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8/2/2019 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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