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Challenges And Approaches in wireless reprogramming WSN: An overview

Authors:
  • Director, IIIT Kottayam, Kerala, India Institute of National Importance

Abstract and Figures

Sensor networks are dense wireless networks of small, low-cost sensors, which collect and disseminate environmental data. Wireless sensor networks facilitate monitoring and controlling of physical environments from remote locations with better accuracy. They have applications in a variety of fields such as environmental monitoring, military purposes and gathering sensing information in inhospitable locations. Sensor nodes have various energy and computational constraints because of their inexpensive nature and adhoc method of deployment. In most applications sensor networks are deployed once and intended to operate unattended for a long period of time. Management and maintenance tasks of WSN are challenging. Enabling sensor networks to be reprogrammable is a way to address such challenges. This paper review the problem and approaches associated with the remote reprogramming of wireless sensor networks .
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National Conference on “Emerging Trends in Electronics Engineering & Computing” (E3C 2010)
J D College of Engineering, Nagpur(M.S.) 1235
Challenges And Approaches in wireless
reprogramming WSN: An overview
R P. Shaikh
Master of Engineering [WCC]
Computer Science D
e
par
t
m
e
n
t
GHRCE, N
a
gpur.
rpshaikh@gmail
.
com
Dr. R.. V. Dharaskar
Professor, CSE
Computer S
cie
n
ce
D
e
par
t
m
e
n
t
GHRCE, N
a
gpur.
Dr. V. M. Thakare
Professor, CSE
S.G.B. Amravati universi
t
y
Amrava
t
i
Abstract
Sensor networks are dense wireless networks
of
small, low-cost sensors, which collect and
disseminate
environmental data. Wireless sensor networks
facilitate
monitoring and controlling of physical environments
from
remote locations with better accuracy. They have
applications
in a variety of fields such as environmental monitoring,
military
purposes and gathering sensing information in
inhospitable
locations. Sensor nodes have various energy
and
computational constraints because of their inexpensive
nature
and adhoc method of
deployment.
In most applications sensor networks are
deployed
once and intended to operate unattended for a long period
of
time. Management and maintenance tasks of WSN
are
challenging. Enabling sensor networks to be
reprogrammable
is a way to address such challenges. This paper review
the
problem and approaches associated with the
remote
reprogramming of wireless sensor networks
.
KeyWords: Wireless Sensor Network, W ireless
Sensor,
Network
1.
I
ntroduct
i
on
A wireless sensor network (WSN) is a wireless ne
t
work
consisting of spatially distributed autonomous devices
using sensors to cooperatively monitor physical or
environmental conditions,such as temperature, sound
,
vibration, pressure, motion or pollutants, at di
ff
eren
t
locations. Originally developed as a military applica
t
ion
for battlefield surveillance, wireless sensor network has
been an area of active research with many civilian
application covering areas such as environment and
habitat monitoring, traffic control, vehicle and vessel
monitoring, fire detection, object tracking, smart building
,
home automation, etc are but few examples Wireless
sensor networks gather data from places where it is
difficult for humans to reach and once they are
deployed, they work on their own and serve the data
f
or
which they are deployed. When the environmen
t
changes, sensor network should change too. Since bug
fixes and regular code updates Dynamic Recon
f
igura
t
ion
of Wireless Sensor Networks are common to any
software development life cycle as one goes through a
number of analysis design-implemen
t
a
t
ion-
t
es
t
ing
iterations, there is also a need to reconfigure the nodes
National Conference on “Emerging Trends in Electronics Engineering & Computing” (E3C 2010)
J D College of Engineering, Nagpur(M.S.) 1236
so that they can keep generating relevant information
f
or
us. It is not feasible to collect each and every sensor
node which is deployed and reconfigure it for our needs
.
Hence a set of protocols, applications and opera
t
ing
system support are needed to reconfigure wireless
sensor networks remotely. Reprogramming
is a
mandatory feature in WSN . It is the process o
f
dynamically updating the software running on the sensor
nodes. The ability to add new functionality or replace an
existing functionality with a new one in order to change
the sensor behavior totally, without having to physically
reach each individual node, is an important service even
at the limited scale at which current sensor networks are
deployed. The code is distributed over the air using code
dissemination protocols [2] [3] [5] [6]. These pro
t
ocols
deal with the splitting and compressing the code to be
sent for upda
t
ingthe software on the nodes
.
Communication in these protocols is either single-hop or
multi-hop. In single-hop method the nodes are directly
connected to the base station either through wired or
wireless
and then
reprogrammed. In mul
t
i-hop
communication method, the code is sent hop-by-hop in
the network. After the reception of code at the node, i
t
has to either add or update the existing software running
on it. This requires efficient memory managemen
t
mechanisms
Sensor Node Arch
i
tecture
.
Figure 1 shows schematic diagram of sensor node
components. Basically, each node comprises of a micro-
controller, power source, Radio Frequency (RF)
transceiver, external memory, and sensors
.
These sensor nodes collectively form a W ireless
Sensor Network (WSN), which are used in wide varie
t
y
of applications now a days. A W SN typically consists o
f
hundreds or thousands of sensor nodes. These nodes
have the capability to communicate with each o
t
her
using multi-hop communica
t
ion
.
National Conference on “Emerging Trends in Electronics Engineering & Computing” (E3C 2010)
J D College of Engineering, Nagpur(M.S.) 1237
Figure 1: Sensor Node Archi
t
ecture
Challenges and Requiremen
t
s
Thus, reprogramming sensor nodes, i.e. changing
t
he
software running on sensor nodes after deployment, is
necessary for sensor networks. A scheme is required
t
o
wirelessly reprogram the nodes The scenario poses
many challenges, of them being energy consump
t
ion
,
bandwidth consumption , energy ,node deployment, da
t
a
reporting method, sensor/link heterogeneity,
f
aul
t
tolerance scalability mobility ,quality of service
(QoS).and reprogramming time. Thus the scheme
should satisfy the following requiremen
t
s
:
1.The code image should be transferred through
t
he
network without any errors, since a single incorrect byte
can cause erroneous behavior to the application
t
ha
t
sensors run
..
2.The scheme should be resilient to losing some packe
t
s
during the process since nodes may operate in noisy
conditions, have very simple radios, and cannot a
ff
ord
expensive transmission schemes
.
3.When updating code, we want the application to be
stopped for only a short period of
t
ime
.
4.Significant bandwidth is used for ne
t
work
programming. Delivering the entire program image
(which might be of the order of kilobytes) over the whole
sensor network is a lot more different than just delivering
aggregated data for a possible query (of the order o
f
bytes) and as a result bandwidth should be treated wi
t
h
devo
t
ion
.
5. Energy efficiency is also of great importance. Due
t
o
the fact that sensor nodes work with batteries,
t
heir
power supply is limited. So, the amount of energy
consumption spent for code dissemination should be
taken under considera
t
ion
6. Some of the possible sources for wasting the valuable
power include message collision, idle listening and
useless retransmissions. Hence , the number o
f
messages sent and received should be as low as
possible
.
7. In addition to that, memory requirements of ne
t
work
programming should be minimized
.
Existing reprogramming protoco
l
s
An efficient reprogramming system should not only
reduce communication overhead by
t
ransmi
tt
ing
incremental patches, but it should also ensure that
t
he
amount of flash rewriting at the recipient is minimized
.
The earliest XNP (Crossbow Ne
t
work
Programming) which updates nodes in a single hop
range with whole binary image
.
The Multi-hop Over the Air Programming
(MOAP) protocol extended this to multiple hops [1].
It
introduced several concepts which are used by la
t
er
multi-hop reprogramming protocols, namely, local
recovery using unicast NACKs and broadcast of
t
he
code, and sliding window based protocol for receiving
parts of the code image. In this protocol, nodes
t
rans
f
er
the data in a neighborhood-by-neighborhood basis.
I
n
essence this implies a single-hop mechanism that can
be recursively extended to multi-hop. At each
neighborhood, only a small subset (preferably, only one)
of the nodes is the ‘source’ while the rest are
t
he
receivers. Here, when a node is having a code update, i
t
will advertise its code and those nodes which are
interested in updating the code, will subscribe to
t
ha
t
node.The Problem with this approach is that it canno
t
handle delayed subscriptions. In case of delayed
subscriptions, if no other advertisement reaches
t
he
node, it will not be able to update the code
.
The three protocols that define the s
t
a
t
e-o
f
-
t
he-
art today are Deluge, MNP, and Freshet. They are all
based on the idea of epidemic based reliable mul
t
icas
t
whereby code images are flooded through the ne
t
work
in a controlled manner guaranteeing reliability
t
hrough
the use of epidemic multicast. Deluge is the extension o
f
XNP, which supports multi-hop environment. It is
designed to solve the broadcast storm problem by
suppressing identical simultaneous broadcast packe
t
s
Deluge [2] was the earliest and laid down some design
principles used by the other two. It uses a mono
t
onically
increasing version number, segments the binary code
image into pages, and pipelines the different pages
across the ne
t
work.
The problems faced by Deluge when
t
he
algorithm is applied to highly dense network. As it says, ”
for data dissemination in wireless networks, naive
retransmission of broadcasts can lead to the broadcas
t
storm problem, where redundancy, contention, and
collisions impair performance and reliability”
[
Dunkels
(2004)]. It also states that Deluge has been found
t
o
National Conference on “Emerging Trends in Electronics Engineering & Computing” (E3C 2010)
J D College of Engineering, Nagpur(M.S.) 1238
suffer from slower propagation in the central areas of a
National Conference on “Emerging Trends in Electronics Engineering & Computing” (E3C 2010)
J D College of Engineering, Nagpur(M.S.) 1239
wireless sensor network due to hidden-
t
erminal
collisions caused by increasing neighborhood size and
consequent effects on timing and sender suppression
.
The authors attempt to solve the problem by using
predefined geometric structures. W hile these geome
t
ric
structures can enhance the efficiency of the code
dissemination process, they are of limited use in real
world scenarios where it may not be possible to place
the nodes precisely according to the specified geome
t
ry.
It builds on top of Trickle [7], a protocol for a node
t
o
determine when to propagate code over a single hop
.
The MNP is another variant of XNP-based mul
t
i-hop
network re-programming protocol, but it proposes a
sender selection algorithm for the broadcast s
t
orm
problem and hidden terminal problem to select the mos
t
effective node as a sender. Design goal of MNP [3] is
t
o
choose a local source of the code, which can satisfy
t
he
maximum number of nodes. The authors provide a
detailed algorithm for sender selection using the number
of requests seen by a sender as the key parameter
f
or
the selection. They provide energy savings by turning o
ff
the radio of all the nodes that are not selected as
t
he
sender
.
Freshet [5] aggressively optimizes the energy
consumption for reprogramming by allowing a node
t
o
sleep till the code reaches its neighborhood. It also
reduces the energy consumption by exponen
t
ially
reducing the meta-data rate during conditions of s
t
abili
t
y
in the network when no new code is being in
t
roduced
.
Trickle[6] is a protocol for maintaining code
updates for WSN. Here, nodes stay up-to-date by
periodically broadcasting a code summary to
t
heir
neighbors. The Problem with Trickle is its periodici
t
y.
Since remote code updates is once-a-while
phenomenon, the nodes end up wasting a lot of energy
and time by periodically advertising code upda
t
e
.
There
are solutions provided to stop redundant code upda
t
es
being trickled, but still it is highly inefficient to adver
t
ise
updates based on
t
ime
.
Typhoon is a protocol designed to reliably
deliver large objects to all the nodes of a wireless sensor
network (WSN). Typhoon uses a combination of spa
t
ially
tuned timers, prompt retransmissions, and
f
requency
diversity to reduce contention and promote spatial re-
use. We evaluate the performance benefits
t
hese
techniques provide through extensive simulations and
experiments in an indoor testbed. Results show
t
ha
t
Typhoon is able to reduce dissemination time and
energy consumption by up to three times compared
t
o
Deluge.These improvements are most prominent in
sparse and lossy networks that represent real-life WSN
deploymen
t
s
.
Deluge is the extension of XNP, which suppor
t
s
multi-hop environment. It is designed to solve
t
he
broadcast storm problem by suppressing iden
t
ical
simultaneous broadcast packe
t
s
.
Stream [4] uses the principles of Deluge
f
or
code propagation but greatly reduces
t
he
National Conference on “Emerging Trends in Electronics Engineering & Computing” (E3C 2010)
J D College of Engineering, Nagpur(M.S.) 1240
reprogramming time and energy compared to Deluge
.
low link reliabilities cause problems in mul
t
i-hop
networks. [11] showed that shortest path algorithm in a
network with lossy links selects a path with poor
reliability. In [10], the authors evaluate Deluge and MNP
for different densities and packet organiza
t
ions
The network reprogramming protocol XNP
operated over a single hop.. But as far as we know
,
there has been no prior work to study the effect o
f
parameters like link reliabilities on the performance o
f
multi-hop reprogramming. In this paper, we show how
poor link qualities adversely affect mul
t
i-hop
reprogramming making the alternate single hop
reprogramming approach a
tt
ractive
.
Reijers and Langendoen [13] use a di
ff
-based
updating scheme for W SNs. Diffscripts generated by
diff-based approaches reduce transmission overhead
,
they cannot by themselves guarantee low overhead in
rewriting the flash. This is a direct consequence of being
detached from the higher-level aspects of applica
t
ion
evolution. Consider a set of functions f, g, and h laid ou
t
in _ash as shown in Figure 2. Function f invokes g and
h. If g is updated,there are two interesting possibili
t
ies
.
growing and shrinking. When g shrinks, all the code
below it is shifted to lower addresses. Thus, even
t
hough
h (and other functions below it) do not change in
t
heir
content, they need to be shifted to their new locations.
If
there are any calls to these functions (e.g., the call to h
from f ), the targets of those call instructions need to be
patched by re-applying the corresponding reloca
t
ion
entries3. Basic diff-scripts contain copy and insert (or
add ) instructions [5]. Copy instructions specify a region
of the original, that can be copied to the modi
f
ied
version
.
Insert instructions are used to introduce
t
he
actual changes between the two versions, and are
therefore larger than copy instructions. In this example
,
copy instructions could be used to slide code to lower
addresses (starting with function h). However, inser
t
s
will be required for patching calls to any function
t
ha
t
follows g (e.g., the call to h from f), as these
f
unctions
are relocated. Updating g 's implementation will also
require insert 's (and possibly a few copy
'
s)
.
The boot loader will have to rewrite pages 15
onwards, due to code shift. Page 14 also needs to be
rewritten to update the target of the call to h. Expanding
g has similar repercussions. If g remains the same size
,
diff instructions are generated only to modify the region i
t
occupies. Copy instructions will be generated for the res
t
of the _ash. We notice that although the diff-scripts can
be small, several pages of _ash need to be rewri
tt
en
.
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indica
t
e
Figure 2. Pure diff-based approach. Tabs
which pages are rewri
tt
en
.
shifted. Additionally, functions are provided with a slop
region [20] to grow without running into another
f
unction
.
If a function attempts to grow beyond its allocated slop
region, it is relocated to a larger region, with addi
t
ional
slop space.To illustrate the incremental linking
approach, consider the earlier example. The linker is
modified to provide slop space for each function. Figure
3 shows the effect of g shrinking and growing by small
amounts. The slop provides space for g to grow, and no
code needs to be relocated.Thus, no call targets need
t
o
be updated. The only pages that need to be rewri
tt
en
are due to the insert 's and copy 's necessary to upda
t
e
g .In addition to reducing flash rewriting, the delta is
smaller, and the memory required for rebuilding the _ash
memory is less. Figure 2 does not show this detail, but in
current diff-based approaches, insert instructions are
used to edit the original image or add new data, and
copy 's are used to copy all the unmodified regions
.
Thus, the entire flash is rebuilt in a temporary bu
ff
er
(such as the EEPROM or external _ash), and modi
f
ied
pages are rewritten. Some diff formats provide a
windowing mechanism, which allows for a smaller bu
ff
er
by working with small segments of the original. In our
incremental linking approach, diff instructions are
generated only for the modified pages, and only
t
hose
pages need to be buffered. Utilizing page-sized windows
can reduce or eliminate external flash memory
requirements by working with smaller buffers in SRAM
.
Figure 3. Incremental linking approach. Slop
space is hatched, and tabs indicate which
pages are rewri
tt
en
.
In other words, the sizes of the diff-scripts are
inconsistent with the extent of actual adap
t
a
t
ion
.
Increamental linking approach [14] modify the linking
procedure, to function in an incremental fashion. By
doing so, it minimize the costs of transmission and
f
lash
rewriting. Flash rewriting is reduced by ensuring that as
far as possible, functions that do not change are
no
t
National Conference on “Emerging Trends in Electronics Engineering & Computing” (E3C 2010)
J D College of Engineering, Nagpur(M.S.) 1242
Broadcast Storm and Hidden Term
i
na
l
Prob
l
ems(cha
ll
enges)
For code dissemination in classic flooding: a base node
broadcasts new codes to its neighbors. Upon receiving the
data, each node stores and then rebroadcasts to i
t
s
neighbors. However, in large-scale WSNs with high density
and limited energy, classic flooding is par
t
icularly costly and
results in serious redundancy, con
t
en
t
ion
,
and collision. It is
called the “broadcast storm” problem
[
2
],
and is mainly
caused by two deficiencies:• Da
t
a redundancy (implosion
[3]): A sender may send ou
t
unnecessary (e.g.,
alreadyreceived)data to i
t
s neighbors. To
reduce data redundancy, a sender should be aware of what
data has already been received by i
t
s receivers. Sender
redundancy: Some senders are redundant to cover a
desired area. These nodes canno
t
offer additional
coverage (i.e., nodes that have not been covered by other
broadcasts).The hidden terminal is another issue in
wireless communications. If two nodes are out of the
transmission range of each other (
t
hus “hidden” to each
other), when they send packets at
t
he same time, it may
result in packet collisions at any node located within the
intersection area of these senders
.
The hidden terminal problem degrades the per
f
ormance of
CSMA MAC substantially because carrier sensing cannot
prevent collisions
.
National Conference on “Emerging Trends in Electronics Engineering & Computing” (E3C 2010)
J D College of Engineering, Nagpur(M.S.) 1243
Approaches to Avoid Broadcast Storms and H
i
dden
Term
i
na
l
s
One of the major di
ff
erencesamong various
reprogramming systems is how they address
t
he
broadcast storm and hidden terminal problems. As
discussed above, to avoid the broadcast storm problem
,
data redundancy and sender redundancy need to be
overcome.For data redundancy, two major approaches
exist: data aggregation and negotiation. Da
t
a
aggregation is commonly used to reduce the elas
t
ic
sensing data. The negotiation-based approach,
f
irs
t
proposed in SPIN [3], is used in reprogramming.
It
introduces three-way handshakes between senders and
receivers. A simple negotiation protocol contains
t
hree
types of messages:1 ADV: The source node adver
t
ises
its received objects profile (meta-data). 2 REQ:
It
s
neighbors send back requests after receiving the ADV
t
o
notify the source node about which objects are needed
.
3
DATA: The source node only sends out
t
he
requestedobjects. Popular reprogramming sys
t
ems
(e.g., Deluge and MNP) use the negotiation approach
.
Through negotiations, the source node knows
t
he
requested object (a segment in Deluge and MNP) be
f
ore
sending it out. However, negotiation also adds delays
and introduces control overheads. Therefore,
t
he
negotiation approach has
to be designed and
implemented in an efficient and lightweight
f
ashion
.
Sprinkler divides the whole WSN area in
t
o
square-shaped clusters, and one node is selected in
each cluster as the cluster head. A connected
dominating set (CDS) is calculated from the cluster head
set. The nodes in CDS will be selected to receive and
rebroadcast the data in the first phase in Sprinkler. In
t
he
second phase data will be transmitted from CDS nodes
to all non-CDS nodes. Compared to the other schemes
,
the CDS algorithm is centralized and causes ex
t
ra
overhead
.
Trickle uses a counter-based approach called
polite gossip. Trickle breaks the time into intervals, and
at a random time of an interval, a node broadcasts an
ADV type message (code summary). If a node has
already heard the same ADVs as its own ktimes in
t
his
interval, it “politely” stays quiet. When a node hears an
older summary than its own, it broadcasts DATA
packets. The number k bounds the number o
f
advertisements made in a given cell by suppressing
other nodes, which is adjusted according to the densi
t
y
of the network. Deluge uses the same polite gossip as in
Trickle. However, it adds negotiation (ADV-REQ-DATA)
and segmentation on top of Trickle (Fig. 3a). Poli
t
e
gossip helps Deluge minimize the set of senders
t
o
advertise in a time interval. Negotiation further reduces
the number of senders by giving higher priority to a node
that transmits a segment with a smaller segment ID.
I
n
MNP negotiation is also used in a similar way for sender
selection (Fig. 3b). Thus, both Deluge and MNP ensure
that nodes receive program segments sequen
t
ially.
Sender selection in MNP also uses a request coun
t
er
:
National Conference on “Emerging Trends in Electronics Engineering & Computing” (E3C 2010)
J D College of Engineering, Nagpur(M.S.) 1244
an advertising node in MNP uses it to keep track of
t
he
number of the requests received from its neighbors a
ft
er
advertising a segment. An advertising node will be
suppressed if it overhears others’ ADVs for the same
segment with a larger request count. TDMA and
negotiation are two approaches to overcome the hidden
terminal problem. Sprinkler uses TDMA for media
access control. Transmitting packets is scheduled in
TDMA time slots. The number of TDMA slots affects
t
he
latency of the WSN reprogramming service. However
,
TDMA is not widely supported in current W SNs, and, in
addition,using TDMA implies extra network
f
unctionali
t
y
requirements such as time synchronization.MNP uses
negotiation to suppress hidden terminals. The source
node sends out the ADV twice. The first ADV con
t
ains
meta data, and the second ADV adds the number o
f
REQs received after the first ADV. The REQs from
t
he
receivers also include this request counter to suppress
hidden terminals. Assuming that several nodes are
advertising concurrently, if they overhear REQs
destined to other nodes, hidden terminals that have a
smaller number of requestors will be suppressed
.
Reliability Approaches
Program codes should be delivered to the whole
network or target nodes reliably. Most exis
t
ing
reprogramming systems use a NACK-based approach
since it significantly reduces control traffic, while an
ACK-based approach requires an ACK per packet. Error
recovery is limited to a single hop in all existing sys
t
ems
since the reliability is decreased exponentially as
t
he
number of hops increases. Deluge and MNP uses REQ
as negative acknowledgment (NACK). A REQ packe
t
contains a missing packet bit vector of the adver
t
ising
segment. Each bit of the vector corresponds to a packe
t
in a segment. Sprinkler unicasts requests from
t
he
receiver to get missing packets from source nodes
.
MOAP has no concept of segmentation, and it uses a
sliding window approach to keep track of missing
packe
t
s
.
Conclusions
This survey helps the researchers in unders
t
anding
various aspects like issues, design characteristics while
building reprogramming WSN system software. The
issues presented here motivates the design principles
t
o
be considered/followed while designing reprogramming
system for W SN. The survey not only explores
t
he
existing design approaches of reprogramming sys
t
em
for WSN, but also explores new requirements to be
considered by keeping in mind the future applications o
f
WSN. There are lots of open problems that need
f
ur
t
her
investigation to make reprogramming highly usable and
efficient. Code dissemination is a continuing focus o
f
current
research. However, design trade-offs and
impact factors have not been fully unders
t
ood
.
Approaches to solving the broadcast storm problem
need further study to improve system performance by
reducing control overhead. There has been li
tt
le
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research on scope selection, complete validation, and
code acquisition functions. Design and implemen
t
a
t
ion
of energy-efficient routing and one-
t
o-many
communication protocols for WSN are still evolving. For
practical use, security measures in reprogramming need
to be considered (e.g., DoS attacks and node hijacking)
.
Interoperation among heterogeneous network nodes
and systems is also impor
t
an
t.
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