utilized in routing data efficiently

especially in the absence of standard addressing scheme[36].

Furthermore, nodes equipped with a low power GPS receiver can obtain

their location directly by communicating with a satellite [37]. The

location information could be used to efficiently diffuse quires to a

certain region of a sensor network rather than flooding the entire

networks. To save more energy, some location based schemes demand

that nodes should go to sleep if there is no activity. Some protocols that

was originally developed for mobile ad hoc networks are also applicable

to sensor networks while others like Cartesian and trajectory-based

routing [38] [39] are not. In this section we review the most prominent

energy aware location-based protocols.

3.4.1 GAF

Geographic Adaptive Fidelity (GAF) [37] is an energy-aware location-based routing algorithm developed for mobile ad hoc networks but can be

used in sensor networks. It can also be considered as a hierarchical

protocol where the clusters are based on geographic location. The

network area is divided into fixed zones to form a virtual grid. Each node

associate itself with a zone in the grid based on its GPS-indicated

location. Nodes within the same zone collaborate to save energy byturning off unnecessary nodes without affecting the level of routing


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fidelity. Nodes that are located with the same zone on the grid are

considered equivalent in terms of packet routing. Thus, GAF can

substantially increase the lifetime as the number of nodes increase. GAF

nodes can be in one of three states: discovery, for determining the

neighbouring nodes in the grid, active reflect participation in routing and

sleep when radio is turned off. Mobile nodes estimates its leaving time of

grid and forward it to its neighbours so that sleeping neighbours adjust

their sleeping time accordingly to keep high level of routing fidelity.

Before the leaving time of the active nodes expires, sleeping nodes

wakeup and one of them becomes active. In [37] the fixed zones are

chosen to be square and equal. GAF strives to keep the network

connected by keeping representative nodes always in active node for each

region on its virtual grid. Simulation results have shown that GAF

performs as well as a normal ad hoc routing protocol. It was proved that

GAF saved energy which result an increase of network lifetime and

decrease in delays and packet rates.

3.4.2 MECN

Small Minimum Energy Communication Network (MECN) was

proposed in [40], it use low power GPS to compute an-energy efficient

sub-networks. The main objective of MECN is to form a sub-network

such that the number of nodes and the transmission power is minimized.

This allows finding global minimum power paths without considering the

entire network. This is achieved by utilizing a localized search for each

node considering its relay region. MECN identifies a relay region for

every node. The relay region consists of nodes in a surrounding area

where transmitting through those nodes is more energy efficient than

direct transmission. The relay region for node pair (1, r) is depicted in

Figure 7 redrawn from [41].


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Figure 7: Relay region of transmit-relay node pair (i, r) in MECN.

The enclosure of a node I is then created by the union of all nodes

reachable by i. the protocol can be viewed as two stages:

1. Construct the enclosure graph which contains globally optimal

links in terms of energy consumptions. This phase requires local

computations in nodes.

2. Identify optimal links on the enclosure graph using distributed

Belman-Ford shortest path algorithm with power consumption as

the cost metric.

MECN support fault tolerance because they are self-reconfiguring and

thus can dynamically adapt to nodes failure or deploying/ leaving sensors.

The small minimum energy communication network (SMECN) [40] is an

extension to MECN which consider obstacles between any pair of nodes.

In MECN, it is assumed that every node can transmit to every other node

which is not possible every time. But the network is assumed to be fully

connected as in MECN. In terms of the number of edges, the sub-

network constructed by SMECN is smaller than the one constructed by

MECN. As a result, the sub-network constructed by MECN is smaller

than that constructed by SMCN if the broadcast region is circular around

the broadcasting node for a given power setting. The energy

consumption needed to transmit data from a node to all its neighbours in


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SMECN sub-graph is less than that needed in MECN sub-graph.

Moreover, in SMECN maintenance cost is less than that in MECN since

the former makes it more likely that the path used requires less energy

consumption. Finally, a sub-network with smaller number of edges

introduces more overhead in the algorithm.

3.4.3 GEAR

Geographic Energy Aware Routing (GEAR) [42] exploit the fact that data

queries often contain geographic attributes to send queries only to a

particular area which reduce the number of messages significantly which

can conserve more energy. GEAR uses energy aware and geographically

informed neighbour selection heuristics to route a packet to destination

region. In GEAR environments, nodes keep and estimated cost (a

combination of residual energy and distance to destination) and a learning

cost of reaching the destination through its neighbours (a refinement of

the estimated cost that accounts for routing around holes in the network).

When a node does not have any closer neighbour to the target region than

itself, hole occurs. In the absence of any holes, the estimated cost is equal

to learned cost. The route setup for next packet is adjusted by

propagating the learned cost on hop back every time a packet reaches the

target. There are two phases in the algorithm:

1. Forwarding packets towards the target region: When a node has

data to send it checks its neighbours to find closer neighbour to the

target region than itself. In case of more than one node, the nearest

neighbour to the target region is selected as next hop. When there

is a hole, one of the neighbours is selected to forward the packet

based on the learning cost function.

2. Forwarding the packets within the region: As soon as the packet

reaches the target region, it can be diffused in that region by either


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recursive geographic forwarding or restricted flooding. The latter

is more energy efficient in high density networks.

When compared to GPSR [43] which is a geographic routing protocol

that uses planar graphs to solve the holes problem. GEAR was found to

reduce energy consumption for route setup and perform better than GPSR

in terms of packet delivery. The simulation results have also revealed

that for an uneven traffic distribution, GEAR delivers 70% to 80% more

than (GPSR). For uniform traffic pairs GEAR delivers 25% - 35% more

packets than GPSR.

3.5 Routing Protocols Based on Protocol Operation

In this section, we review routing protocols based on routing

functionality. Some of the protocols may fall under one or more of the

above routing categories.

3.5.1 Multi-path routing protocols

In the following we review briefly a set of routing protocols that use

multiple paths to enhance the network performance and support fault

tolerance. The fault tolerance (resilience) of a protocol is measured by

the likelihood that an alternate path exists between a source and a

destination when the primary path fails. This increase energy

consumption and traffic generation. The following table summarize some

of these protocols.In [36], the path whose nodes have the largest residual energy is used to route packets.

The energy consumption is distributed over node by switching to the backup path

when the primary path energy falls.

In [44], based on the energy consumption of each path, sub-optimal paths are chosen

to increase network lifetime.

In [45], a trade off between minimizing the total power consumed and the residual

energy of the network was achieved. In some cases, it is very expensive to use the

path with larges residual energy.

In [46], the trade off between the amount of traffic and network reliability in studied

using redundancy function that is dependent on the multi-degree and on failingprobabilities of the available paths. This algorithm operates as follows: instead of


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sending multiple copies of the same packet on multiple paths, the packet is divided

into sub-packets that can be transmitted over available paths. The receiving nodes

reconstructs the original message even with some sub-packets was lost.

[47] Is a robust multi-path routing algorithm based on directed-diffusion to keep the

cost of maintaining the multi-paths low. The main idea is to compare the costs of

alternative paths to primary path because they tend to be much closer to the primarypath.

Table 2: Multi-path routing protocols

3.6 Query based routing

In these protocols, the destination nodes generate a query for data that

describes a specific sensing task to sensing nodes. Usually queries are

written in high level language similar to natural language. Only nodesthat receive the query and have data which matches the query respond by

sending their data. Directed-diffusion and rumor routing, discussed

earlier, are examples of this type of routing.

3.6.1 Negotiation based routing protocols

These set of protocols use high level data descriptions to eliminate

redundant data transmissions through negotiation. Communication

decisions are taken based on available resources. SPIN family protocols

discussed earlier is an example of such protocols.

3.6.2 QoS-based routing

QoS protocols consider delay, energy, bandwidth, etc. when delivering

data to destination. These protocols keep a trade off between energy

consumption and data quality.

3.6.3 Sequential Assignment Routing (SAR) [48]

Is a table-driven multi-path protocol that aims to achieve energy

efficiency and fault tolerance. SAR crates a tree rooted at the source

node to destination nodes to crate multiple paths from a source node.

Routing decisions in SAR is based on three metrics: energy resources,

QoS on each path, and the priority level of each packet. Periodic re-


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computation of paths is initiated by the root of the tree, also any

topological changes, such as nodes failure, triggers path re-computation.

Failure recovery is achieved by keeping consistency between up streams

and down streams nodes on each path. When the number of nodes is

large, the protocol suffers from the overhead of maintaining the tables

and states at each sensor node. Simulation results showed that SAR

consumes less energy than the minimum energy metric algorithm.

3.6.4 Maximum lifetime energy routing

Proposed in [49] and based on a network flow approach to increase the

network lifetime. In this approach routing decisions is based on node

remaining energy and the required transmission energy using specific

link. This protocol tries to find traffic distribution to maximize the

network life lifetime. Two maximum residual energy path algorithms are

studied to find out the best link metric for the stated maximization

problem. The two nodes residual energy. The used link costs are:

where eij is the energy consumed during transmission of packets over

link i-j, and Ei is the residual energy at node i. The least cost paths to the

destination are found using Bellman-Ford shortest path algorithm. When

compared to Minimum transmitted energy (MTE) [5], it was found that

the proposed maximum residual energy path approach has better averagelifetime than MIE for both link and cost models.

3.6.5 Coherent and non-coherent processing

Loosely speaking, most protocols can be categorized, based on data

processing techniques incorporated, into coherent and non-coherent

protocols [48]. In coherent routing, nodes perform minimum processing

before transmission to aggregators (nodes that perform further data


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processing). The minimum processing includes time stamping, duplicate

suppression, etc. Coherent routing is typically used in energy sensitive

applications. Whereas non-coherent data processing routing, nodes

process raw data locally before transmission for other nodes for further

processing. In non-coherent processing has three phases:

1. Target detection, data collection, and pre-processing

2. Membership declaration, node declare to all neighbours its

participation in a cooperative function

3. Central nodes election, this node must have enough energy to

perform more sophisticated data processing

In [48], a single and multiple winner algorithms where proposed for non-

coherent and coherent data processing respectively. The single winner

algorithm (SWE) aims to build a minimum-hop spanning tree that covers

the entire network. SWE nodes elect a single aggregator node based on

the energy levels and computational capacity to perform complex data

processing. While in multi-path winner algorithm (MWE), an extensionto SWE, each node keep a record for the best n central aggregator nodes

to limit the number of sources that can send data to the central aggregator

node which consumes a large amount of energy. At the end of the MWE

process, each sensor has a set of the minimum energy paths to each

source node. After that, the SWE is used to find the node that results the

minimum energy consumption. This complex operation of MWE addsoverhead, longer delays and limited scalability unlike that for non-

coherent processing networks.


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4 Chapter Four: New routing protocol

In this chapter we propose a new multi-path hierarchical routing protocol

that is a self-organized cluster-based protocol appropriate for situations

where constant monitoring by the sensor network is a need. As in

LEACH (see p. 35), nodes are often designated into logical groups called

clusters where each cluster is managed by a cluster head through which

nodes can communicate with the sink. Every node must belong to some

cluster to participate in the protocol and every node can belong to only

one cluster. The entire cluster is viewed by the sink and other cluster in

the network as a single virtual node. In this protocol robustness is

achieved by storing multipath and electing backup node(s) that can

substitute the cluster-head in some failures. It also improves network

performance and reliability by localizing network traffic. Finally, the

protocol is simulated and performance is measured and evaluated.


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4.1 Description of the new routing protocol

The main objective of this protocol is to provide energy-efficient and

robust communication. The energy efficiency is achieved by load sharingat two levels:

Network level: cluster-head node perform traffic multiplexing

over multiple paths

Cluster level: rotation of the cluster head role every given interval

of time

This will prevent ene

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Robust and Energy Efficient Wireless Sensor Networks Routing Algorithms