Wireless Sensor Networks: Issues, Challenges and Survey of Solutions

foamyflumpMobile - Wireless

Nov 21, 2013 (5 years and 3 months ago)


Wireless Sensor Networks: Issues, Challenges and Survey of Solutions

Mani Potnuru, Phanindra Ganti

Department of Computer Science

University of Illinois Urbana
Champaign,Urbana,IL 61801.



Wireless ad
hoc sensor networ
ks have recently emerged as a premier research topic. They have great
term economic potential, ability to transform our lives and pose many new system
challenges. Sensor networks pose a number of new conceptual and optimization problems such
location, deployment and tracking in that many applications rely on them for needed information.

The past works are scattered across all of the systems layers: from physical layer to data link layer to
network and application layer. In this report, we p
resent an overview of wireless sensor networks and
issues involved in employing them. We make an attempt to provide a snapshot of solutions proposed in
recently published literature for different issues like Medium Access Control, Data Dissemination,
ity and Coverage determination.

Sensor Networks, Wireless Sensor Networks, Distributed Sensor Networks, WINS,

1 Introduction

In the foreseeable future sensor networks have wide applicability from Observing scientific
phenomenon t
o use in agricultural monitors and warehouse inventory management.
In order to
understand these scientific phenomenon, it is necessary for researchers to collect numerous measurements
of a scientific event in a geographic region. While these measurements c
an be obtained at a distance
(remote sensing), there is often no substitute for observations made firsthand within the region of interest
situ). One form of technology that can accomplish such in
situ science is the Wireless Sensor Network
(WSN). In WS
Ns a number of probe devices are distributed throughout a geographic region to observe
local scientific conditions. In addition to sensors, probes are equipped with computational resources for
network data processing, as well as wireless transceivers fo
r communication with neighboring probes.
Recent advances in integrated circuitry, microelectromechanical systems (MEMS), communication and
cost, low
power design have fomented the emergence of these wireless sensors.

Most current deployed sensor netw
orks involve relatively small numbers of sensors, wired to a central
processing unit where all of the signal processing is performed [4]. In contrast, this survey focuses on
distributed, wireless, sensor networks

in which the signal processing is distribut
ed along with the
sensing [5].

Why distributed sensing?

When the precise location of a signal of interest is unknown in a monitored
region, distributed sensing allows one to place the sensors closer to the phenomena being monitored
than if only a single

sensor was used. Line of sight, and more generally obstructions, cannot be
addressed by deploying one sensor regardless of its sensitivity. Thus, distributed sensing provides
robustness to environmental obstacles.

Why wireless?

When wired networking of di
stributed sensors can be easily achieved, it is often the
more advantageous approach. Moreover, when nodes can be wired to renewable (relatively infinite)
energy sources, this too greatly simplifies the system design and operation. However, in many
ned applications, the environment being monitored does not have installed infrastructure for
either communications or energy, and therefore untethered nodes must rely on local, finite, and
relatively small energy sources, as well as wireless communication

Why distributed processing?
Finally, although sensors are distributed to be close to the phenomena,
one might still consider an architecture in which sensor outputs could be communicated back to a
central processing unit. However, in the context
of untethered nodes, the finite energy budget is a
primary design constraint. Communications is a key energy consumer as the radio signal power in
sensor networks drops off as r
[6] due to ground reflections from short antenna heights. Therefore, one

to process data as much as possible inside the network to reduce the number of bits transmitted,
particularly over longer distances.

2 How are Sensor Networks different from other kinds of Networks?

Sensor Network is similar to a general purpose Mobil
e Ad
Hoc network (MANET) in many aspects, they
are distributed, self
organized, multi
hopped and lack a fixed infrastructure. The main difference lies in
the fact that the former basically has lower cost, lesser bandwidth, smaller processing power, higher
redundancy and are more power
constrained. While MANET is a general structure with mobility as its
main feature, the sensors have no or low mobility. Another special MANET is the Bluetooth technology
[20] with cable replacement as its goal, also shares som
e features with sensornets. However, here the
power constraint is not so strict, the processing power is much higher and the target applications are quite
different. The main aim of any sensornet is to spatially densely and temporally continuously monitor
gather data, thus often forming many
one correlated traffic pattern from the sensors to the collection
station. But the aim of Bluetooth, similar to general MANET, is to provide one
one independent

3 Motivating Applications

orical Example: Oceanography

Buoy networks are being used in oceanic environment evaluation to collect and monitor physical
parameters, like sea temperature, wave direction, current speed and so on. Many real
time, climactical
buoy networks have been imple
mented around the world [7] to successfully to capture such information.

Modern Applications:

In all fires, early warnings are critical in trying to prevent small harmless brush fires from becoming
monstrous infernos. By deploying specialized wireless sen
sor nodes in strategically selected high
areas the detection time can be drastically reduced, increasing the likelihood of success in
extinguishing efforts [10].

Wireless sensor networks provide a viable alternative to several existing applications. L
arge buildings
contain hundreds of environmental sensors that are wired to a central air conditioning and ventilation
system. The significant wiring costs limit the complexity of current environmental controls and
reconfigurability of these systems. Replac
ing the hard wired monitoring units with wireless sensor
nodes can improve the quality and energy efficiency of the environmental system while allowing
almost unlimited reconfiguration and customization [10].

One field where these sensor
nets will be used
is large scale environmental monitoring (air, water, soil
chemistry). The goal is to enable scattering of hundreds of thousands of these nodes in areas that are
difficult to access for study using conventional methods. The network could then monitor events
perform local computations on the data, and either, relay aggregated data, or configure local and
global actuators.

Biomedical sensor applications like Artificial Retina, Glucose Level Monitors, Cancer Detectors,
General Health Monitors [40].

Smart Kinde
rgarten: The envisioned system would enhance the education process by providing a
childhood learning environment that is individualized to each child, adapts to the context, coordinates
activities of multiple children, and allows continuous unobtrusive eva
luation of the learning process by
the teacher [41].

Habitat Monitoring: Long
term data
collection for systematic and ecological field studies [42].

Commercial Applications: Agriculture Monitors, Warehouse Inventory, Product Maintenance, Smart
spaces, Fact
ory Instrumentation.

NASA Applications:

NASA uses sensor networks primarily in In
situ data collection, Precision landing guidance,
Vehicle health sensors, Trail markers and exploration of distant regions such as surface of Mars.

4 Engineering Challen

Most envisioned sensor network applications encounter the following challenges [5]:

for energy and communication requiring maximal focus on energy efficiency.

Ad hoc deployment
, requiring that the system identifies and copes with the result
ing distribution and
connectivity of nodes.

environmental conditions requiring the system to adapt over time to changing connectivity
and system stimuli.

Unattended operation
requiring configuration and reconfiguration be automatic (self

To address these technical challenges several strategies are going to be key building
blocks/techniques for sensor networks:

Collaborative signal processing among nodes that have experienced a common stimulus will greatly
enhance the efficiency (in
formation per bit transmitted)

Exploiting redundancy has application when the cost of deploying the initial set of sensors is much less
compared to the cost of replacing defective or failed nodes or renewing node resources. Thus
redundancy can be exploited

to extend system lifetime. Another application is when sensors cannot be
positioned carefully; redundancy can be exploited to extend coverage by using a subset of the nodes,
which are positioned favorably.

Adaptive fidelity signal processing can be exploi
ted to strike a balance between energy, accuracy and
rapidity of results. The timeliness and accuracy of the signal processing can be adapted keeping in
mind the energy resources and latency requirements.

Hierarchical, tired architecture can greatly contri
bute to overall system lifetime and capability.
Whenever possible, higher capacity system elements can be used to offload drain on small factor
elements, while the latter can be exploited to obtain the desired physical proximity to stimuli.
Moreover, even
among elements with homogeneous capabilities, creating clusters and assigning
special combining functions to cluster heads can contribute to overall system scalability and lifetime.
However, to ensure robustness, such clustering/hierarchy must be self
figuring and reconfiguring
in the face of environmental or network changes.

5 Sensor Node Architecture

In sensor networks, the architecture of a node is highly dependent on the purpose of the deployment. But
a generalized architecture can be shown as
in Figure 1 [57]. Each node consists of a sensor, processor,
and radio for communication, battery, and memory. According to their operational, we can divide its
functionality into two broad categories: Heavy
duty part, includes sensor and data converter an
d signal
processing, has to operate at low power level, using in real
time system. The low
duty part with extra
energy performs further processing and communication.

Figure 2 [58] shows the general layered architecture of a sensor node. In this proposed
rchitecture the network functionality is divided between main CPU and radio board. The main idea
behind this architecture is to decrease the functionality on the sensor CPU by transferring some of the
functionalities to the radio board. The radio boards pr
ocess the information in the form of Micro
Controller Units (MCU), which are used for the physical and MAC layer implementation. Thus part of
the network functionality is transferred to the radio board as shown in Figure 3.

Figure 1: The Architecture of

a Sensor Node

Several institutions have begun large
scale projects to develop system and protocol architectures
for wireless sensor networks. The projects include:

: Adaptive Wireless Arrays for Interactive, Reconnaissance, Surveillance,

and Target
Acquisition in Small Unit Operation (UCLA/Rockwell Science Center) [61,62].

: Wireless Integrated Network Sensors (UCLA/ Rockwell Science Center) [63,64].

: Scalable Coordination Architectures for Deeply Distributed Systems (USC/ISI)

Smart Dust
: Autonomous Sensing and Communication in a Cubic Millimeter (U.C.Berkeley) [59,60]
(see Figure 4 for the Berkeley Mote).

: Micro
Adaptive Multi
domain Power
aware Sensors (MIT) [66].


J.Hill. et.al [67] propose a event driven
operating system to reduce the burden of application
development by providing convenient abstractions of physical devices and highly tuned
implementations of common functions while providing efficient modularity and robustness.
The TinyOS is designed to fi
ll the role of the software platform to support and connect the
tiny wireless sensor nodes where the current real time embedded operating systems are
unsuitable [68]. It fits in 178 bytes of memory and supports the concurrency intensive
operations required

by networked sensors (hence the choice of event based model) with
minimal hardware requirements. TinyOS supports an application level
messaging model, a
variant of Active Messages (AM) called
Tiny Active Messages
[69]. This concept strives for

of communication and computation and is well suited for the event based
execution model of TinyOS. The low overhead associated with event
based notification is
complementary to the limited resources of the networked sensors.


Link Layer Issues

The tw
o major services the link layer provides to higher layers are formation of link
layer topology (or
infrastructure) and regulation of channel access among the nodes. Like in all shared
medium networks,
medium access control (MAC) is important for successful

operation of network. Current MAC design for
wireless networks can be broadly divided into two categories: contention based and explicit organization
in time/frequency/code domains [21]. The various flavors of MACA, MACAW, 802.11 are examples of
the forme
r. These contention based schemes are clearly not suitable for sensor networks due to their
requirement for radio transceivers to monitor the channel at all times. This is particularly expensive
operation for the low radio ranges of interest for sensor ne
tworks, where transmission and reception have
almost have the same energy cost. One would like to turn off the radios when no information is to be sent
or received. The other class of MAC protocols is based on reservation and scheduling. The task of
ment of channels (i.e., TDMA slots, FDMA frequency bands or CDMA spread spectrum codes) to
links between radio neighbors avoiding the collisions is a hard problem and needs a hierarchical structure
to make the channel assignment task more manageable. The p
roblem in this approach is how to determine
cluster memberships and cluster heads such that the entire network is covered [20,45]. Moreover, when
the number of nodes within a cluster changes, it is not easy for a TDMA protocol to dynamically change
its fra
me length and time slot assignment. So its scalability is not as good as that of a contention
protocol. For example, Bluetooth [20] may have at most 8 nodes in a cluster.

Relevant issues in designing a good MAC protocol for the wireless sensor netwo

: This is the foremost important factor for any issue in the sensornets.

: A good MAC protocol easily accommodates changes in network size, density and topology.
Some nodes may die over time and new nodes may join later
; some nodes may move to different

: In traditional wireless voice or data networks, each user desires equal opportunity and time to
access the medium i.e., sending and receiving packets for their own applications. Per
hop MAC level
irness is this an important issue. However, in sensor networks, all nodes cooperate for a single task and
normally there is only one application running at any time. In this case fairness is not important as long as
level performance is not deg


Latency can be important or unimportant depending on what application is running and the
node state. During a period when there is no sensing event, there is normally very little data flowing in the
network and most of the time nodes are
in idle state. Sub
second latency is not important, and we can
trade it off for energy savings by letting the node turnoff their radios to reduce the energy consumption
due to idle listening.

Thus energy conservation and self
configuration are primary goa
ls for sensor networks, while
other attributes like fairness, latency, throughput and bandwidth utilization are only of secondary concern

While the sensors are mostly stationary, mobile nodes are usually introduced in a sensor network
to serve as ga
teway to the outside world. Thus MAC solutions can be classified into kinds. One category
caters to the communication between the mobile nodes and the stationary sensors and the other category
caters to communication between stationary nodes.


bi and Pottie [21] proposed this Self
organizing Medium Access Control for Sensor networks
(SMACS). This distributed protocol enables nodes to discover their neighbors and build a network for
communication without any master nodes. It builds a flat topolog
y i.e., there are no clusters or cluster
heads. Each node maintains a TDMA
like frame, called
super frame,
in which the node schedules
different time slots to communicate with its known neighbors. The structure of this frame can change
from time to time.
The TDMA schedule consists of two separate regions. The first region is called the
period, when nodes randomly search on a fixed frequency band for new nodes to include in the
network or to rebuild severed links. The other region is reserved for com
munication tasks with
neighboring nodes. At each time slot a node talks to only one neighbor. To avoid interference between
adjacent links, the protocol assigns different channels, i.e., FDMA or CDMA, to potentially interfering
links. Although the super fr
ame structure is similar to a TDMA frame, it doesn't prevent two interfering
nodes from accessing the medium at the same time. The actual multiple access is achieved by FDMA or

One interesting feature of Piconet Radio Protocol [48] is that it also p
uts nodes into periodic sleep
for energy conservation. The scheme that piconet uses to synchronize neighboring nodes is to let a node
broadcast its node is before its starts listening. If a node wants to talk to a neighboring node, it must wait
until it re
ceives the neighbor's broadcast.

Woo and Culler [47] propose a contention based medium access control scheme with the goal to
be energy efficient and fair bandwidth allocation to the infrastructure for all nodes in the multihop
network. They conclude that

limiting the length of listening, the introduction of random delay in addition
to backoff, and phase shift at the application level are necessary for the traditional CSMA mechanism.
They claim that the proposed
adaptive rate control mechanism

is effective

in achieving their fairness goal
while being energy efficient for both low and high duty cycle of network traffic. But in the context of
sensor networks fairness is only of secondary importance and it can even be traded
off for further energy

ong and Shah et.al. [50] propose a distributed access mechanism combining best of CSMA and
spread spectrum techniques. It trades the bandwidth in broadband applications for higher power efficiency
and throughput. This access protocol does not require a ded
icated control channel, or synchronization,
whether global or local. Additionally, it has very low delay and does not have the problem of coordinating
broadcast and scheduled unicasts.

MAC (Sensor MAC)

The S
MAC [46] is designed with the primary goals
of energy conservation, collision avoidance and self
configuration. It utilizes a combined scheduling and contention scheme. The Protocol tries to reduce
energy consumption from all sources causing energy waste i.e., idle listening, collision, overhearing
control overhead. S
MAC uses three novel techniques to overcome these factors. To reduce energy
consumption in listening to idle channel, nodes periodically sleep. Neighboring nodes form
to auto
synchronize on sleep schedules. Similar
to PAMAS [49], S
MAC also sets the radio to
sleep during transmissions of other nodes. Unlike PAMAS, it only uses in
channel signaling. Finally, S
MAC applies
message passing
to reduce contention latency for sensor network applications that require
forward processing as data moves through the network. The basic idea is to divide the long
message into small fragments and transmit them in a burst. The result is that a node who has more data to
send get more access time to the medium. This is unfair
from a per
hop MAC level perspective. But this
method can achieve energy savings by reducing control overhead and avoiding overhearing. The most
important feature of wireless sensor networks, in
network data processing requires store

of mechanism. In this case, MAC protocols that promote fragment
level fairness actually
increase message
level latency for the application. In contrast, message passing reduces message
latency by trading off the fragment
level fairness.

6.3 Eaves
drop and Register (EAR)

This mobile MAC protocol [21] is designed to provide the required connectivity to mobile nodes as they
interact with the static sensor network, while adhering to the constraints for the entire network. Since it is
desirable to setup

connection with as few message exchanges as possible, the mobile node assumes full
responsibility of connection setup. The mobile node keeps a registry of all the sensors in its neighborhood
and makes handoff decisions whenever the SNR drops below a thres
hold value. The EAR algorithm is
designed to be transparent to the protocol followed by the stationary nodes. The first slot following the
bootup period is reserved for mobile nodes thus giving them higher priority. The EAR algorithm uses the
invitation me
ssage broadcast during the bootup period as a trigger. The mobile node simply eavesdrops
on to these messages and forms a registry of all stationary nodes within hearing range.



Perhaps the most directly relevant to sensor networks is the ongoing

work on ad
hoc wireless networks. A
central focus of the work in the ad
hoc wireless networking has been the design of proactive routing
protocols like Ad Hoc On Demand Distance Vector (AODV), Destination Sequenced Distance Vector
(DSDV), Temporally Order
ed Routing Algorithm (TORA) [12][13][14] and reactive [15] routing
protocols, and combination there of. Proactive routing protocols continuously compute nodes to all nodes
so that a route is already available when a packet needs to be sent to a node. Such
continuous route
computation is highly energy inefficient. Reactive routing protocols on the other hand start route
discovery process only when a packet needs to be sent. However, these protocols may redundantly flood
requests throughout the network. A com
bination of proactive and reactive routing protocols may
overcome the above
mentioned disadvantages, but still wont be as energy efficient as schemes that can
exploit the application specific knowledge to route data and queries. Another aspect of sensor ne
that makes these multi
hop ah
hoc protocols not appropriate is the low mobility or lack of it. In these ad
hoc protocols QoS is important and has to route large amounts of multi
media traffic in the presence of
high mobility.

Sensor Protocols for
Information via Negotiation (SPIN)

Kaulik et.al. [22] proposed this family of adaptive protocols to efficiently disseminate information among
sensors in an energy
constrained sensor network. SPIN uses metadata negotiation and resource
adaptation to overc
ome several deficiencies of traditional dissemination approaches. Using metadata
names, nodes negotiate with each other about the data they possess. This negotiation ensures that nodes
only transmit data when necessary and never waste energy on useless tra
nsmissions. Because the nodes
are resource aware, they are able to cut back on their activities whenever their resources are low to
increase their longevity. SPIN uses three kinds of messages for communication:


When a node has data to send it ad
vertises using this message containing metadata.


A node sends this message when it wishes to receive some actual data.


Data message containing the data with a metadata header.

The four specific SPIN protocols are:

: This poi
point communication protocol assumes that two nodes can communicate with
each other without interfering with other nodes communication and also that packets are never lost. A
node, which has information to send, advertises this by sending an ADV to n
eighboring nodes. The nodes
that are interested in the data express their interest by sending a REQ. The originator of ADV then sends
the data to the nodes that sent a REQ.

: This protocol just adds energy heuristic to the previous protocol. A nod
e participates in the
process only if it can complete all the stages in the protocol without going below a energy threshold.

This broadcast channel based protocol differs from the previous protocols in that nodes do
not immediately send out REQ
messages on hearing an ADV. Instead each node waits a random amount
of time before sending out the REQ message. The other nodes whose timers have not yet expired cancel
their timers on hearing the REQ, thus preventing redundant copies of REQ being sent.


This protocol was designed for lossy broadcast channels by incorporating two adjustments.
First each node keeps track of the advertisements it receives and re
requests data if a response from the
requested node is not received within a specified t
ime interval. Second nodes limit the frequency with
which they will send data. Every node waits for predetermined time before servicing requests for same
piece of data.

Directed Diffusion

D.Estrin et.al. [16] propose a communication model where each sens
or node

data that it generates
with one or more attributes similar to the metadata concept of the SPIN suite. A sink may query for
information by disseminating an
Syntactically, an interest is simply a range of values for one or
more attrib
utes. An example for such a scenario can be a sensor network in which each node can detect
motion (and possibly some other information) within some vicinity. One or more sink nodes may query
the sensor network for motion information from a particular secti
on of the terrain (e.g., from the southeast
quadrant). Moreover, because of the relatively short life span of nodes, as well as the large amount of data
and system redundancy possible, it may be more useful to address data instead of individual nodes [17].

Each sensor may name the data it generates using a single attribute
which has a geographic
location (e.g. latitude/longitude or relative location with respect to some landmark) as its value.

data may be described using several attributes: (
type: seismic, id =12, location = 75N/120E) [1]. Interests
may be of the form (type = seismic, location = 70
140E). Each node disseminates interests
based on the contents of the interest. In the above motion detection example, nodes send the intere
towards the neighbor in the direction of
east quadrant
. Conceptually, the path of interest
propagation sets up a reverse data path for data that matches the interest (query). In the above diffusion
model this data propagation is said to have an as
. The notion of gradient is useful for
robustness when each intermediate node propagates interest towards multiple neighbors. The authors call
that the strength of the interest is different towards different neighbors, resulting in source
sink paths
with different gradients. In its simplest form gradient can be a scalar. Negative gradients are also possible,
which inhibit the distribution of data along a particular path and positive gradients encourage the
propagation of data along the p
ath. The value of the gradient can have application specific semantics. In
the aforementioned motion detection example, if a node has two outgoing paths, one with a gradient of
0.7 and other with a gradient of 0.3, then the node may send twice as much deta
il along the higher
gradient path than along the lower.

The diffusion model allows intermediate nodes to cache or locally transform (e.g. aggregate) data.
Caching and aggregation can increase the efficiency, robustness and scalability of coordination. Loc
cached data can be accessed by other sinks with much lower energy consumption. The diffusion model's
data naming and local data transmission features capture the data
centricity and application specificity
that is possible in sensor networks.

nsky and D.Estrin in [17] further analyze the method of routing queries to nodes that have
observed a particular event. This follows the philosophy of retrieval of

data keyed on the event, not on the
underlying network addressing scheme. They define an

as an abstraction, identifying anything from
a set of sensor readings, to the node's processing capabilities. Similarly a

is defined as a request for
information. If the amount of returning data from a query is significant makes sense in discoveri
ng short
paths from the sources to the sink. Methods such as Directed Diffusion need an initial flood of query for
exploration. GEAR (Geography and Energy Aware Routing) [18] relies on localization information of the
nodes and provides savings over a compl
ete network flood by limiting the flooding to a geographical

Flooding need not be restricted to queries. For applications where there are few events and many
queries, it makes sense to flood the event, and set up gradients towards the query. Howev
er, unless the
number of queries per event and the amount of data conveyed by each event is quite high, the setup cost
for event flooding cannot be effectively amortized. GRAdient broadcast (GRAB) [19] contributes in this
direction of event centric routing

state in the network. GRAB describes a way of building a cost field
toward a particular node, and then reliably routing queries across a limited size mesh toward that node. It
comes with the overhead of a network flood to set up the cost field, but querie
s are routed along an
interleaved set of short paths, and can thus be delivered cheaply and reliably.

Rumor Routing

The rumor routing [17] is intended to strike a balance between query flooding and event flooding. It is
only useful if the number of queri
es compared to the number of events is between the two intersection
points. An application aware of this ratio can use a combination of rumor routing and flooding to best
utilize available power (see Figure 4).

Figure 4

The idea here is to create path
s leading to each event; whereas event flooding creates a network
gradient field [19]. In this way when a query is generated it can be sent on a random walk until it finds the
event path; instead of flooding it across the network. As soon as the quer
y discovers the event path, it can
be routed directly to the event (see Figure 5). If the path can't be found, the application can retry and can
as a last resort flood the query. This makes sense, since two straight lines (although neither the path nor

query is entirely straight) in a plane are very likely to intersect. The algorithm employs a set of long
lived agents that create paths (in the form of state in nodes) directed towards the events they encounter.
Whenever an agent crosses a path to an even
t it has not yet seen, it adapts its behavior and creates path
state that leads to the event.

Figure 5

Figure 6

In Figure 6, agent A1 has been creating path state leading to event E1. Agent A2 has been creating path
state leading to E2. When A2 cro
sses the path created by A1, it begins to create aggregate path state,
leading to both E1 and E2. The agents can also optimize the paths in the network if they find shorter ones.
When an agent finds a node whose route to an event is more costly than its ow
n, it will update the node's
routing table to the more efficient path (see Figure 7).

Figure 7

Adaptive Local Routing for Cooperative Signal Processing

It is clear that some layering of distributed signal processing and networking functions is necessar
y for
energy efficiency. Since the communications dominate the energy cost when cooperative functions
among nodes are needed, the question that arises is to what extent the signal processing hierarchy
demands a corresponding networking hierarchy. K.Sohabri

et.al. presented algorithms for setting up sub
networks to perform cooperative signal processing [21]. The two types of cooperative signal processing
are non
coherent and coherent. For non
coherent processing, raw data is
preprocessed at the node itself
before forwarding it to a central node (CN) for further processing. For coherent processing, raw sensor
data is forwarded after minimal processing to the central node. Because of fairly low data traffic load for
coherent processing energy efficient alg
orithmic techniques assume importance. On the other hand,
since coherent processing generates long data streams, energy efficiency must be achieved by path

coherent Cooperative Function

Three phases are involved in this algorithm: Phase
I involves target detection, data collection, and
preprocessing. After preprocessing if a node finds that its information might be of interest, it declares its
intention to participate the cooperative function (Phase II). In Phase III, a CN is elected for
sophisticated information processing by taking into account the energy reserves and computational
capabilities of nodes. The CN election algorithm has two components: Single Winner Election (SWE) and
Spanning Tree (ST) algorithm. The first component i
nvolves the necessary signaling in single candidate
election. The second component computes a minimum
spanning tree rooted at the CN. An
is broadcasted by every node willing to be a CN along with a set of parameters that serve as the election

criteria. The nodes that receive these messages then compare the criteria with themselves and respond
with a second set of messaged with the result and store the winner in their registry. The routing
information is piggybacked along with the

e thus allowing the calculation of a minimum
spanning tree rooted at the winner simultaneously. Thus the winner's information diffuses through the
network and the spanning tree gradually increases its coverage and finally covers the whole network.

t Cooperative Function

Since the energy cost of uploading long data stream to the CN is high, a Multiple Winner Election
(MWE), which is a simple extension of SWE, is used to limit the number of sensor source node (SNs)
providing the data. Instead of keep
ing record of one best candidate, each node will now keep up to n of
them. Just as in SWE, for each winning SN candidate, a minimum energy path can be computed. After
computing the total energy consumption to upload data from each SN to each node, with the

use of SWE
process, the node with minimum energy consumption is used to act as the CN. In general this formation
process has longer delay, higher overload, and lower scalability than the non
coherent processing.

Location Mechanisms

The problem of locali
zation, that is, determining where a given sensor is physically located in a network,
is a challenging one, and yet extremely crucial for many of the envisioned applications of sensornets. For
example, localization opens up new ways of reducing power consu
mption in multihop wireless networks.
GEAR(Geography and Energy Aware Routing) uses location information to achieve power savings in
routing. In context aware applications, localization enables intelligent selection of appropriate devices,
and also support
s useful coordination among devices. The desired granularity of localization is
application dependent [44].

Global Positioning System (GPS) [51] solves the problem of localization in outdoor
environments for PC
class nodes. However for large networks of
small, cheap, low
power devices like
sensor networks, practical considerations such as size, form factor, cost and power constraints preclude
the use of GPS on all nodes.

Some of the design goals of localization in wireless sensor networks are [44]:

Normally, the sensors have some kind of short
range radio transceivers for
communication. By leveraging this radio for localization the high cost and size requirements of
GPS can be avoided.

For greater scalability, the responsibility
for localization must lie with the receiving
node that needs to be localized and not with the reference points.

Ad Hoc:
For easy deployment, the solution should not require preplanning or extensive

Low Energy:
Since the sensors have modest
processing capabilities, the mechanisms should
minimize computation and message costs to reduce power consumption.

Adaptive Fidelity:
The accuracy of the localization algorithms should be adaptive to the granularity
of available reference points.

tion methods typically rely on some form of communication between reference points with
known positions and the receiver node that needs to be located. Various localization techniques can be
classified into two broad categories based on the granularity of
information inferred during the
grained localization systems (e.g., GPS)
provide high precision location
information, typically estimated ranges or angles relative to beacons (reference points) and compute
location of the unknown node u
sing trilateration (position estimation from distance to three points) or
triangulation (position estimation from angles to three points).
grained localization systems
estimate unknown node location from proximity to beacons or landmarks [52].

erty et.al [53] proposed a coarse
grained localization system based on RF
induced constraints. Known peer
peer communication in the network is modeled as a set of geometric
constraints on the node position. As a physical example, if a parti
cular RF system can transmit 20m and
two nodes are in communication, their separation must be less than 20m. These constraints restrict the
feasible set of unknown node positions. Formally, the network is a graph with

nodes at the vertices
(each node hav
ing a Cartesian position) and with bi
directional communication constraints as the edges.
Positions of the first
nodes are known (

and the remaining
positions are unknown.
The feasibility problem is then to find (
... x
, y

uch that the proximity constraints are satisfied.
There will be constraints among open nodes though their positions are unknown. Connections that are not
reported are not detrimental to the performance of this algorithm. To calculate the feasible solutions

to the
position estimation problem convex optimization is used. This methodology requires centralized
computation i.e., all nodes must communicate their communicate their connectivity information to a
single computer to solve the optimization problem. Thi
s solution doesn't scale well for networks of the
order of 1000s of nodes since the problem becomes too computationally intensive to be handles at one
place. And also the communication cost increasers with the number of sensors. A decentralized approach,
here large network is divided into sub networks and position estimation can be carried out for each
member of the network based on unknown centroid of the local region. Following these local estimations,
the sub network centroids can be abstracted to nodes

in the larger network and placed accordingly with
another iteration of position estimation.

Bulusu et.al. [44] propose a GPS
less localization methodology suitable for outdoor environments
using RF
connectivity. Multiple nodes in the network with overlap
ping regions of coverage serve as
reference points. They are situated at known positions (these nodes can be capable of running GPS) (X1,

), that form a regular mesh and transmit periodic beacon signals (period = T) containing their
positions. Sensors listen for a period

t >> T to evaluate connectivity. If the percentage of messages received from a beacon in a time interval
exceeds a threshold
that beacon is considered connected. When the beacon placement is
uniform, the
centroid of the positions of all connected beacons is a feasible solution in the region of
connectivity overlap. For non
uniform placement, a feasible solution can be found using the convex
optimization techniques used in the previous method [53]. This coa
rse grained, decentralized protocol
doesn't require coordination among reference points or sensor nodes. It is therefore potentially scalable to
very large networks.

Niculescu and Nath [54] propose APS (Ad hoc Positioning System), a method to extend the
apabilities of GPS to non
GPS enabled nodes in a hop
hop fashion in an ad hoc network. Positioning
is based on a hybrid method combining distance vector like propagation and GPS triangulation to
estimate location in presence of signal strength measureme
nt errors. This mechanism applied same
principle as GPS with the difference that the landmarks are contacted in a hop by hop fashion rather than
directly. This method is similar to the distance vector routing, in the sense that at any time each node
nicates with its immediate neighbors and in each message exchange it communicates its available
estimates to landmarks acquired so far. APS is distributed, doesn't require special infrastructure or setup,
provides global coordinates and requires recomputat
ion only for moving nodes. Actual locations obtained
by APS are on average less than one radio hop from true location.

8.1 Beacon Placement

Beacon nodes, which know their position and serve as a reference, are a vital aspect of almost all
n systems. Beacon placement strongly affects the quality of localization. Each node may need
to hear from a certain minimum number of beacons to be able to localize itself, and the beacon nodes
heard must be non
linear. Fixed beacon placement strategies su
ch as uniform and very dense placement
are not always viable, energy efficient and will be inadequate in very noisy environments in which sensor
networks are expected to work [43]. It is virtually impossible to preconfigure to the terrain and

uncertainties and compute an ideal (or even a satisfying) beacon placement that uniformly
achieves a desired quality of localization across the region. So the beacon placement needs to adapt to the
noisy and unpredictable environmental conditions. The ap
proach taken by Bulsu et.al. [44] is based on
measurement based adaptation
i.e., improving the quality of localization by adjusting beacon placement
or adding a few beacons rather than by completely redeploying all beacons. By measurement based, the
s mean the deployment of additional beacons is influenced by the measurements of the operating
localization systems rather than by careful off
line analysis of a complete system model. The authors
propose three approaches to adaptive beacon placement. For
sparse beacon densities, the algorithms
augment the existing beacon infrastructure by adding new beacons at empirically

determined points,
based on (i) terrain exploration and measurements made by a mobile robot [44] (ii)
: A distributed,

(heap based) approach in which beacons exchange information about their neighboring
beacons. Based on this information beacons evaluate a suitable point in their immediate neighborhood for
adding a beacon. Eventually, the candidate points selected by beac
ons must be sent to a central control
site, which must decide from amongst various candidate points where to deploy new beacon nodes [55].
For dense beacon deployment, [52] propose
(Selectivey TuRning Off Beacons), a localized
algorithm that rotate
s functionality amongst beacons to reduce interference among the densely placed
beacons, allows adaptation to noisy environments as well as extend the system lifetime. In unattended
sensor networks, where new sensors cannot be physically deployed as needed
, one could begin with a
very dense initial beacon deployment for redundancy. Each beacon determines its role during a given time
interval based on coordination with its neighbors rather than from an assignment by a central server.

Range Estimation

deals with range estimation, a critical requirement for fine
grained localization. While many
mechanisms (e.g., RF, IR, Acoustic) for range estimation exist, any individual mode of sensing can be
blocked or confused by the environment. Girod and Estrin [56
] suggest the use of

channel to detect and eliminate these measurements.

This approach is based on the following three


For every sensory system there exists a set of environmental conditions that will confuse it and a subset
of those in which it fails to identify that it is confused.

Some sensory modalities are "orthogonal" to each other i.e., their sets of failure conditions are largely
disjoint. (e.g., acoustic information and the information from a video camera).


modalities can identify each others failure modes and thus improve the data quality through
coordination and communication with significantly less effort relative to the effort required to
incrementally improve the sensors on their own.

e.g., Consider a
system composed of many standalone ranging units. Acoustic ranging performance
suffers when the "line of sight" path is obstructed. Longer deflected paths lead to unbound error. It is very
difficult to identify these errors based exclusively on analysis of

acoustic data. Now if we use a camera,
where each camera's filed of view contains several ranging units, which might be identified by a
characteristic pattern strobed on an IR LED. Any ranging unit that the camera can see has a high
probability of LOS to
the camera and thus in those cases an accurate range can be determined with

Coverage and Exposure

One of the fundamental issues that arise in sensornets in addition to location management, deployment,
and power management is coverage. Coverage

can be considered as the measure of Quality of Service
offered by a sensor network. For example, in the application of the fire detection sensors, one may ask
how well the network can observe a given area and what are the chances that a fire starting in a

location will be detected in a given time frame. Coverage formulations identify weak areas and this is
helpful in future deployment and reconfiguration schemes to improve the QoS. In most sensor networks,
two seemingly contradictory,yet related v
iewpoints exist: worst and best coverage. In the worst case
coverage QoS is quantified by finding areas of lower observability from sensor nodes and detecting
breach regions. In the best coverage case, QoS is specified by finding areas of high observabilit
y from
sensors and identifying the best support and guidance regions [10].

S. Meguerdichian et.al propose an optimal polynomial time algorithm combining computational
geometry techniques (Voronoi diagram and Delauny triangulation) with graph theoretical a
techniques (graph search algorithm). The breach (support) path problem (MB (S) PP) can be stated as:

Given a field A instrumented with sensors S where the location of each sensor is (x
)and the
areas I,F correspond to initial and final locat
ions of an agent,

he goal is to identify P
the path of
maximal breach of surveillance in S, starting in I and ending in F. A high
level description of the
proposed algorithm is as follows:

Generate Voronoi diagram of S

Construct a weighted undirected gr
aph G, where the vertex set is V(from Voronoi diagram), and
each edge corresponds to each line segment in the Voronoi diagram The weight is assigned as
the minimum distance from the closest sensor to this edge.

Find P

using binary search and Breadth

Here P

is chosen such that its closest distance to any of the sensors is as large as possible. For the
maximal support path the the farthest distance from the closest sensors is minimized. In this paper the
authors assume identical sensor sensit
ivities where the coverage depends only on the geometrical
distances from sensors. In practice, other factors influence coverage such as obstacles, environmental
conditions, and noise. In addition to non
homogeneous sensors, other possible sensor models ca
n deal
with non
isotropic sensor sensitivities, where sensors have different sensitivities in different directions.
The integration of multiple sensors such as seismic, acoustic, optical etc. in one network platform and the
study of the overall coverage of

the system present several interesting challenges.

S. Meguerdichian et.al in [11] deal with another important problem in sensor networks: exposure.
Exposure is directly related to coverage in that it is a measure of how well an object, moving on an
rary path, can be observed by the sensor network over a period of time. Exposure can be informally
specified as expected average ability of observing a target in the sensor field. More formal definition
given by the authors is an integral of a sensing func
tion that generally depends on a path from a starting
point P

to destination point P
. Due to diminishing effects of noise bursts in measurements, sensing
ability can improve as allotted sensing time (exposure) increases. To find the
minimal exposure path

sensor networks under arbitrary sensor and intensity models is an extremely complex optimization
problem. The paper presents the following generic algorithm:

Transform the continuous problem domain into a discrete on using grid
based approach.

Apply gr
aph theoretic abstractions and convert the grid into an edge
weighted graph.

Compute the minimal

exposure path
using Dijkstra's single source shortest path algorithm.

The minimal exposure path provides valuable information about the worst
case exposure
ed coverage.
The algorithm works for arbitrary sensing and intensity models and provides an unbounded level of
accuracy as a function of runtime.

Time Synchronization

Time synchronization is an important aspect of the distributed wireless sensor networks

but often have
unique constraints in the scope, lifetime and precision of the synchronization required as well as time and
energy that can be expended to achieve it. Different applications like beam
forming array, data
aggregation, recognition of duplicat
e detection of same event from different sensors, ordering of logged
events have different synchronization requirements and also any single synchronization mechanism is not
appropriate for all circumstances sensors should have multiple methods available to

them so that they can
dynamically trade precision for energy, or scope for convergence time. Existing time synchronization
methods like NTP conserve use of bandwidth and try to keep the clock synchronization at all times but
are not aware of the stringent

energy constraints and the heterogeneity of the hardware that may be
deployed in sensornets.

J.Elson and D.Estrin in [8] propose a
facto synchronization

where clocks are normally
unsynchronized. When a stimulus arrives, each node records the time o
f the stimulus with respect to its
own local clock. Immediately afterwards, a third party node (a beacon) broadcasts a synchronization
pulse. Nodes that receive this pulse use it as an instantaneous time reference and normalize their stimulus
timestamp wit
h respect to that reference. This method is limited by the transmit range of the beacon and
creates only an "instant" of synchronized time. This method is inappropriate for an application that needs
to communicate a timestamps over long distances or times.

However it provides enough service for beam
forming applications, localization systems and other situations in which one need to compare the relative
arrival times of a signal at a set of spatially local detectors. The receiver clock skew (clocks don't ru
n at
exactly same rates) variable delays in receivers (all receivers don't detect the signal at the same event) and
propagation delay of the synchronization pulse affect the precision achievable by this method. NTP can
be used to discipline the frequency o
f each node's oscillator.

Many network synchronization methods including the one described use a design where a server
periodically sends a message containing its clock value to a client. J.Elson et.al propose a scheme
Broadcast Synchronization

or RBS

[9] that sync
hronizes a set of receivers with one another"
as opposed to the traditional model of synchronization of sender with receiver [2,3]. In this scheme nodes
periodically send beacon messages to their neighbors using the network's physical
ayer broadcast.
Recipients use the message's arrival time as a point of reference for comparing their clocks. The message
contains no explicit timestamp, nor is it important exactly when it is sent. The most significant limitation
of RBS is that it can't b
e used in networks that employ point
point links since it requires a network with a
physical broadcast channel
However it is applicable to a wide range of applications in both wired and
wireless networks where a broadcast domain exists and higher precisio
n synchronization is required that
the 500
sec bound that NTP can typically provide in a LAN. To come over this limitation th

paper also proposes a multihop scheme where it dynamically constructs a "time route" through a series of
nodes which all
ows locally coordinated timescales to be federated into a global timescale, across
broadcast domains with little loss in accuracy.

Lifetime Estimation

[24] explore the fundamental question concerning the limits of energy efficiency of sensor networks

hat is the upper bound on the lifetime of a sensor network that collects data from a specified region
using a certain number of energy
constrained nodes? The answer to this question allows one to calibrate
the performance of collaborative strategies and pr
otocols being proposed regularly. The exposure of
lifetime's dependence on factors like source behavior, source region, basestation location, number of
sensors, available initial energy, radio path characteristics allows one to see what factors have most
mpact on lifetime and consequently where engineering effort is best expended.

The authors measure the bounded network lifetime as the cumulative active time to the first loss
of coverage. They state the Lifetime Bound Problem (LBP) as below: Given the re
gion of observation(R),
the source radius of observability (d
), the node energy parameters (

and n), the number of nodes

(N), the initial energy in each sensor (E), what is the upper bound on the active life time (L) of
any network establish
ed using these nodes which gather data from a source residing in R with spatial
location behavior l
(x,y). In the model, they have assumed that the sensor nodes are static, while the
target moves around according to some location distribution. This is

a reasonable assumption in most of
the applications.

First the energy consumed for point
point communication is established as follows. Given a
transmitter A and a receiver B separated by D meters, intermediate nodes between A and B are introduced
as rel
ay nodes to prevent any nodes from spending too much energy. They introduce the Minimum
Energy Relay scheme, which transmits data between any two nodes such that overall energy dissipation is
minimized. Suppose K
1 relays are introduced with distance betwe
en any two consecutive nodes di, i=
1...K. Then the total energy is

[ equa 1 from SN survey ]

After minmizing the function Plink (D), the following result is derived

[equa 2 ]

Where d
char =
[equa 3]

The main observation from above bound is that for a giv
en D, there are a certain optimal number of
intervening nodes acting as relays that must be used. Using more or less than this optimal number leads to
energy inefficiencies. Notice that this analysis is best
case analysis considering that the worst
alysis is meaningless in this situation, since the lower bound of lifetime can be arbitrarily bad for any
network. The work presented in this paper will enable a deeper understanding of the fundamental limits of
energy efficiency of wireless sensor network

Monitoring Wireless Sensor Networks

For extending the lifetime of a sensor network, the sensor itself must be made, as energy efficient as
possible and the collaborative strategy, which coordinates sensors, must be energy efficient. However, in
ios where battery replacement is infeasible, the network lifetime can't be extended beyond a certain
time, which depends on the initial capacity of the batteries in the sensors. [37,38] deal with the situation
where the replacement of batteries is feasible
. The problem of fundamental importance in this scenario is
identifying faulty (crashed) nodes in the network. The diagnostic information (i.e., the status


of each node) gathered by operational sensors can be used by an external ope
rator to
maintain network functionality by replacing the depleted batteries. Since the traditional distributed
diagnosis protocols are designed for multiprocessor computers or wired networks they are infeasible or
extremely energy consuming. For this reaso
n, the authors developed a distributed silent fault diagnosis
protocol called

explicitly designed for wireless sensor networks. The protocol takes advantage
of the shared nature of communications and aims at minimizing the total number of bits exch
anged for the
purpose of diagnosis, thus reducing the energy consumption entailed by the protocol execution. The
protocol first constructs a spanning tree of the graph representing the network topology, and then
exchanges diagnostic information only along
the edges of the tree. This allows a significant reduction in
the number of messages to be sent for the purpose of diagnosis.

Zhao et.al. in [39] deal with the same problem but take a different approach. While they agree for
the need to have continuously

updated information about network resources and application activities in a
wireless sensor network after its deployment in an unpredictable environment, they argue that due to the
constraints of low user
sensor ration, limited energy and bandwidth res
ources, it is inefficient to extract
state of each individual sensor. They propose sensor network

as indicator of network health. The
proposed mechanism for collection
residual energy scan (eScan
applied localized algorithms in sensor
networks for e
network aggregation
of local scans. Rather than collect all local scans
centrally, this technique builds a composite scan by combining local scans piece
wise. At each step of
aggregation these partial scans are
by varying the
ir resolutions. They also propose to apply
incremental update

to scans i.e., when the state of a sensor changes rather than continuously re
sending its entire scan, it sends a partial update to a scan only when its local state has changed
significantly. F
urthermore, update traverses up the aggregation hierarchy if it impacts some
aspect of the overall representation. An aggregate scan may loose detailed information such as
the residual energy level at each node, but the compactness of such an abstracted re
can reduce the communication and processing cost significantly. Through simulations they show
that the trade
off between this reduced fidelity and increased energy savings is acceptable. This
mechanism, besides helping the user to decide where

new sensors be deployed to avoid energy
depletion, can help verifying the behavior of energy aware routing protocols and guide in
incremental deployment of sensors.

Simulation Tools

Wireless sensor networks with their focus on applications requiring tig
ht coupling with the physical
world, as opposed to the personal communication focus of conventional wireless networks, pose
significantly different design, implementation, and deployment challenges. Their application
nature, severe resource constr
aints, limitations on their lifetime and the presence of sensors lead to
interesting interplay between sensing, communication, power consumption and topology that designers
need to consider [26,31]. These numerous challenges make the study of real deployed

sensor networks
very difficult and financially unfeasible. At the current stage of technology, a practical way to study
sensor networks is through detailed simulations that can provide a better understanding of sensor
networks and facilitate the developme
nt of new protocols and applications with performance evaluation.

So we need a detailed simulation framework, which can accurately model different micro
sensors [33,34,35] while providing a versatile testbed for new algorithms and protocols. These
tools wi
ll be an indispensable aid in estimating the resources required for the network protocols
to function correctly in new node architectures. By simulating and validating one can also get a
good indication of code size and memory requirements thus resulting i
n feasible low cost designs

[27] discusses another issue relevant to network simulation, the issue of "Level of Detail"
appropriate for wireless simulation models. Too much detail results in slow and cumbersome
simulators while simulators, which la
ck necessary details, can result in misleading and incorrect
answers. Researchers must choose their level of simulation detail with care.

Although sensor networks have received a lot of attention, there are still not many formal
tools available for system
atic study of sensor networks. Currently available tools for modeling
wireless networks such as GloMoSim[28],OPNET[36] and ns
2[25,29] provide great flexibility
in the simulation of wireless ad
hoc networks at all layers [31]. But these tools do not suppor
modeling the power and sensing aspects that are essential to wireless sensor network design.

SensorSim[30] extends ns
2 in a sensor network context by providing new power and
communication models, support for hybrid simulation that allows the interactio
n of real and
simulated nodes and real time user interaction with graphical data display. The framework (see
Figure) contains two types of models. The sensor functional model represents the software
abstraction of a sensor, which includes modules for netwo
rk protocol stack, sensor protocol
stack, middleware and applications.

The second type of model is the power model, which simulates
the actual hardware, abstracts (CPU, radio, geophone, microphone) that carry out the functions of the
sensor function model.

These two layers can be viewed as parallel layers that simulate the software and
hardware. Another feature introduced is the notion of "sensor channel"(similar to wireless channel),
which can be viewed as a medium through which sensing devices can detect
events. The sensor and
network protocol stack are coordinated through middleware and applications. Since sensor networks are
still at their infancy, the properties of the sensor channel are not completely understood, by enabling
interaction with real senso
rs good quality sensor measurements can be introduced into the simulation
directly. This hybrid approach also facilitates a better understanding of the sensor channel and better
channel models. This approach also helps in developing and testing new protoco
ls on real sensors that can
run on large scale simulated networks. This approach is a combination of simulation and emulation and
hence captures best of the both worlds.

The work in SensorSimII[32] presents a Java based online simulator for sensor networ
ks that can
create and simulate simple topologies but doesn't have models for power management. Currently it is
more of a framework for simulations than a general
purpose simulator.

For exploring software strategies applicable to sensors without access to

the existing prototypes,
code emulators on these prototypes will be very valuable.

13 Security Protocols for Sensor Networks

As sensor networks edge closer towards widespread deployment, security issues become a central
concern. All the work that was
presented till now has focused on making sensor networks feasible and
useful, and has not concentrated on security [23]. Despite the severe challenges of limited processing
power, storage bandwidth and energy, security is important for these devices. These

sensors measure
environmental parameters and control air
conditioning and lighting systems. Serious privacy questions
arise, if third parties can read or tamper with sensor data. In the future these wireless sensor networks will
be used for emergency and
critical systems and there these questions of security becomes foremost.
The limited energy supplies create tensions for security: on one hand, security needs to limit its
consumption of processing power, on the other hand, limited supply limits key l
ife time (battery
replacement reinitializes devices and zero out the keys). The aforementioned constraints make the current
secure algorithms impractical. For example, the working memory of a sensor node is even insufficient to
hold the variables required
by asymmetric cryptographic algorithms like RSA It is found that purely
symmetric cryptographic primitives (where both parties share a common key) are more suitable for the
resource constrained sensor networks.

The security properties required by sensor
networks can be classified as below:

Data Confidentiality

A sensor network should not leak sensor reading to the neighboring networks. The
standard solution is to encrypt the data with a secret key.

Data Authentication:

An adversary can inject messages, s
o the receiver needs to make sure that the data
used in decision
making process originates from correct source, In two
party communication case, data
authentication can be achieved through a purely symmetric mechanism. But the sensors need an

broadcast mechanism and hence we need to construct an asymmetric mechanism from
symmetric primitives.

Data Integrity:

This is necessary to ensure the receiver that the received data is not altered in

Data Freshness:

Given that all sensor networks

stream some form of time varying
measurements, it is not enough to guarantee confidentiality and authentication; we must make
sure that each message is fresh. Data freshness implies that the data is recent and that no
adversary replayed old messages. The
possible two types of freshness are : weak freshness,
which provides partial message order but carries no delay information and strong freshness,
which provides a total order on a request
response pair and allows for delay estimation. Weak
freshness is en
ough for sensor measurements, while strong freshness is useful for time

Perrig et.al. present SPINS [23], a suite of security building blocks optimized for resource
constrained environments and wireless communication. The main achievement

of the authors is that they
show that it is feasible incorporate security mechanisms on minimalistic hardware. Their trust setup each
node is given a master key, which is shared with the base station, and hence nodes implicitly trust the
base station. Ini
tially they setup secure channels between nodes and base stations to bootstrap other secure
channels. SPINS has two secure building blocks: SNEP (Sensor Network Encryption Protocol) and
version of Timed Efficient Streaming Loss
tolerant Authe
ntication). SNEP uses a shared
counter between sender and receiver and thus avoid transmitting the counter value in contrast to other
ryptographic algorithms. With the use of this counter SNEP achieves Data confidentiality, two
data authentication,
integrity, semantic security and weak freshness. SNEP achieves low communication
overhead since it only adds 8 bytes per message and by keeping the counter state at each endpoint. A
particularly hard and important mechanism for sensor networks is to provid
e efficient broadcast
authentication. But this requires an asymmetric mechanism; otherwise any compromised receiver could
forge messag
s from the sender.

TESLA overcomes the computation, communication and storage
overhead of asymmetric mechanisms through
a delayed disclosure of symmetric keys.

TESLA requires
that the base station and nodes are loosely time synchronized and each node ne
ds to know an upper
bound on the maximum synchronization error. To send an authenticated packet, the base station simply
computes a MAC (message authentication code) on the packet with a key that is secret at that point in
time. When the node gets the packet, it can verify that the corresponding MAC key was not yet disclosed
by the base station. Since the receiving node is a
ssured that the MAC key is only known by the base
station it is assured that no adversary could have altered the packet and hence buffers the packet. At the
time of key disclosure, the base station broadcasts the verification key to all receivers. When the

receives the disclosed key, it verifies the authenticity of the key and uses it to authenticate the stored
packet. Hence the key disclosure is independent of the packet broadcast and is tied to time intervals. The
authors show that most of the overhe
ad of adding security to the sensor networks comes from the
transmission of extra data than computational costs.

Although SPINS addresses many security issues, it doesn't deal with information leakage due to
covert channels. The suite merely ensures that
a single compromised sensor doesn't reveal the keys of all
the sensors. It is still an open problem on how to design efficient protocols that scale down to sensor
networks, which are robust to compromised sensors. Finally, the problem of DoS attack on a wi
network by jamming the radio channel with a strong signal has to be dealt with.

14 Conclusions

In conclusion, wireless sensor networks present fascinating challenges for the application of distributed
signal processing and distributed control. T
hese systems will challenge us to apply appropriate techniques
and metrics in light of the new technology opportunities (cheap processing and sensing nodes) and
limitations (energy constraints).

We need a systematic analysis (similar to the SPEC benchmark
s) of the architectural alternatives
in the network sensor regime. Any proposed algorithm has to be experimented with larger number of
nodes to further explore the scalability. Much of the current work is evaluated using ad
hoc simulations.
Though current
simulators are helpful in this regard, we need a common framework simulator, which can
be used by everyone and hence one can make comparisons from the results. Furthermore the different
solutions proposed have to be deployed in a real test bed and a detail
ed comparative analysis has to be
made. Although hands
on experience with real embedded systems is essential for algorithm development
in solving real problems, dealing with the real uncertainties, using real capabilities, it is difficult to isolate

for specific behaviors and explore the space of possible interactions in this mode. An emulator
with reasonable detail may prove helpful in this regard. Novel debugging and visualization technologies
designed specifically for the new challenges of sensor
networks will be very helpful in testing and
maintenance of new algorithms and applications. We also see the need to come to a consensus on some
characteristics of wireless sensor networks and the underlying assumptions that can be made while
working on an
y solution.


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ocalized algorithms for coordination

better coordination thru "localized algos".

What's the rationale :

1)each node communicates only with other nodes in some neighborhood

the communication overhead
scales well with increase in network size.

algos will

be robust to n/w partitions and node failures.

Node energies are better utilized since the cluster heads adapt to changes in energy levels. In effect the
sensors in the cluster take turns at being the cluster head based on their current energy levels thu
s leading
to more efficient energy usage.

But localized algorithms are difficult to design

Directed Diffusion

defines a set of abstractions that describe the communication patterns underlying many localized algos.

The diffusion models data naming and lo
cal data transformation features capture the data
centric and
application specificity in sensor networks.

The diffusion primitives help set up communication paths between nodes in sensornets. They play the
role of the routing system in traditional data ne
tworks. If more sensor applications are localized, sensor
networks are unlikely to incorporate reactive routing system like that found in internet. Instead routing
function is tightly integrated with the application. Applications has to use a combination o
f pro
active and
reactive schemes to achieve energy
efficient communication.


gossiping approach based on hierarchy for data aggREGATION

Other areas relevant...

Internet Multicast and Web Cach
ing : Techniques such as lightweight sessions and soft state

management are also applicable in the sensor netwo
rk context.

hoc networking :