Sandip Mandal and Rama Sushil
526 REAL TIME DEPLOYMENTS AND SENSING OF PATIENT’S VITAL PARAMETERS BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS
the overall performance of the network and because
redundant nodes can be put into a low-power sleep
mode, ready to be reactivated when the need arises. This
can prove to be the best effort in achieving maximum
energy ef ciency. In the following subsections, we will
expand on the details of the most important aspects of
a WSN, namely network integrity, sensing quality and
power management, and how autonomic principles aid
in their optimization.
Intelligent Power Management:
Due to the deployment of WSN in a potential hazard-
ous environment, it is of paramount importance that the
network operates for as long as possible without requir-
ing routine and frequent maintenance tasks. The most
common of these tasks is battery replacement. Thus, it is
vital that the nodes of the network manage their power
consumption in an intelligent manner to deliver the lon-
gevity required by the network. A node’s lifetime will be
proportional to the amount of time it is active. Therefore
limiting the nodes activity on a network wide basis will
increase the lifespan of the entire network and this has
proved to be one of the most effective power manage-
ment techniques for WSNs [F. Ye et al, 2003]. An inactive
node is termed as hibernating, a temporary state in which
negligible or no power is consumed by it. Hibernating
nodes are unable to report their sensed data and as such
leave a blind spot in the network, where no active sensor
is monitoring. The hibernation of a node must be per-
formed in context to a network wide quality of service
metric that must be maintained by the active nodes. For
power management, this is typically coverage. Each sen-
sor is associated with a sensing radius within which it
can sense and outside which it is dormant. Some tech-
niques use an inverse distance relationship between the
sensor and its ability to sense but there is still a limit
to its sensing capability. An area is deemed covered if
the union of all the active sensors sensing discs includes
every point within the sensed area. This is the constraint
under which nodes may be hibernated, thus managing
the networks power consumption intelligently while
maintaining a quality of service with regard to its surveil-
lance density. Algorithms based on this principle include
CCP [H. Zhang et al, 2005] and OGDC [F. Ye et al, 2003].
These techniques operate on an optimization of the prob-
lem, ensuring that the intersection of two sensing discs
is covered by another sensor and that every intersection
point of the sensors discs within the boundary area are
also covered by another sensor. Using this property the
algorithms manage the nodes activity and limit the power
consumption by the network. Another technique based
on the standard coverage maintenance is interpolation,
and more speci cally interpolation error [R.Tynan et al,
2005]. For a given sensor network meant for sensing tem-
perature for example, there will be a temperature distri-
bution function across the area of the network. Sensors
of the network are responsible for sampling this func-
tion at discrete locations within the area and report their
readings to a base station. Interpolation is a mathematical
technique for approximation of function values between
known values of that function. The known points of the
temperature function are at the locations of the sensors
within the context of sensor networks. Therefore, inter-
polation could be used to approximate the temperature
between the sensors. A set of sensors used to approximate
another sensor’s temperature reading and then compar-
ing it to the actual reading at the sensor’s location is the
interpolation error. Hibernating sensors whose interpo-
lation error is less than a particular de ned application
threshold, then the node is hibernated, thus conserving
further amounts of energy. This interpolation technique
is the rst coverage technique that includes neighboring
node locations as well as neighboring node readings and
can deliver greater savings.
Intelligent Routing:
Forwarding of packets from a source to a destination is
the fundamental feature of multi-hop networks. In such
networks, a node should be capable of identifying the
best node to pass on the packet so that it reaches the
destination in time. The easiest way to do it would be
to ood the entire network; however, this is an unfeasi-
ble approach due to the energy consumption and packet
overhead that the ooding would cause. An ef cient
routing poses great challenges such as which node to
forward the packet among all neighbouring nodes and
the trade-off between lightweight (but with high delay)
reactive on-demand type routing and low latency (but
computational heavy) of proactive type routing. Proac-
tive protocols try to keep an accurate snapshot of the
networks working status and maintain full knowledge
of the system; hence they are suitable for high computa-
tional capability devices but the low processing capabil-
ity of sensors prevents them possessing a full proactive
routing protocol. On the contrary, on-demand routing
protocols exempt from maintain a routing table all the
time but only build it when a packet needs to be relayed
to a certain destination. This is not an easy task, espe-
cially when the destination is unknown, and the situa-
tion is exacerbated when sensor node energy constraints
do not permit continuous network ooding of packets. A
secondary issue concerns global node addressing, which
causes to prevent functioning of nodes in two differ-
ent parts of the networks from having the same ID; an
undesirable situation which can lead to incorrect routing
for certain destinations. Apparently, this is not a trivial
problem, particularly when dealing with large scale sen-
sor networks that may comprise thousands of units. The