energy efficient unicast

DEPARTMENT OF ELECTRONICS AND
COMMUNICATION ENGINEERING
EVEN SEM 2020-2021
Course: 18EC931 – Wireless Sensor Networks
Topic : Energy Efficient Unicast
Faculty: Ms. M. Benedict tephila
1

Introduction
• Energy-efficient unicast routing appears to be a simple problem: take the
network graph, assign to each link a cost value that reflects the energy
consumption across this link, and pick any algorithm that computes least-
cost paths in a graph.
• Djikstra’s Algorithm is used to calculate shortest path.(metrics differ )
2

Aspects
Minimize energy per packet (or per bit)
• The most straightforward formulation is to look at the total energy required to
transport a packet over a multihop path from source to destination.
• Minimizing the hop count will typically not achieve this goal as routes with
few hops might include hops with large transmission power to cover large
distances.
3

Energy-efficient unicast…
Maximize network lifetime
• WSN’s task is not to transport data, but to observe.
Several options exist
• Time until the first node fails.
• Time until there is a spot that is not covered by the network –loss of
coverage
• Time until network partition –two nodes in the network not able to
communicate.
• For the network partition, for example, nodes in the graph’s minimal cut set
should have equal energy consumption (or rather, supplies) to ensure
maximum time to partition.
4

Routing considering available battery energy
• While maximizing the network lifetime is clearly a useful goal, it is not
immediately obvious how to reach this goal using observable parameters of
an actual network. As the finite energy supply in nodes’ batteries is the
limiting factor to network lifetime, it stands to reason to use information about
battery status in routing decisions
• Maximum Total Available Battery Capacity Choose that route where the sum
of the available battery capacity is maximized, without taking needless
detours (called, slightly incorrectly, “maximum available power”.
• In the example network, route A-B-E-G-H has a total available capacity of 6
units, but that is only because of the extra node G that is not really needed –
such detours can of course arbitrarily increase this metric.
• Hence, AB-E-G-H should be discarded as it contains A-B-E-H as a proper
subset.
• So the route A-C-F-H is selected.
5

Energy efficient unicast
Minimum Battery Cost Routing (MBCR)
• Instead of looking directly at the sum of available battery capacities along a
given path, MBCR instead looks at the “reluctance” of a node to route traffic.
This reluctance increases as its battery is drained
• For example, reluctance or routing cost can be measured as the reciprocal of
the battery capacity. Then, the cost of a path is the sum of this reciprocals
and the rule is to pick that path with the smallest cost
• Since the reciprocal function assigns high costs to nodes with low battery
capacity, this will automatically shift traffic away from routes with nodes
about to run out of energy.
6

Min–Max Battery Cost Routing (MMBCR)
• This scheme follows a similar intention, to protect nodes with low energy
battery resources. Instead of using the sum of reciprocal battery levels,
simply the largest reciprocal level of all nodes along a path is used as the
cost for this path
• Then, again the path with the smallest cost is used. In this sense, the optimal
path is chosen by minimizing over a maximum
• The same effect is achieved by using the smallest battery level along a path
and then maximizing over these path values. This is then a
maximum/minimum formulation of the problem.
7

Conditional Max–Min Battery Capacity Routing (CMMBCR)
• Another option is to conditionalize upon the actual battery power levels
available
• If there are routes along which all nodes have a battery level exceeding a
given threshold, then select the route that requires the lowest energy per bit
• If there is no such route, then pick that route which maximizes the minimum
battery level.
8

Minimize variance in power levels
• To ensure a long network lifetime, one strategy is to use up all the batteries
uniformly to avoid some nodes prematurely running out of energy and
disrupting the network. Hence, routes should be chosen such that the
variance in battery levels between different routes is reduced.
9

Minimum Total Transmission Power Routing (MTPR)
• The situation of several nodes transmitting directly to their destination,
mutually causing interference with each other
• A given transmission is successful if its SINR exceeds a given threshold
• The goal is to find an assignment of transmission power values for each
transmitter (given the channel attenuation metric) such that all transmissions
are successful and that the sum of all power values is minimized.
10

Unicast protocols - examples
Attracting routes by redirecting
• It uses the idea that nodes can overhear packet exchanges between other
nodes
• If, in these packets, information about the energy required to communicate
between two adjacent nodes X and Z is included, a third node Y can decide
whether it can offer a more energy-efficient route by breaking the direct
communication X-Z into a two-hop communication X–Y–Z
Distance vector routing on top of topology control
• The relay regions concept also lends itself to a formulation of an energy-
efficient routing problem. In a Bellmann–Ford–type algorithm is used to find
paths with minimal power consumption for the graph.
11

Unicast protocols…
Maximizing time to first node outage as a flow problem
• It attempt to maximize the time until the first node runs out of energy. To do
so, they use a centralized, flow-based modeling approach.
• Given is a directed graph to represent the network annotated with the initial
battery capacity of each node and, for each link, the energy costs to transmit
a fixed-size packet.
• the rates of data flows coming from certain nodes in the network, and their
destination nodes are known.
• The goal is to find assignments of flows to forwarding nodes such that the
time until the first node runs out of energy is maximized.
12

Maximizing time to first node outage by a max–min optimization
• It approach the network lifetime maximization problem as a max–min
optimization problem. They propose two algorithms
• One of them has to know the battery power level of each node in the network
and the other can work without this information at only slightly reduced
performance
The max min zPmin approximation
• This heuristic starts out from the intuition to use paths that have a large
residual energy, that is, that path where the minimal remaining power in all
nodes is the largest. This heuristic, however, can result in arbitrarily bad
performance .
13

• The idea is to use a max–min path but limit its maximum power consumption
• This limit cannot be chosen in absolute terms but is best defined as a ratio to
the path with the smallest possible power consumption
• Thus, the path to be chosen should have at most a power consumption of a
factor z times the power consumption along the most efficient path, Pmin;
among the paths that fulfill these constraints, the max–min path with respect
to battery power will be selected as the actual path to be used.
14

The zone routing approximation
• The disadvantage of max min zPmin is that knowledge of battery power
levels is required.
• The “zone routing” heuristic removes this need.
• This is done by partitioning the network in geographical zones where nodes
within the zones are responsible for routing in the zone. Routing among
zones is organized hierarchically.
• The main point is that the resulting performance loss is relatively small,
showing that the approximative maximum lifetime routing can be
implemented on the basis of locally available information.
15

Maximizing number of messages
• A slightly different optimization goal is to maximize the number of messages
that can be sent over a network before it runs out of energy.
• In practice, this can be more important than maximizing the time until the first
node runs out of energy, depending on what can be assumed about the
actual data sensing process and energy consumption.
16

Bounding the difference between routing protocols
• They consider a class of networks where all nodes transmit with identical
power and all nodes continuously have data to deliver to a base station
(possibly over multiple hops)
• Apart from these assumptions, their results apply to a wide range of real
networks, irrespective, for example, of the concrete topology; data
aggregation is not considered. The key question is the energy consumption
of nodes during the data exchange, not the overhead of the routing protocol
itself.
17

• To approach this problem, the graph is partitioned into “spheres” Si that
include all the nodes that are reachable from the base station in at most i
hops
• The interesting case is then networks where “most” nodes are more than a
single hop away from the base station (otherwise, the network is not
particularly interesting anyway). Then, all traffic has to go through the nodes
of sphere S1, and because there are relatively few of these nodes, they limit
the lifetime of the network.
18

Overview
• Multipath routing protocols construct several paths between a given sender
and receiver.
• The basic goal is to find k paths that do not have either links or nodes in
common.
• Once the paths have been established by the routing protocol, the
forwarding phase can then dynamically decide which path (or even paths) to
choose to transmit a packet.
• This can increase the robustness of the forwarding process toward link or
node failures.
21

Sequential Assignment Routing (SAR)
• As a basic rule of thumb, computing such k-disjoint paths requires about k
times more overhead than a single-path routing protocol
• To reduce the multipath-induced overhead by focusing the disjointness to
that part of a network– near the data sink, as the nodes close to the sink are
(often) those that likely are going to fail first because of depleted battery
resources
• Hence, they use other paths to reach the sink.
• The Sequential Assignment Routing (SAR) algorithm achieves this objective
by constructing trees outward from each sink neighbor; in the end, most
nodes will then be part of several such trees.
22

Constructing energy-efficient secondary
paths
• When using multiple paths as standby paths to quickly switch to when the
primary path fails, an obvious concern is that of the energy efficiency of
these secondary paths compared to the (hopefully) optimal primary path
• Their first observation is that strictly requiring node disjointness between the
various paths tends to produce rather inefficient secondary paths as large
detours can be necessary
• To overcome this problem and yet retain the robustness advantages of
multiple paths, they suggest the construction of so-called “braided” paths
(sometimes also called “meshed” multipaths.
23

paths (contd.)
• These braided paths are only required to leave some (even only one)
node(s) of the primary path but are free to use other nodes on the primary
path.
• The braided multipath routing (BMR) improves reliability through creating a
backup node for each node on the main path. If any node in the primary path
fails, the backup node is used to guarantee path connectivity.
24

paths (contd.)
Disjoint and braided paths
25

paths (contd.)
• For disjoint paths, the data sink not only reinforces the primary path via its
best neighbor toward the data source but also sends out an “alternate path”
reinforcement to its second-best neighbor (or several such neighbors, for
multiple standby paths)
• This alternate path reinforcement is then forwarded toward the best neighbor
that is not already on the primary path. For braided paths, each node on the
primary path sends out such an alternate path reinforcement, which only has
to avoid the next upstream node on the primary path but is then free to use
nodes on the primary path.
26

Simultaneous transmissions over
multiple paths
• When using multiple paths as a standby for the primary path, failover times
might be improved compared to strictly single-path solutions. Nevertheless,
there is some delay in detecting the need to use a secondary path.
• Depending on which node makes this decision – only the source node or any
node on the primary path – there can be more or less overhead involved.
• To further shorten the time to delivery and to increase the delivery ratio of a
given packet, it is also conceivable to use all or several of the multiple paths
simultaneously.
• The simplest idea is to assume node-disjoint paths and to send several
copies of a given packet over these different paths to the destination.
Clearly, this trades off resource consumption against packet error rates.
27

Randomly choosing one of several paths
• When maintaining multiple paths, it actually makes sense also to use paths
that are less energy efficient than the optimal one
• One reason to do so is to share the load among all nodes in order to use the
available battery capacity in the network better
• Each node maintains an energy cost estimate for each of its neighbors
(toward the destination, packets are not routed “away” from their destination)
• When forwarding a packet, the next hop is randomly chosen proportional to
the energy consumption of the path. To the upstream node, the weighted
average of these costs is reported.
28

Trade-off analysis
• Their basic observation, made in a simplified scenario of five nodes, is that it
is not possible to simultaneously optimize both robustness and energy
efficiency of a given set of paths, but only the notion of Pareto optimality can
be applied .
• However, that single path solutions that require a larger transmission power
tend to dominate multipath solutions with low transmission power.
29

energy efficient unicast

More Related Content

What's hot

Similar to energy efficient unicast

Recently uploaded

energy efficient unicast