1. National Conference on Current Trends in Computer Science and Engineering - CSECONF2012
Energy consumption mitigation Routing Protocols for Large-Scale Wireless Sensor Networks
Anil Kumar H1 , Suma 2, Manjunath CR3 , Dr Nagaraj GS4
1,2
Mtech 2nd sem,Dept of CSE,SET,Jain University
3
Asst prof,Dept of CSE,Jain University
4
Prof,Dept of CSE,RVCE,VTU
hmsanilkumar@gmail.com
suma_vaidya@yahoo.com
Abstract: With the advances in micro-electronics, wireless energy of a sensor reaches a certain threshold, the sensor
sensor devices have been made much smaller and more will become faulty and will not be able to function
integrated, and large-scale wireless sensor networks properly, which will have a major impact on the network
(WSNs) based the cooperation among the significant performance[1,2].
amount of nodes have become a hot topic. “Large-scale” The routing protocols for large scale WSNs can
means mainly large area or high density of a network. categorized as control
Accordingly the routing protocols must scale well to the • overhead reduction-based,
network scope extension and node density increases. A • energy consumption mitigation-based and
sensor node is normally energy-limited and cannot be • energy balance-based.
recharged, and thus its energy consumption has a quite
significant effect on the scalability of the protocol. In a II-Energy consumption mitigation-based
hierarchical routing protocol, all the nodes are divided into category: The routing protocols in this class aim to
several groups with different assignment levels. The nodes mitigate the energy consumption. They exploit various
within the high level are responsible for data aggregation means to achieve this target, such as dynamic event
and management work, and the low level nodes for sensing clustering, multi-hop communication, cooperative
their surroundings and collecting information. With focus communication and so on. These methods can consume the
on the hierarchical structure, in this paper we provide an energy appropriately and avoid wasted energy [1].
insight into Energy consumption mitigation routing
protocols designed specifically for large-scale WSNs.
III - Data Gathering algorithm based on Mobile
According to the different objectives, the protocols are
generally classified based on different criteria such as Agent (DGMA)[3]
control overhead reduction, energy consumption mitigation In terms of energy consumption reduction and
and energy balance. This paper focuses on the study of network end-to-end delay decrease, a Data Gathering
energy consumption mitigation to show how to mitigate the algorithm based on Mobile Agent (DGMA) is proposed for
energy consumption. the cluster-based wireless sensor network. where an
emergent event occurs is clustered dynamically based on
Keywords: large-scale wireless sensor networks, routing the event severity, by which the scale and lifetime of
protocol. clusters are determined. In each cluster a mobile agent is
utilized to traverse every member node to collect sensed
data. In the higher level of the network, a virtual cluster is
formed among the cluster heads and the base station, and
I- Introduction multi-hop communication is adopted for sensed data
WSN is widely considered as one of the most delivery to the base station (BS).
important technologies for the twenty-first century. A WSN In DGMA, all the sensor nodes are in “restraining” state
typically consists of a large number of low-cost, low- and they are activated only when some emergent event
power, and multifunctional wireless sensor nodes, with occurs. Then the nodes having monitored the event are
sensing, wireless communications and computation clustered. After the event intension gets reduced, the
capabilities . These sensor nodes communicate over short clustered nodes will change to a “restraining” state for the
distance via a wireless medium and collaborate to sake of energy consumption reduction. In the cluster, the
accomplish a common task, for example, environment tree structure is used to save energy instead of single hop
monitoring, military surveillance, and industrial process communication between the sensor nodes and the cluster
control.In many WSN applications, the deployment of1 head. After the cluster construction is complete, a route for
sensor nodes is performed in an ad hoc fashion without the mobile agent, which is equipped on the cluster head, is
careful planning and engineering. Once deployed, the used to traverse all the member nodes for collecting the
sensor nodes must be able to autonomously organize sensed event data. This process is started up by the cluster
themselves into a wireless communication network. Sensor head and repeated at every cluster member by broadcasting
nodes are battery-powered and are expected to operate a request packet, and anticipating a reply from its each
without attendance for a relatively long period of time. In neighbor for getting residual energy, path loss, and event
most cases it is very difficult and even impossible to intension information of the neighbor. To deliver the
change or recharge batteries for the sensor nodes. When the sensed data to the final destination (here the base station) in
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2. National Conference on Current Trends in Computer Science and Engineering - CSECONF2012
the higher level of the network a virtual cluster is formed b) The Data Aggregation on Mobile Agent
wherein the base station acts as the cluster head. As in the The mobile agent consists of identification ID, route
local cluster, a multi-hop communication is adopted. The information, data buffer and processing codes, in which
current cluster head will select the node which is the closest data buffer mainly load the data distilled or fused data from
to the base station in the neighboring nodes as its next hop. sensor nodes
If the distance from all neighbor nodes to the base station is
longer than that from the node itself, the node will IV - Dynamic Minimal Spanning Tree Routing
communicate with the base station directly. When the Protocol (DMSTRP)[4]
number of the sensor nodes increases, the energy DMSTRP is a cluster-based routing protocol,
consumption in DGMA increases more slowly. uses Minimal Spanning Tree (MSTs) to replace clubs to
Furthermore, the dynamic cluster formation feature further connect the node in the clusters in two layers of the
reduces the energy consumption. The use of a mobile agent network: intra-cluster and inter-cluster. Because clubs are
reduces energy consumption, but extends the delay for the less effective than a spanning tree in connecting the nodes
cluster head to collect all the sensed data from all the if the network area is larger, DMSTRP is an elegant
member nodes. The chain-like route delivery of data by the solution in larger network areas.
cluster head makes the node closest to the base station (Low Energy Adaptive Clustering Hierarchy)LEACH
overloaded and destroys the reliability. chooses clubs as the basic topology of the network, as
Cluster-based wireless sensor network saves energy by shown in Figure 1 and managing clubs does not need multi-
reducing the number of nodes communicating with base hops and thus makes the routing path simple. One step
station. Compared to direct communication, cluster-based further in (Base Station Controlled Dynamic Clustering
method has a remarkable improving in energy-efficient. Protocol )BCDCP, the CHs are connected by a tree instead
DGMA includes dynamic clustering and Data Gathering of a club and the BS functions as the manager of the whole
Based on Mobile Agent for Emergent Event Monitoring network, so BCDCP is more energy-efficient than LEACH.
DMSTRP improves BCDCP further by connecting nodes in
Dynamic Clustering clusters by MSTs. In each cluster, all the nodes including
a) Dynamic Clustering Based on Event Severity Degree: the CH are connected by a MST and then the CH acts as
After wireless sensor network is deployed into the the leader to collect data from the nodes on the tree. On the
monitoring environment, all nodes will be set to higher level, all the CHs connected by another MST
“restraining” state rather than clustered. And they’re cooperate to route data towards the BS. The data fusion
activated just when some emergent event occurs. Then the process is handled during the packet transmission along the
nodes will be clustered. The scale and lifetime of the tree route.
clusters lie on the event severity degree. After the Obviously, DMSTRP consumes energy more efficiently
stimulating intension is reduced, those activated nodes will than LEACH and BCDCP, because the average
change to “restraining” state over again. The cluster-tree transmission distance between nodes is reduced through the
structure is used to save energy , with multi-hop rather than multi-hop intra-cluster and inter-cluster communications,
single hop from the member nodes to the cluster head. and thus the energy dissipation of transmitting data is
potentially reduced. Furthermore, due to the reasonable
schedule, the transmission collision is alleviated and
b) The Construction of Virtual Cluster: Generally, single- DMSTRP can achieve shorter delay compared with
hop communication is taken between the cluster heads and LEACH and BCDCP. But the transmission schedule
the base station in spite of long distance, in which those creates more overhead.
cluster heads away from the base station always have a
weak lifetime because of more energy consumption led by V - Hierarchical Geographic Multicast Routing
long-distance. A multi-hop virtual cluster is formed with (HGMR).[5]
base station as the cluster head. The path from the cluster HGMR aims at enhancing data forwarding efficiency and
head to base station can be searched as follows. The cluster increasing the scalability to a large-scale network. HGMR
heads always select the node which is the closest to base seamlessly incorporates the key design concepts of the
station in the neighbor nodes as its next hop. If the distance Geographic Multicast Routing (GMR) and Hierarchical
from all neighbor nodes to base station is longer than that Rendezvous Point Multicast (HRPM) protocols, and
from the node itself to base station, the node will optimizes the two routing protocols in the wireless sensor
communicate with base station directly. network environment. HGMR starts with a hierarchical
decomposition of a multicast group into subgroup of
manageable size using HRPM’s key concept of mobile
Data Gathering Based on Mobile Agent for Emergent geographic hashing. Within each subgroup, HGMR uses
Event Monitoring GMR’s local multicast scheme to forward a data packet
a) Dynamic Route Planning of Mobile Agent: along multiple branches of the multicast tree in one
For an emergent event monitoring scene, when some event transmission. In HGMR, the multicast group is divided into
occurs, only those nodes in event area would be activated subgroups using the mobile geographic hashing idea: the
to cluster. The selection of the next hop for mobile agent deployment area is recursively partitioned into equal-sized
not only bases on energy consumption and path loss, but square sub-domains called cells, where d is decomposition
also the stimulated intension received by the nodes, in index depending on the encoding overhead constraints, and
which the discrete emergent event is under consideration. each cell consists of a manageably-sized subgroup of
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3. National Conference on Current Trends in Computer Science and Engineering - CSECONF2012
members. An Access Point (AP) is responsible for all joint clustering and optimal cooperative routing, where
members in its cell, and APs are managed in turn by a neighboring nodes dynamically form coalitions and
Rendezvous Point (RP). The role of each AP or RP is cooperatively transmit packets to the next hop destination.
mapped to some unique geographic location by a simple The cooperative sensor network can be modeled as an
hash function. The node that is currently closest to that edge-weighted graph, based on which minimum energy
location then serves the role of AP/RP, and routing to the cooperative routing is characterized by using the standard
AP/RP is conveniently achieved by geographic routing. To shortest path algorithm.We study two interesting cases: 1)
join a hierarchically decomposed multicast group, a node For the case where the delay can be expressed in terms of
first hashes the multicast group identifier (GID) to obtain the number of hops, we use the bi-section method to find
the hashed location of the RP via a hashed function and the maximum throughput routing; 2) For large scale
sends a JOIN message to the RP, which is the same as in networks where the end-to-end delay can be approximated
the flat domain scenario. After receiving the value of the as the product of the number of hops and the average one-
current d of the hierarchy from the RP, the node utilizes the hop delay, we present a polynomial time algorithm to find
hash function with d and the node’s location to compute the the maximum throughput routing. the energy efficient
hashed location of the AP belonging to its cell. Note that cooperative routing can enhance the performance of WSNs
computing the hashed location assumes that all nodes know significantly.
the approximate geographic boundaries of the network.
After that the source builds an overly tree, the Source → We have taken some initial steps to investigate distributed
APs tree, whose the vertices are active APs in a topology cooperative geographic routing, building on node
graph; and an AP → Members overly tree is also built from cooperation and traditional geographic routing. As
the AP, considering each member as the vertex. 2 d illustrated in Fig. 1, for a given source-destination pair, the
When a source needs to send data packets, it utilizes the routing problem in a coalition-aided network was treated as
unicast-based forwarding strategy belonging to HRPM to a multiplestage decision problem, where at stage i, the
propagate data packets to each AP along the Source → APs coalition head, denoted as CHi, first broadcasts data
tree. In each cell, adjusting the value of d, the number of packets to all nodes within its coalition and looks for the
members for which an AP is responsible does not increase next stage coalition to forward the packet to. Once the next
too much. Therefore, GMR’s cost over progress optimizing stage CH, denoted by CHk, was chosen, CHi coordinates
the broadcast algorithm, which is used to select the next the nodes within its coalition to cooperatively forward the
relay node at each hop, contributes to reduce the number of packet to CHk. This process continued until the data were
data transmissions while maintaining a low encoding forwarded to the destination
overhead compared with the unicast communication.
Sensor nodes running GMR use the position of their
neighbors to select the subgroup which is the best one to
deliver the message towards the destination, and the
selected neighbors can reduce most the total route to
destination. When no neighbor of the current node can
reduce the route to the destination, face routing is used to
circuitously search the path to the destination. In HGMR,
the geographic hashing algorithm makes the membership
management very simple with almost zero cost. According
to the number of the nodes which play the different roles,
HGMR selects the transmission methods for different
hierarchies in reason, which makes the routing energy-
efficient and scalable. However, the RP is in charge of too
much missions in HGMR, which may bring the problem of
rapid energy consumption and make the entire network
collapse.
It focus on joint optimal clustering and cooperative routing.
HGMR starts with a hierarchical decomposition of a Consider a cooperative sensor network, where a node with
multicast group into subgroups of manageable size (i.e. data would first multicast the packet to a subset of its
encoding overhead) using HRPM’s key concept mobile neighbors, and then ask them to dynamically form a
geographic hashing. Within each subgroup, HGMR uses coalition, and cooperatively transmit the packet to the next-
GMR’s local multicast scheme to forward a data packet hop destination. The corresponding energy consumption is
along multiple branches of the multicast tree in one the sum of the multicast cost and the
transmission. Thus, HGMR can simultaneously achieve cooperative transmission cost. Intuitively, when the number
energy efficiency (through higher forwarding efficiency of nodes in a coalition increases, the cooperative
utilizing multicast advantage) and scalability (through low transmission
overhead hierarchical decomposition). cost would decrease, but the multicast cost would increase,
and vice versa.
VI - Joint Clustering and Optimal Cooperative
Routing (JCOCR):[6]
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4. National Conference on Current Trends in Computer Science and Engineering - CSECONF2012
JOINT CLUSTERING AND MINIMUM ENERGY
COOPERATIVE ROUTING includes
a) Optimal Coalition Size: Consider a sensor network,
where each node has a strict power constraint Pmax. Data
need to be routed from a source node S to a destination VII- CONCLUSION
node D. In each transmission, an intermediate node would
multicast the packet to a subset of its neighbors, and ask the At present, routing in large-scale WSNs is a hot research
nodes in the subset to dynamically form a coalition and topic with a limited but rapidly growing set of efforts being
cooperatively transmit the packet to next stage destination published. This paper is contribution to study on 4 various
(point-to-multiple-point transmission first, and then
routing protocols of Energy-Consumption Mitigation in
multiple-point-to-point transmission). During the routing
large-scale WSN’s.
process, the number of neighboring nodes that participate
With the increasing functionalities available to a wireless
in the cooperative transmission, i.e., the size of the dynamic
sensor node, more complicated tasks which involve more
coalition, plays a key role. Note that the energy cost of each
energy consumption and network overhead may be
transmission is the sum of the multicast cost and the
assigned to the sensor nodes. To increase energy efficiency
cooperative cost. Intuitively, a larger coalition would
and scalability of the network still remains a challenging
reduce the cooperative cost, but may require more multicast
research area.
energy to reach nodes further away, whereas a smaller
coalition would require less multicast energy but higher
cooperative cost. Thus motivated, we characterize the REFERENCES
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