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Introduction
A wireless sensor network is a collection of sensor nodes used for various kinds of sensor information to a central controller or base station. A typical sensor network consists of sensing, transmission, reception, power and processing subsystems. In general the energy consumed by the communication subsystem is considered to be higher than that of other subsystems.We propose an algorithm for enhancing the life time of sensor network by reducing the power consumption by applying the benefits of data aggregation and state transition.
Extending network lifetime and sensor functionality is crucial for the successful utilization of wireless sensor networks (WSNs) in applications where replacing or charging energy storage units (i.e. batteries) is impractical or not cost effective.
However, many applications have requirements which make existing controlled mobility approaches infeasible.
We identify three key requirements.
1) The location of the nodes and the communication topology are not mutable because of coverage requirements. For example, in an environment monitoring application, the exact placement of sensor nodes may not be adjusted without compromising the monitoring coverage.
2) Nodes face differential power consumption where some nodes consume significantly more power than other nodes. For example, nodes
closer to the sink in a given routing topology often have to transmit more data and thus consume more power than nodes farther from the
sink in the given topology.
3) All nodes have similar, typically limited, sensing/communication/mobility capabilities. This rules out approaches that require a few nodes with extra capabilities and the ability to perform complex motion planning.
An introduction to Markov decision processes has been given, and important extension models have been also reviewed. Then, many designs of the Markov decision process in wireless sensor networks have been discussed including data exchange and topology formation, resource and power optimization, area coverage and event tracking solutions, and security and intrusion detection methods.
In Section II consider the literature survey of based paper or few reference paper and discover all dis-advantage of all these paper. In Section III to be consider for proposed system to avoid all dis-advantage to arise in literature survey also involved proposed system architecture, algorithms and
mathematical module.In Section IV consider Result of these system and discuss these result and finally acknowledgment for all supported persons.
2 LITERATURE SURVEY
Paper Title ” ”
In this section, we present an overview of the components, network model and assumptions.
A. Network Components Fig. 1 gives an illustration of the network we consider. SenCars perform wireless energy replenishment for sensor nodes one after another following a recharge sequence. Sensory data is
generated at nodes and delivered to the Base Station in a multihop fashion. The base station also collects energy information periodically and it is where battery replacement for the SenCars is conducted. In the Adaptive Algorithm, the base station performs network partition and informs the SenCars of recharge
requests in their partitions using long range radios. Besides data collection, sensors also monitor a number of targets that appear randomly in the sensing field, stay at a location for a random time before disappearing (e.g., meteorologic phenomena such as lightening).
Related work:
Energy-efficient communication in WSNs has received significant attention in recent years. The following summarizes the research literature that is more closely related to our proposed solution. We first present general approaches related to lifetime maximization and load balancing, then focus on schemes specifically targeted to data collection trees.
Several approaches have been proposed for extending the lifetime of a network. In general, they can be classified into four main groups: duty cycling, data reduction, topology control and controlled mobility.
The problem of maximizing the network lifetime under sensing coverage constraints was considered in [12] and [13].Specifically, the work in [12] addressed -coverage of a target field and employed an energy-aware activation policy with provable lifetime guarantees. The work in [13], instead, considered the problem of maximizing the network lifetime under
joint network and target coverage as a maximum tree cover and proposed an efficient heuristic algorithm for scheduling active nodes. However, these works defined lifetime in terms of coverage, which is an application-specific characterization. In contrast, we take a network-oriented approach that is independent of a specific application, but general enough to be applied
in different scenarios. Several works have exploited multipath routing for energy efficient communication in WSNs. An energy-efficient node disjoint
Multi-path routing algorithm was proposed in to establish multiple collision-free paths between a source and a sink through joint power control and flooding. A traffic adaptive routing algorithm was proposed in [15] based on the traffic loads of neighboring nodes and their distance from the destination.
There exists a rich literature showing plenty of clustering algorithms in the field of WSN. These clustering algorithms can be classified on basis of different criteria like number of nodes per cluster, cluster count, clustering is centralized or distributed, parameters for cluster head selection such as weight, residual energy, node distance, probability, degree,
location, etc.
One of the parameters for classification of clustering algorithms is the residual energy of a sensor node. Other synonyms for residual energy are remaining energy, leftover energy. Some of the algorithms that consider residual energy as the parameter for CH selection and cluster formation are
mentioned below. Low Energy Adaptive Clustering Hierarchy-Centralized
(LEACH-C) protocol is an enhancement of LEACH.
B. Cluster Head Selection
The first step towards clustering is Cluster Head (CH) selection. CH is selected on the basis of current residual energy. Network deployment is considered as manual, thus, the geographical location of each node is known. Clusters are formed within each zone. A node is said to be in range of the
other if distance between them is less than their transmission range. Such nodes are referred to as neighbor nodes. CH selection flow diagram is shown in Figure 3.The steps in CH selection are as follows:
1) After zone formation, degree (Number of neighbors) for each node is calculated.
2) Then Residual energy of each node is found.
3) Initially, nodes with highest residual energy become CH, provided that its degree is not zero; i.e. Degree must be greater than or equal to 1.
Virtual Scheduling Graph (VSG) based algorithm is a type of centralised approximation algorithm of Virtual Backbone Scheduling (VSB) technique as in [1] in which structure and energy of network is modelled together. In this algorithm, each sensor node consume fixed amount of energy and then formed as isolated nodes in each round.
Due to these characteristics, it preserves the connectivity
of network. Further preservation of connectivity can be done
using Local Replacement algorithm. Thus we combine these
algorithms as Virtual Scheduling Backbone Replacement
(VSBR) algorithm to improve the lifetime of network by
preserving the connectivity of network.
Generally, random replacement of sensor node without
any condition causes collision and packet loss mainly during
the sharing of channel while transmission takes place. Thus in our proposed algorithm, we place the replacement scheme based on energy level of sensor node using switching probability mainly to avoid collision and loss during transmission takes place.
Conclusion and future work:
In this paper have provided an extensive literature review related to the
Energy efficiency and improving the lifetime of network topology of wireless Sensor Network.Many wireless communications needs Energy efficient communication for various applications to improve life span of the network. Wireless Sensor Networks plays a main role in sensing and monitoring application in various fields. Motivated by the practical needs in data sharing,we discussed about proposed node residual energy and node distance based algorithm for clustering of nodes to minimize the average energy consumption in WSN.Finally, the paper has discussed about a few interesting research directions.
As future work, we will further evaluate the energy consumption for ESCP in various complex environments and with different algorithms.