LBTC: A Conceptual Energy Saving Framework for Mobile Ad hoc Networks
Mobile ad hoc networks (MANETs), are more challenging due to their unique characteristics and wide applications. The main challenge of this network is its limited battery capacity as nodes are operated by battery. These batteries are limited in capacity and it is a cumbersome task to replace and recharge them in some environments like military operations, environment monitoring etc. Due to this limitation, proper utilization of battery power is very much essential for energy constraint mobile nodes. It is observed that transmission power is the major constitutes of energy consumptions. So to achieve significant energy saving, it is necessary to reduce transmission power at node level. Considering this facts present study has proposed a framework for energy saving using two techniques in MANETs. The proposed distributed topology control algorithm adaptively adjusts transmission power at node level based on nodes neighborhood information. For this, each node maintains a table and updates that periodically. A node reduces its transmission power based on information stored in its table. The second technique applies sleep scheduling approach to further reduce energy consumptions by putting some nodes in sleep state. A node goes to sleep state only when it has no pending traffic and it satisfies the connectivity constraints within its neighborhood.
Received: February 06, 2012;
Accepted: March 19, 2012;
Published: June 27, 2012
Wireless technology has influenced the present society strongly through their
inherent advantages and wide range of products. It has several applications
in different fields which includes cellular data services (GSM, GPRS, CDMA and
3G), satellite communications, hotspot (Wi-Fi) technology, Bluetooth etc. These
technologies are available in the door step by the help of different wireless
networks. Some of these networks are infrastructure dependent while others are
infrastructure independent. Cellular networks such as GSM, CDMA are belonging
to first category while wireless ad hoc networks represent the second.
Wireless ad hoc networks such as, mobile ad hoc network (MANET),
Wireless Sensor Network (WSN), vehicular ad hoc network (VANET) are most
interesting due to their unique characteristics and exclusive applications (Samara
et al., 2011). They are getting more attentions in areas where infrastructure
based networks are neither deployable nor economic, such as: military operations,
environmental monitoring, disaster recovery, patient monitoring, search-and-rescue
operations etc. The major difference between cellular network and wireless ad
hoc network is the resource management and routing. Base stations in cellular
network simplifies routing activity by taking the decision in a centralized
manner (Amin and Islam, 2009) but in wireless ad
hoc network routing decisions are made in a distributed manner at node level.
All the nodes in ad hoc network coordinate to each other to enable communications
among them. Each node acts like a host as well as router, for which nodes are
more intelligent. However, due to lack of central arbitration they are more
vulnerable to many challenges. They suffered some unmet challenges in form of
contend for physical mediums, this in turn reduce the throughputs susceptible
to collisions due to presence of hidden nodes, it is the major issues at MAC
layer, limited battery power, also have to forward the data packets of others,
unpredicted mobility, this issue aggravate when nodes frequently enter and leave
the network and restricted bandwidth, etc. (Cheng and Li,
2008; Wu and Tseng, 2007; Qin
and Chen, 2012; Meng et al., 2008). These
challenges motivate researchers to put their effort to tackle these issues.
Continuous efforts are made and varieties of solutions are obtained but some
problems are not considered in a concrete way under the umbrella of these solutions.
Proper utilization of battery power is considered to be one of the key requirements
in energy constraint wireless networks. It become a pervasive issue in all layers
of communication protocols, until now, research and development in the field
of communication networks was mainly targeted at their functionality and performance
issue, but for battery-driven devices such as sensor nodes, energy efficiency
is a significant consideration. The intensity of its importance has induced
a new research area with energy efficient of communication networks as the main
objective (Shi et al., 2010; Atiq-Ur-Rahman
et al., 2011).
Energy conservation is considered to be one of the key performance metrics
for wireless networks as network longevity and network capacity merely relies
on it. Looking to its importance, efforts are being made to reduce the energy
consumption at all layers of protocol stack. Researchers are focused mainly
at routing and link layer to reduce power consumption at network level while
very few works has done on other layer, also energy saving can be considered
as a cross layer, approach (Lin et al., 2006),
where rather than focusing on one particular layer attention can be made on
multilayer for the same objective. The proposed work considers the cross layer
approach of energy saving. Two power saving techniques are introduced in this
framework using power management and topology control approach. It has been
observed that the overall performance of MANETs such as channel utilization,
end-to-end delay, as well as life time of the network is enhanced if the transmission
power of the nodes is properly adjusted to a lower level (Gomez
and Campbell, 2007; Jayashree and Murthy, 2007;
Wang et al., 2011). Proper adjustment of transmission
power is required not only to increase energy efficiency but also to reduce
the network interference.
Most of the energy management strategies for wireless networks rely on base-stations
support, but base-stations are often not applicable in the MANETs. Due to this
energy management strategies for mobile ad hoc networks are different
from traditional infrastructure based wireless network. We classify the works
reported in the literature as power management approach and topology control
approach. In power management approach nodes in ad hoc network remain
in one of the three possible states: (a) active (b) idle and (c) sleep. Active
states consume more power in comparison to idle and sleep states. In this state
mobile nodes actively participate in the network traffic by sending and receiving
data and control packets. Idle state is the default state in MANETs as nodes
stay most of the time in this state. Idle state power consumptions are nearly
same as that of receiving power consumptions (Feeney, 2004).
Nodes in idle state wait for the traffics to participate. However, nodes in
sleep state switched off their radio transceivers for a particular period of
time and wake up after the end of sleep time. Hence, they consume very less
amounts of power as compare to other two states. Due to its power saving advantages
sleep state is the desired state of power saving in mobile ad hoc networks.
Power management based protocols tries to put nodes in sleep state to save substantial
amount of energy. IEEE 802.11 standard (IEEE Std. 802.11,
1999) and its variants are the representatives of this approach.
Two types of power managements are used in IEEE 802.11 standard protocol. First
type is used for infrastructure based wireless a network while second is for
infrastructure less network. The second power saving approach is relevant to
the ad hoc model and is known as IBSS power save. Synchronized beacon
interval is established by the node which initiates the IBSS and is maintained
in a distributed fashion. In this mode nodes remain within the radio range to
each other. IBSS PS mode saves substantial amount of energy but its power saving
for multi hop ad hoc network is a major issue. The power management scheme
in IEEE 802.11 protocol has several challenges such as clock synchronization,
beacon contention and neighbor maintenance, setting sleep duration etc. These
challenges are more serious where the network is large and dense. To overcome
these challenges and improve the energy efficiency Wu et
al. (2005) proposed an asynchronous power management protocols for multi-hop
ad hoc network. They suggested that their protocol provides better energy
efficiency and throughput. Tseng et al. (2003)
proposed a protocol called dominating-awake-interval by redesigning the IEEE
802.11 PS mode. However, the limitation of the protocol is node, remains awake
for a longer period of time as compared to IEEE 802.11. Ray
and Turuk (2009) discussed some energy efficient MAC protocols for wireless
ad hoc and sensor network. They suggested that power management techniques
are the main stay of power saving in all types of wireless networks.
In contrast topology control approach uses other way of power saving. Rather
than putting the nodes in sleep state in reduces transmission power by implementing
different techniques. It minimizes the maximum power used by nodes at node level
and maximizes network longevity. It preserves major network constraints such
as connectivity (bi-connectivity), k-neighbor set etc. Santi
(2005) addresses several topology optimization problems where he analyses
the problem of designing energy-optimal topologies for different communication
patterns such as unicast, broadcast and multicast. SPAN (Chen
et al., 2002) is a distributed topology control protocol adaptively
elects coordinator from all nodes in the network. Coordinator nodes stay awake
continuously and perform multi hop packet routing. Other nodes remain in power
save mode to conserve energy. SPAN achieves energy saving by selecting few nodes
to work as a coordinator. Communication among the nodes takes place through
these coordinator nodes. It gives guarantee of network connectivity by ensuring
that every node has at least one coordinator node in its radio range. Sahoo
et al. (2007) proposed a distributed transmission power control protocol
to build the power saving tree topologies without taking the local information
of the nodes. They maintain network topology by changing the transmission power.
Most of the topology control protocols require certain information like location,
direction, neighbour list, etc to construct final topology. Location information
can be obtained through Global Positioning Systems (GPS) technology. However,
it is associated with increase cost to support GPS technology. In order to reduce
hardware cost some of the techniques assumes that a subset of the node are equipped
with GPS receiver while other nodes get their location information by exchanging
message with GPS enabled nodes. Direction based approaches requires direction
information rather than location information. Cone based topology control (Li
et al., 2005) uses directional information to constructs the final
topology. Some techniques for estimating the direction has been proposed in
IEEE Antenna and propagation community (IEEE, 2004).
The framework proposed here is based on location information rather than less
accurate direction information.
PROPOSED ENERGY EFFICIENT TECHNIQUES
The main objective of the proposed Location Based Topology Control (LBTC) framework is to achieve energy efficiency by controlling the transmission power and putting nodes in sleep state when they are idle i.e., when they have no traffic to carry and their absence do not create any local partitioning in their neighborhood. The LBTC controls the topology by adaptively adjusting transmission power using local information of the nodes.
Network structure and assumptions: Following assumptions are made here:
||Nodes communicate through Omni-directional antennas and are
identified by their ID
||Nodes are aware of their locations and the source knows the ID and location
information of the destination
||Signal from neighboring nodes is received accurately and the received
signal strength can be measured with the help of radio interface in each
Let G = (V, E) be the topology of the network, where V is the set of nodes and E is the set of links which can change dynamically as nodes move with a random speed. Let Puv denotes the minimum power required for node u to communicate directly to node v. A node u can determine the power Puv when v sends a message to u and vs maximum transmission power Pmax is known to u by using received signal strength calculation as given below. Suppose u receive a message with power Pr and Pmin denotes the nodes smallest possible receiving power. Then:
Pr is given by the free space propagation model:
where, Pt and Pr denote the signal power at transmitting and receiving antenna, respectively λ denotes the carrier wavelength, d denotes the distance between the sender and the receiver and gt and gr denotes the antenna gains at the sender and receiver, respectively n is the path loss coefficient which depends on the environment.
The LBTC framework consists of two phases: (i) link determination phase and (ii) sleep scheduling phase. The network structure of LBTC is shown in Fig. 1.
Link determination phase: In this phase a node randomly broadcast a Hello message using maximum power Pmax. A node that listen this Hello message computes Puv since transmission power (Pmax) of Hello message is constant. Hello message contains the identity of the sender, SenID and its location information, LocInfo. Each node maintains a vicinity table having six fields as shown in Table 1. The purpose of each field is explained below:
||Records the ID of the node which has sends the Hello message
|| Location information of the sender
|| Communication cost between the node and the sender, computed as Puv,
where, u is the current node and v is the node from which it has received
the Hello message
||Minimum cost between the node and the sender
||This field contains a node between the current node and the node which
SenID. Communication from the current node to SenID through the ComNode
shall consume less energy
|| Indicates whether the node is direct or indirect. For one-hop neighbor
the entry is d and for multi-hop neighbor the entry is i
|| Structure of vicinity table
|| Vicinity table at Node X after receiving Hello message from
|| Vicinity table of node X after receiving hello message from
Initially the vicinity table is empty and is calculated when a node receives Hello message from its neighbor. For example when node X with its current location (100, 96) receives the Hello (Y, (101, 96)) message from node Y, it updates its vicinity table as shown in Table 2. The co-ordinates (101, 96) is the current location of node Y. DirCost is computed as in Eq. 1 and MinCost is set to DirCost. LinkType is d as X is the neighbor of Y and ComNode is set to null.
Vicinity table of node X after receiving Hello message from all its neighbors is shown in Table 3.
After gathering initial information about its entire neighbor, nodes X determines whether there exist any ComNode between itself and its neighbor.
Algorithm to determine ComNode: Given 3 points A (a1, a2), B (b1, b2) and C (c1, c2):
|First check for co-linearity of three given points A(a1,
a2), B(b1, b2) and C(c1, c2)
If A, B and C are collinear and B lies between A and C go to Setp (ii),
else go to Step (iii)
||If (a1<b1<c1) and [cost (AB)+cost
(BC)<cost (AC)] then B is the common node. End
||No common node exits between A, B and C
Based on information available in its vicinity Table 3, node
X computes, Y to be the ComNode between itself and node Z and N to be the ComNode
between itself and node O. Updated vicinity table at node X after computation
of ComNode is shown in Table 4. The MinCost between node X
and Z is computed as be 6 instead of 8 and between X and O is 13 instead of
|| Modified vicinity table at node X after determining ComNode
|| Vicinity table at node X
The link type is modified from d to i indicating that there is an indirect
path from node X to Z through Y and from node X to O through N. Cost of communication
through this indirect path will be lesser than the DirCost.
After computing the ComNode, X selects the farthest direct node. Farthest direct node is the neighbor of node X for which MinCost is maximum. Transmission range of node X is set to this MinCost and data is transmitted from node X using this power. Setting transmission range of node X to the cost of farthest direct node, each and every direct node is reachable from node X.
Node X from its vicinity table can determine the appropriate transmission power with which it can transmit to its neighbor. As given in Table 5, if node X has a data packet to be transmitted to node Z, it can use the minimum cost 1 to transmit to node Y which can relay it to node Z. Thus, the total energy consumed at node X is reduced.
Sleep scheduling phase: In this phase nodes which do not take part in
the traffic are put into the sleep state based on criteria as described below.
The nodes work in three states such as: active (A), watching (W) and sleep (S).
The state transition diagram of the three-state is depicted in Fig.
2. Initially a node is in active state and exchange the Hello message, the
duration of Hello message is TH. After expire of Hello message, node
enters to the watching state for taking decision for the next state. When the
node finds that it has some pending traffic, it comes back to active state,
otherwise it runs sleep-scheduling algorithm. Other traffic aware power saving
protocol (Belghith, 2007) saves energy at link layer
by adaptively adjusting the beacon window but in our approach an intended node
goes to save power only when its connectivity in its surroundings is preserved.
Sleep-scheduling algorithm: The algorithm determines the sleep eligibility
for a node. When a node founds it has no traffic to send in the next clock cycle,
before entering to sleep state, it checks the connectivity in its neighborhood
by running sleep-scheduling algorithm. A node decides not to sleep if it found
that any two of its neighbors are not reachable directly or through any other
ways within its two-hop communications. That means node only enters to the sleep
state if connectivity in its neighborhood is preserved without being its active
|| Network structure LBTC
|| State transition diagram of sleep scheduling phase
The node in the sleep state periodically wakes up after sleep time TS
and enters to watch state for determining which state to enter next. If the
node has any data to send then it goes to active state and exchange the Hello
message. The nodes take their own decision regarding their sleep and wake up
strategy. The node executes a procedure called Local-connectivity to find connectivity
in its neighborhood before going for sleep. If the procedure returns connectivity-retain
the node goes to sleep for the duration of TS.
Consider the network structure of LBTC, as depleted in Fig. 1.
When node X has no traffic to participate it waits for TH period
when it conformed that it has no traffic to participate in the next clock period
it wants to enter sleep state. Before entering sleep state node runs the Local-connectivity
procedure. When it found that its absent will break network connectivity
and create network partition it doesnt enter to the sleep state. So, node
X cant goes for sleep even if it has no traffic pending. Like that when
node N runs the procedure it found that its neighbors are reachable even it
goes to sleep state. So, N will enter to sleep state and will wake up after
TS period. At the end of TS the node N will come to watch
state to check the status of traffic. In this way, all intended node will run
local connectivity procedure to find their sleep eligibility.
|Procedure Local-Connectivity (Graph G, Node V):
In this study, we proposed LBTC framework for energy saving in mobile ad hoc network using two methodologies. We propose link determination and sleep scheduling procedure for this purpose.
In LBTC nodes adjust transmission power adaptively using link determination phase and runs local-connectivity procedure to determine sleep eligibility. We believe that our proposed framework not only conserve more power but also increase network throughput as it guaranty network connectivity at worst case. The node reduces the energy consumptions by transmitting with a low transmission power by calculating the link cost in link determination phase. The most common problem of low transmission power communication is to maintain the network connectivity which can affect the network life time. Previous work in this directions only considers energy saving as the time spend by the node in sleep state by sacrificing some throughput and they does considers the connectivity which are normally addressed at higher layer but our algorithm consider the same at node level in a distributed manner. In sleep scheduling phase nodes tries to keep the local connectivity, therefore, sleep in LBTC does not produce any local network partition. However, the Hello message time (TH ) and sleep time (TS ) must be adjusted properly otherwise, if TH is so large then more number of node will participates in the traffic it may results increase in idle power consumption. If TS period is large then more energy may be saved but its impact can affects throughput. Like that if these values are kept low they can hamper energy saving for which threshold must be calculated otherwise its fruitfulness cannot be redeem. Another area of concern is to reduce the message complexity which is a key challenge in energy constrained wireless network.
We are now validating our proposed methodology and comparing its performance
with ESATC (Tian et al., 2009) and XTC (Wattenhofer
and Zollinger, 2004), UDCA (Abidoye et al., 2011).
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