This project is enhancement of many to one transmission with power aware routing protocol. This work focuses on single sink based WSNs, in which a WSN is composed of a number of sensor nodes associated with a single sink node. The primary role of sensor nodes is to gather data of importance from its surroundings. In this work, we consider a WSN design that is practical, and low cost. We propose Advanced Hybrid Multi-hop routing (AHYMN) to mitigate the impact of sink node isolation. To our best knowledge, this is the first design that considers a hybrid multi-hop routing architecture1 given below. Although flat multi-hop routing algorithms enable routing of data in a fashion that minimizes the power consumption of the WSN, they fail to exploit the data aggregation opportunities by virtue of data collected from the WSN. In many WSN applications with the relatively high node density, the data collected by individual nodes are highly redundant, thus making data aggregation a very attractive scheme in WSNs. Hierarchical multi-hop routing algorithms aim to capitalize on the highly correlated nature of WSN’s collected data. We describe the operation of the most notable example of hierarchical multi-hop routing algorithms, from that we can improve the routing efficiency and channel allocation in cluster networks. The cluster head selection is obtained based on energy level and routing efficiency of the network. We compare the various parameters like Cluster head size, energy, time, packet loss etc., with conventional methods (existing methods)
INTRODUCTION |
| In recent years, with the rapid development of wireless communication technology, and the miniaturization and
low cost of sensing devices, it has become feasible to employ Wireless Sensor Networks (WSNs). WSN is a group of
sensors equipped with transmission capable devices that are deployed in great numbers to monitor areas of interest. The
general structure of a WSN is composed of a set of sensor nodes and a sink node. The role of sensor nodes is to gather data
from their surroundings and send them to the sink node. In addition, the sensor nodes also assume the role of relaying data
by virtue of infrastructure less nature of the network. On the other hand, the general role of the sink node, which can be
mobile or immobile, is to act as a data assembly point in which data is extracted from the network. A significant limitation
of current sensor nodes is low battery capacity; as a direct consequence, efficient use of the sensor node’s energy reserve is
essential. The sensor node utilizes its built-in battery for communications and sensing; in the occasion of battery’s
exhaustion, the sensor’s functionality stops. In such an occasion, part of the networks functionality is lost; also note that
changing batteries of a large number of sensor nodes over wide areas in unsafe terrain is practically infeasible.
Consequently, much research has been focused on maximizing the lifetime of the sensor network; however, most of the
previous works do not take into account the isolation of the sink node, caused by the death of its surrounding nodes. In the
case where the sink is mobile, the load can be balanced over all nodes in the network, and the problem can be avoided by
changing the migration route of the sink node to gather data from most of the living nodes as in KAT [1]; this problem
cannot be avoided if the sink node is immobile. However, it is possible to mitigate this problem by controlling the routes of
data transfer that largely affect the variance of individual power consumption of each sensor node, especially inside the high load areas. The objective of our research in this paper is to extend the network lifetime of WSNs via a better routing
algorithm. In this paper, we propose a routing algorithm that assumes that the sink is immobile and demonstrate its
effectiveness through mathematical analysis and computer simulations. The rest of this paper is organized as follows.
Section II reviews existing multi-hop routing algorithms for WSNs, and describes the sink isolation problem. |
II.MULTI-HOP ROUTING ALGORITHMS FOR WIRELESS SENSOR NETWORKS |
| In general, in order to maximize the WSN’s lifetime, the total power consumption of the network should be
minimized while ensuring fair power consumption among nodes. Much effort has been focused on WSN multi-hop routing
algorithms, and many algorithms have been proposed. These may be widely categorized as flat multi-hop routing
algorithms and hierarchical multi-hop routing algorithms. |
| Fig. 1(a) shows an example of how flat multi-hop routing is used to send data. In the figure, an arrow’s thickness
is proportional to the amount of data being sent over the corresponding link. Each sensor node has the ability to
communicate over a bounded area with other sensor nodes. Link utilization differs
greatly among different algorithms. For example, algorithms proposed in [2], [3] have been designed to minimize the total
power consumption of the network as the objective; in this kind of algorithms, the cost of using a communication channel is
defined by the following equations. |
| linkcost(i, j) = es(i) + er(j) |
| Here, linkcost(i, j) is defined as the amount of energy consumed for sending a unit of data from node i to j. es(i) is
the energy consumed by node i while sending a unit of data to node j; this value is proportional to the square of di,j , which
is the distance between nodes i and j. er(j) is the energy consumed by node j in receiving a unit of data. By using the route
where the sum of all link costs is minimized, the WSN’s total power consumption can be minimized. From a different
perspective, these algorithms pose an inherent problem, i.e., certain nodes are over-burdened, and thus consume their
energy in a rapid manner. An effective algorithm [4], which uniformly distributes power consumption over each node, aims
to address this problem. The following equation is used to define the link cost. |
| linkcost(i, j)uniform = linkcost(i, j) ∕ Eni. |
| By using the residual power of the sending node, Ei, as the denominator of the link cost, the possibility of it being
selected as a relay node decreases as its remaining energy decreases. Toh [2], set n to be 2. It is possible to uniformly
distribute power consumption over individual nodes and at the same time to minimize overall power consumption. Besides
the previously mentioned algorithm, others such as zPmin [3] and max–min T [4]–[5] have also been proposed. |
B. Hierarchical multi-hop routing algorithms |
| In hierarchical multi-hop routing algorithms, sensor nodes assume different roles. Here, we briefly review the most
notable example of hierarchical multi-hop routing algorithms, dubbed Low-Energy Adaptive Clustering Hierarchy
(LEACH) [6], as an example for explanation. LEACH is a two-layered hierarchical multi-hop routing algorithm. Each node
can take on the role of a Cluster Head (CH) or Cluster Member (CM). In addition, each node’s role can be renewed in a
time interval, referred to as a round. In each round, each node can declare itself as a CH with a certain probability;
otherwise, the node behaves as a CM. A network is divided into a number of clusters, called cells. CMs communicate with
the CH that controls the cell to which they belong. Each CH aggregates and compresses the data received from CMs within
the cell that it controls, and sends it to the sink node. In LEACH, since CHs initiate communication directly to the sink
node, the transmission distance between the sink node and each CH tends to be large, thus draining the battery quickly;
Multi-hop LEACH (M-LEACH) [7] has been proposed to mitigate this problem, as shown in Fig. 1(b). In M-LEACH,
minimizing the power consumption of the CHs by means of multi-hop communications, can obviously delay their power
exhaustion. While CHs are determined randomly in LEACH, changing the principle that governs how CHs are selected can
decrease power consumption, as in HEED [8] and PEACH [9]. In hierarchical multi-hop routing algorithms, since the
number of relay nodes used to convey data to the sink node is relatively smaller than that in flat multi-hop routing
algorithms, the length of the communication distance of each hop becomes greater, and thus requiring higher power to
transmit a unit of data. Nevertheless, hierarchical multi-hop routing algorithms are a promising approach in terms of their
capability to use data compression to reduce the power consumption of the network by reducing the data flow. |
C. Sink node isolation problem in WSNs |
| In multi-hop routing algorithms, nodes that are within the maximum transmission range of the sink node form the
subset of nodes that enable the sink node to gain connectivity to the network. We refer to the area, which contains these
nodes, as the Sink Connectivity Area (SCA). As illustrated in Fig. 1(a), since the amount of data relayed per node increases
as nodes become closer to the sink node, leading to quicker power exhaustion of these nodes than others. When all of the
nodes located in SCA die, the sink node can no longer gather data from other alive nodes due to the lack of available routes
between the sink node and the rest of the network; this is equivalent to the breakdown of the network deployed to gather
sensed data. In other words, to evaluate the network lifetime exactly, it is essential to take into account the influence of the
sink node isolation problem, while most of previous works have only investigated the surviving rate of nodes in the
network. Therefore, we propose an algorithm designed with the consideration of the impact of the sink node isolation
problem in order to improve the longevity of the network. |
III. HYBRID MULTI-HOP ROUTING ALGORITHM |
| In general, since the number of sensor nodes in the SCA is much smaller than that outside the SCA, the amount of
data generated by the nodes in the SCA can be negligible as compared to the volume of data flowing into the SCA from
outside. This implies that most of the power consumption in the SCA is due to transferring the data that comes from outside
the SCA to the sink node. In other words, in order to limit the power consumption in the SCA, the amount of data flowing
into the SCA needs to be reduced, and/or the power consumption to relay the data from outside the SCA to the sink node
needs to be minimized. In fact, the proposed scheme, referred to as HYbrid Multi-hop routiNg (HYMN), illustrated in Fig.
1(c), aims to achieve the effect of both solutions by employing a hierarchical multi-hop routing algorithm outside the SCA
and using a flat multi-hop routing algorithm inside the SCA. |
A. Routing outside the SCA |
| Since the transmission power is proportional to the volume of data, it is important to reduce the volume of data
that enters the SCA; this can be achieved by using a data compression mechanism. If there is any relationship among data,
it can be aggregated and compressed; the compression ratio depends on the correlation of the data and can be derived from
the maximum mutual information. In the case of environmental monitoring which collects information on temperature,
humidity, and atmospheric pressure, it has been widely known that data collected from neighboring areas have a strong
correlation that can lead to high compression ratio. From the above discussion, HYMN employs a hierarchical multi-hop
routing which is an appropriate strategy to perform efficient data compression to reduce the amount of data flowing into the
SCA. |
B. Routing inside the SCA |
| In the SCA, the most important aspect in routing is to minimize the power consumption while transferring the data
coming from outside the SCA to the sink node. Fortunately, this can be completely achieved by adopting a flat multi-hop
routing scheme in the SCA. |
C. Optimal location of hybrid boundary |
| Herein, we consider the optimal location of the hybrid boundary, where the employed routing algorithm is
switched from flat to hierarchical and vice versa. To derive the mathematical formulation representing the effect of the
hybrid boundary on the performance of power consumption in the SCA. |
IV. ADVANCED HYBRID MULTI-HOP ROUTING PROTOCOL |
| This protocol is interconnecting the Flat multi-hop and hybrid multi-hop. Where in this approach the next cluster
head is search and found in the same range of height in the plane. In the flat multi-hop the source to destination the distance
is fixed and the time is more. In the hybrid multi-hop finding the cluster head in zic-zac manner and the distance is
unknown and time is also unknown. So when we combine both approaches the time, distance are known and the searching
is happening in a certain height and certain range, so we are saving the energy and power consumption. Hence we are
saving the life time of the network |
V.NETWORK MODEL |
| In this paper, we propose to use the hybrid architecture to achieve both adaptively to changing traffic and low
channel switching overhead. Let G (V, E) be the network topology, where V is the set of mesh routers and E represents
pairs of mesh routers that are within radio communication range. Assume each mesh router has multiple interfaces. In the
hybrid architecture, we let one interface of each mesh router be able to switch channel frequently, and let the other
interfaces work on fixed channels. In the rest of this paper, we call the former interface as dynamic interface, and the latter
as static interface. Fig. 1 illustrates a hybrid multichannel multiradio wireless mesh network. Most mesh nodes including
the =gateway have 3 interfaces, and a few boundary nodes (c, g, I, f) have 2 interfaces. For each mesh node, one interface
works as dynamic interface, and the others work as static interfaces. The channel allocation of static interfaces aims at
maximizing the throughput from end-users to gateways, which usually constitutes a major portion of the traffic in the
network. Some heuristic algorithms, such as [10], could be used. In the algorithm, a load balanced tree is constructed for
each gateway. The goal of the tree construction is to allocate bandwidth fairly to each user with regard to the user-gateway
throughput. After the topology has been constructed, each link can then be assigned channels. |
| The links closer to the gateways are given higher priority to be allocated with less congested channels. In Fig. 1,
the tree topology is shown in bold lines, and we call these links as static links. The channels assigned to static links are also
shown in the figure. Dynamic interfaces work in an on-demand fashion. Two dynamic interfaces that are within radio
transmission range of each other are able to negotiate a common channel and communicate when they have data to
transmit. We call these links as dynamic links. Fig. 1 illustrates all possible dynamic links in dotted lines. As we are
considering hybrid architecture, the dynamic channel allocation should be aware of the interference from the static
interfaces, which requires some additional considerations in the protocol design. |
| • All dynamic interfaces need to negotiate on a default channel in each control interval. As the control interval is
very important for dynamic channel allocation, we should eliminate the interference from static interfaces in this phase.
Thus, we exclude this default channel in the channel allocation of the static interfaces. Note that this will not harm the
channel usage efficiency of the whole system, because dynamic interfaces can still use this default channel in the data
interval. |
| • The data channel is determined by choosing the “least congested channel” in the neighborhood of
the communicating pair. As dynamic interfaces negotiate on the same default channel in each control interval, each
interface can be aware of the channel usage of their neighboring dynamic interfaces by listening on the default channel for
their communication demands. However, they cannot know the channel usage of nearby static interfaces in the same way.
To solve this “hidden terminal problem” in the multichannel environment, we can take advantage of the static nature of
wireless mesh networks. As wireless mesh networks usually have static topology, each mesh node can maintain a stable list
of other mesh nodes within its interference range. (Note that the list of interfering neighbors may change due to the change
in the radio environments. However, it is not varying frequently, so we can assume that it is stable in a period of time. The
list can be updated by rediscovering the radio environment periodically.) We can let each mesh node measure the channel
usage of its static interfaces periodically, and send this information to all the other mesh nodes within its interference range.
Thus, the dynamic interface of each mesh node is able to know the channel usage of static interfaces within its interference
range. There are several advantages of using this hybrid architecture. |
| • If purely static channel allocation strategies are used, the connectivity of the original network topology may be
degraded; this can lead to suboptimal routing paths. By using one dynamic interface in each mesh node, the connectivity of
the network topology can be well maintained, because each dynamic interface is able to communicate with any other
interface within radio transmission range. |
| • Purely static channel allocation strategies cannot adapt to the frequently changing network traffic. For example, in
the tree topology, if the users of the left subtree happen to generate a much larger amount of traffic than the users in the
right subtree, then the links in the left subtree get more congested. The use of dynamic links is able to direct traffic around
the congested areas and therefore achieve better load balance in the network. |
VI. BLOCK DIAGRAM FOR HYMN |
| Although flat multi-hop routing algorithms enable routing of data in a fashion that minimizes the power
consumption of the WSN, they fail to exploit the data aggregation opportunities by virtue of data collected from the WSN.
In many WSN applications with the relatively high node density, the data collected by individual nodes are highly
redundant, thus making data aggregation a very attractive scheme in WSNs. Hierarchical multi-hop routing algorithms aim
to capitalize on the highly correlated nature of WSN’s collected data. We describe the operation of the most notable
example of hierarchical multi-hop routing algorithms, dubbed Low-Energy Adaptive Clustering Hierarchy (LEACH), for
illustrative purposes. In LEACH, nodes are organized in a two-level hierarchy, where their roles differ according to which
level they belong to. That is, a node can be a Cluster Head (CH) or a Cluster Member (CM), and these roles are changeable
in a unit of time referred to as a round. To implement the Advanced HYMN routing approaches in Mesh Network to
improve the life time and QOS of the network through routing. Clustering approaches are used to increase the number of
nodes in network. CH (channel head) selection is based on dynamic (based on highest energy level in every node). To
reduce the routing distance through the clustering approaches (packets transfer from CH to Sink node (receiver node)). |
VII.PERFORMANCE EVALUATION |
| We performed simulations in NS2. The Hyacinth extension [11] was used to support multiple channels and
multiple interfaces per node in the simulator. We made further extensions to support dynamic channel switching on each
interface. In this section, we first evaluate ADCA by comparing it with MMAC. After that, we evaluate the hybrid wireless
mesh network and compare it with purely static and dynamic architecture. Evaluation is based on impact of number of
nodes and impact of number of channel also the impact of number of interfaces. To clarify the influence of the hybrid
boundary location on the performance of HYMN, we conducted simulations by varying the distance, r, between the sink
node and the hybrid boundary, and examined the changes in the power consumption in the SCA. To evaluate the
performance of HYMN with respect to both flat and hierarchical multi-hop routing algorithms, the transmission distance of
each node and the amount of traffic relayed by each node in each strategy is investigated. |
VIII. CONCLUSION |
| In this paper, we have proposed a hybrid wireless mesh network architecture, where each mesh node has both
static and dynamic interfaces. We consider a WSN design that is practical, and low cost. We propose Advanced Hybrid
Multi-hop routing (AHYMN) to mitigate the impact of sink node isolation. To our best knowledge, this is the first design
that considers a hybrid multi-hop routing .Although flat multi-hop routing algorithms enable routing of data in a fashion
that minimizes the power consumption of the WSN, they fail to exploit the data aggregation opportunities by virtue of data
collected from the WSN. We proposed HYMN to mitigate this problem by combining flat and hierarchical multi-hop
routing algorithms. In many WSN applications with the relatively high node density, the data collected by individual nodes are highly redundant, thus making data aggregation a very attractive scheme in WSNs. Hierarchical multi-hop routing
algorithms aim to capitalize on the highly correlated nature of WSN’s collected data. We describe the operation of the most
notable example of hierarchical multi-hop routing algorithms, from that we can improve the routing efficiency and channel
allocation in cluster networks. The cluster head selection is obtained based on energy level and routing efficiency of the
network. |
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Figures at a glance |
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Figure 2 |
Figure 3 |
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References |
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