Low power and limited processing are characteristics of nodes in Wireless sensor networks. Therefore, optimal
consumption of energy for WSN protocols seems essential. In a number of WSN applications, sensor nodes sense data
periodically from environment and transfer it to the sink. Because of limitation in energy and selection of best route,
for the purpose of increasing network remaining energy a node with most energy level will be used for transmission
of data. The most part of energy in nodes is wasted on radio transmission; thus decreasing number of transferred
packets in the network will result in increase in node and network lifetimes. In algorithms introduced for data
transmission in such networks up to now, a single route is used for data transmissions that results in decrease in
energy of nodes located on this route which in turn results in increasing of remaining energy. In this paper a new
method is proposed for selection of data transmission route that is able to solve this problem. This method is based
on learning automata that selects the route with regard to energy parameters and the distance to sink. In this method
energy of network nodes finishes rather simultaneously preventing break down of network into two separate parts.
This will result in increased lifetime. Simulation results show that this method has been very effective in increasing of
remaining energy and it increases network lifetime.
Optimized sensor nodes by fault node recovery algorithmeSAT Journals
Abstract This paper proposes fault node recovery algorithm to enhance the lifetime of wireless sensor networks when some of the sensor nodes shut down due to absence of battery power. The proposed algorithm combined Grade diffusion algorithm with genetic algorithm. The algorithm can result replacement of fewer sensor nodes and more reused routing paths. The proposed algorithm increases the number of active nodes, reduces dataloss during transmission, and reduces energy consumption. Keywords: Wireless Sensor Networks, Energy Consumption, Fault Node Recovery.
Abstract In optical circuit switching the high values of blocking probability is resolved by dynamic wavelength routing algorithms with wavelength conversion. The aim of this paper is to study these algorithms. Then the algorithm is selected which gives good results with and without wavelength conversion. The selected algorithm is then checked for other parameters of networking namely throughput, packet delivery ratio, and delay. A comparative study is then carried out for increasing traffic. We try to prove that these algorithms satisfy the criteria of QoS parameters by this comparative study. The results of simulation show that the parameters follow the trend of blocking probability of the selected algorithm. Keywords: optical burst switching, throughput, packet delivery ratio, delay.
False Node Recovery Algorithm for a Wireless Sensor NetworkRadita Apriana
This paper proposes a fault node recovery algorithm to enhance the lifetime of a wireless sensor
network when some of the sensor nodes shut down. The algorithm is based on the grade diffusion algorithm
combined with the genetic algorithm. The algorithm can result in fewer replacements of sensor nodes and
more reused routing paths. In our simulation, the proposed algorithm increases the number of active nodes
up to 8.7 times, reduces 98.8%, and reduces the rate of energy consumption by approximately 31.1%.
Performance analysis of dwdm based fiber optic communication with different m...eSAT Journals
Abstract Dense Wavelength Division multiplexing (DWDM) is a novel technology that can improve the channel capacity and meet growing demands for bandwidth of the optical fiber communication system. This technology utilizes a composite optical signal carrying multiple information streams. Each information streams transmitted on a distinct optical wavelength onto a single fiber. The performance of DWDM is degraded by non-linear optical effects. They are Cross phase modulation (XPM), Self phase modulation (SPM), four wave mixing (FWM), stimulated brillouin scattering (SBS) and stimulated Raman scattering (SRS). In this paper we analyze the performance of Dense Wavelength Division Multiplexing (DWDM) based fiber optic communication system at different modulation schemes, various power level and different number of data channels. we use the dispersion compensation fiber along with single mode fiber (SMF) for length of 100km at 1550nm to reduce the dispersion of optical signal. The performance of improved detected signals has been evaluated by the analysis of Quality factor and bit error rate (BER). The simulation studies are carried out using optisystem software from optiwave. Keywords: DWDM, cross-phase modulation, self-phase modulation, four wave mixing, stimulated Raman scattering, dispersion compensation fiber, NRZ, RZ, EDFA.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Optimization of workload prediction based on map reduce frame work in a cloud...eSAT Journals
Abstract Nowadays cloud computing is emerging Technology. It is used to access anytime and anywhere through the internet. Hadoop is an open-source Cloud computing environment that implements the Googletm MapReduce framework. Hadoop is a framework for distributed processing of large datasets across large clusters of computers. This paper proposes the workload of jobs in clusters mode using Hadoop. MapReduce is a programming model in hadoop used for maintaining the workload of the jobs. Depend on the job analysis statistics the future workload of the cluster is predicted for potential performance optimization by using genetic algorithm. Key Words: Cloud computing, Hadoop Framework, MapReduce Analysis, Workload
Optimized sensor nodes by fault node recovery algorithmeSAT Journals
Abstract This paper proposes fault node recovery algorithm to enhance the lifetime of wireless sensor networks when some of the sensor nodes shut down due to absence of battery power. The proposed algorithm combined Grade diffusion algorithm with genetic algorithm. The algorithm can result replacement of fewer sensor nodes and more reused routing paths. The proposed algorithm increases the number of active nodes, reduces dataloss during transmission, and reduces energy consumption. Keywords: Wireless Sensor Networks, Energy Consumption, Fault Node Recovery.
Abstract In optical circuit switching the high values of blocking probability is resolved by dynamic wavelength routing algorithms with wavelength conversion. The aim of this paper is to study these algorithms. Then the algorithm is selected which gives good results with and without wavelength conversion. The selected algorithm is then checked for other parameters of networking namely throughput, packet delivery ratio, and delay. A comparative study is then carried out for increasing traffic. We try to prove that these algorithms satisfy the criteria of QoS parameters by this comparative study. The results of simulation show that the parameters follow the trend of blocking probability of the selected algorithm. Keywords: optical burst switching, throughput, packet delivery ratio, delay.
False Node Recovery Algorithm for a Wireless Sensor NetworkRadita Apriana
This paper proposes a fault node recovery algorithm to enhance the lifetime of a wireless sensor
network when some of the sensor nodes shut down. The algorithm is based on the grade diffusion algorithm
combined with the genetic algorithm. The algorithm can result in fewer replacements of sensor nodes and
more reused routing paths. In our simulation, the proposed algorithm increases the number of active nodes
up to 8.7 times, reduces 98.8%, and reduces the rate of energy consumption by approximately 31.1%.
Performance analysis of dwdm based fiber optic communication with different m...eSAT Journals
Abstract Dense Wavelength Division multiplexing (DWDM) is a novel technology that can improve the channel capacity and meet growing demands for bandwidth of the optical fiber communication system. This technology utilizes a composite optical signal carrying multiple information streams. Each information streams transmitted on a distinct optical wavelength onto a single fiber. The performance of DWDM is degraded by non-linear optical effects. They are Cross phase modulation (XPM), Self phase modulation (SPM), four wave mixing (FWM), stimulated brillouin scattering (SBS) and stimulated Raman scattering (SRS). In this paper we analyze the performance of Dense Wavelength Division Multiplexing (DWDM) based fiber optic communication system at different modulation schemes, various power level and different number of data channels. we use the dispersion compensation fiber along with single mode fiber (SMF) for length of 100km at 1550nm to reduce the dispersion of optical signal. The performance of improved detected signals has been evaluated by the analysis of Quality factor and bit error rate (BER). The simulation studies are carried out using optisystem software from optiwave. Keywords: DWDM, cross-phase modulation, self-phase modulation, four wave mixing, stimulated Raman scattering, dispersion compensation fiber, NRZ, RZ, EDFA.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Optimization of workload prediction based on map reduce frame work in a cloud...eSAT Journals
Abstract Nowadays cloud computing is emerging Technology. It is used to access anytime and anywhere through the internet. Hadoop is an open-source Cloud computing environment that implements the Googletm MapReduce framework. Hadoop is a framework for distributed processing of large datasets across large clusters of computers. This paper proposes the workload of jobs in clusters mode using Hadoop. MapReduce is a programming model in hadoop used for maintaining the workload of the jobs. Depend on the job analysis statistics the future workload of the cluster is predicted for potential performance optimization by using genetic algorithm. Key Words: Cloud computing, Hadoop Framework, MapReduce Analysis, Workload
In Cognitive Radio Networks (CRN), Cooperative Spectrum Sensing (CSS) is
used to improve performance of spectrum sensing techniques used for detection of licensed
(Primary) user’s signal. In CSS, the spectrum sensing information from multiple unlicensed
(Secondary) users are combined to take final decision about presence of primary signal. The
mixing techniques used to generate final decision about presence of PU’s signal are also
called as Fusion techniques / rules. The fusion techniques are further classified as data
fusion and decision fusion techniques. In data fusion technique all the secondary users
(SUs) share their raw information of spectrum detection like detected energy or other
statistical information, while in decision fusion technique all the SUs take their local
decisions and share the decision by sending ‘0’ or ‘1’ corresponding to absence and presence
of PU’s signal respectively. The rules used in decision fusion techniques are OR rule, AND
rule and K-out-of-N rule. The CSS is further classified as distributed CSS and centralized
CSS. In distributed CSS all the SUs share the spectrum detection information with each
other and by mixing the shared information; all the SUs take final decision individually. In
centralized CSS all the SUs send their detected information to a secondary base station /
central unit which combines the shared information and takes final decision. The secondary
base station shares the final decision with all the SUs in the CRN. This paper covers
overview of information fusion methods used for CSS and analysis of decision fusion rules
with simulation results.
Wireless sensor networks (WSN) have been widely used in various applications.
In these networks nodes collect data from the attached sensors and send their data to a base
station. However, nodes in WSN have limited power supply in form of battery so the nodes
are expected to minimize energy consumption in order to maximize the lifetime of WSN. A
number of techniques have been proposed in the literature to reduce the energy
consumption significantly. In this paper, we propose a new clustering based technique
which is a modification of the popular LEACH algorithm. In this technique, first cluster
heads are elected using the improved LEACH algorithm as usual, and then a cluster of
nodes is formed based on the distance between node and cluster head. Finally, data from
node is transferred to cluster head. Cluster heads forward data, after applying aggregation,
to the cluster head that is closer to it than sink in forward direction or directly to the sink.
This reduction in distance travelled improves the performance over LEACH algorithm
significantly.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Energy efficient data communication approach in wireless sensor networksijassn
Wireless sensor network has a vast variety of applications. The adoption of energy efficient cluster-based
configuration has many untapped desirable benefits for the WSNs. The limitation of energy in a sensor
node creates challenges for routing in WSNs. The research work presents the organized and detailed
description of energy conservation method for WSNs. In the proposed method reclustering and multihop
data transmission processes are utilized for data reporting to base station by sensor node. The accurate use
of energy in WSNs is the main challenge for exploiting the network to the full extent. The main aim of the
proposed method is that by evenly distributing the energy all over the sensor nodes and by reducing the
total energy dissipation, the lifetime of the network is enhanced, so that the node will remain alive for
longer times inside the cluster. The result shows that the proposed clustering approach has higher stable
region and network life time than Topology-Controlled Adaptive Clustering (TCAC) and Low-Energy
Adaptive Clustering Hierarchy (LEACH) for WSNs.
The main aim of energy efficient routing is to
minimize the energy required to transmit or receive packets also
called as active communication energy. Inactive energy is the
energy which not only tries to reduce the energy consumed
when a mobile node stays idle but also listens to the wireless
medium for any possible communication requests from other
nodes. To conserve energy, many energy efficient routing
protocols have been proposed. Networks of small, inexpensive,
disposable, smart sensors are emerging as a new technology with
tremendous potential. Wireless sensor networks can be randomly
deployed inside or close to phenomenon to be monitored. The
advantage of these networks is the fact that they are selfconfiguring,
which means that a sensor network can be deployed
randomly on a battlefield, in a disaster area or in an inaccessible
area without the need for human intervention. The energy
supplies of nodes are not replenished or replaced and therefore
nodes only participate in the network for as long as they have
energy. This fact necessitates energy efficiency in the design of
every aspect of such nodes. Energy consumption in sensor nodes
occurs mainly due to computational processing and, to a greater
extent, communication. The routing protocol employed by these
sensor nodes can minimize the number of transmissions that
nodes make as well as the computational complexity of routing
path selection. It is therefore of critical importance that the
routing protocol be designed with energy efficiency in mind.
Newton-raphson method to solve systems of non-linear equations in VANET perfo...journalBEEI
Nowadays, Vehicular Ad-Hoc Network (VANET) has got more attention from the researchers. The researchers have studied numerous topics of VANET, such as the routing protocols of VANET and the MAC protocols of VANET. The aim of their works is to improve the network performance of VANET, either in terms of energy consumption or packet delivery ratio (PDR) and delay. For this research paper, the main goal is to find the coefficient of a, b and c of three non-linear equations by using a Newton- Raphson method. Those three non-linear equations are derived from a different value of Medium Access Control (MAC) protocol's parameters. After that, those three coefficient is then will be used in optimization of the VANET in terms of energy, PDR, and delay.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Wavelet energy moment and neural networks based particle swarm optimisation f...journalBEEI
In this study, a combined approach of discrete wavelet transform analysis and a feed forward neural networks algorithm to detect and classify transmission line faults. The proposed algorithm uses a multi -resolution analysis decoposition of three-phasecurrents only to calculate the wavelet energy moment of detailed coefficients. In comparison with the energy spectrum, the energy moment could reveal the energy distribution features better, which is beneficial when extracting signal features. Theapproach use particle swarm optimization algorithm to train a feed forward neural network. The goal is the enhancement of the convergence rate, learning process and fill up the gap of local minimum point.The purposed scheme consists of two FNNs, one for detecting and another for classifying all the ten types of faults using Matlab/Simulink. The proposed algorithm have been extensively tested on a system 400 kV, 3 phases, 100 km line consideringvarious fault parameter variations.
ZigBee has been developed to support lower data rates and low power consuming
applications. This paper targets to analyze various parameters of ZigBee physical (PHY).
Performance of ZigBee PHY is evaluated on the basis of energy consumption in
transmitting and receiving mode and throughput. Effect of variation in network size is
studied on these performance attributes. Some modulation schemes are also compared and
the best modulation scheme is suggested with tradeoffs between different performance
metrics.
DESIGNING AN ENERGY EFFICIENT CLUSTERING IN HETEROGENEOUS WIRELESS SENSOR NET...IJCNCJournal
Designing an energy-efficient scheme in a Heterogeneous Wireless Sensor Network (HWSN) is a critical
issue that degrades the network performance. Recharging and providing security to the sensor devices is
very difficult in an unattended environment once the energy is drained off. A Clustering scheme is an
important and suitable approach to increase energy efficiency and transmitting secured data which in turn
enhances the performance in the network. The proposed algorithm Energy Efficient Clustering (EEC)
works for optimum energy utilization in sensor nodes. The algorithm is proposed by combining the
rotation-based clustering and energy-saving mechanism for avoiding the node failure and prolonging the
network lifetime. This shows MAC layer scheduling is based on optimum energy utilization depending on
the residual energy. In the proposed work, a densely populated network is partitioned into clusters and all
the cluster heads are formed at a time and selected on rotation based on considering the highest energy of
the sensor nodes. Other cluster members are accommodated in a cluster based on Basic Cost Maximum
flow (BCMF) to allow the cluster head for transmitting the secured data. Carrier Sense Multiple Access
(CSMA), a contention window based protocol is used at the MAC layer for collision detection and to
provide channel access prioritization to HWSN of different traffic classes with reduction in End to End
delay, energy consumption, and improved throughput and Packet delivery ratio(PDR) and allowing the
cluster head for transmission without depleting the energy. Simulation parameters of the proposed system
such as Throughput, Energy, and Packet Delivery Ratio are obtained and compared with the existing
system.
Energy Efficient Multipath Routing For Mobile Ad Hoc NetworksZac Darcy
Energy consumption is a significant issue in ad hoc networks since mobile nodes are battery powered. In
order to prolong the lifetime of ad hoc networks, it is the most critical issue to minimize the energy
consumption of nodes. In this paper, we propose an energy efficient multipath routing protocol for
choosing energy efficient path. This system also considers transmission power of nodes and residual energy
as energy metrics in order to maximize the network lifetime and to reduce energy consumption of mobile
nodes. The objective of our proposed system is to find an optimal route based on two energy metrics while
choosing a route to transfer data packets. This system is implemented by using NS-2.34. Simulation results
show that the proposed routing protocol with transmission power and residual energy control mode can
extend the life-span of network and can achieve higher performance when compared to traditional ad-hoc
on-demand multipath distance vector (AOMDV) routing protocol.
Energy Efficient Multipath Routing For Mobile Ad Hoc NetworksZac Darcy
Energy consumption is a significant issue in ad hoc networks since mobile nodes are battery powered. In
order to prolong the lifetime of ad hoc networks, it is the most critical issue to minimize the energy
consumption of nodes. In this paper, we propose an energy efficient multipath routing protocol for
choosing energy efficient path. This system also considers transmission power of nodes and residual energy
as energy metrics in order to maximize the network lifetime and to reduce energy consumption of mobile
nodes. The objective of our proposed system is to find an optimal route based on two energy metrics while
choosing a route to transfer data packets. This system is implemented by using NS-2.34. Simulation results
show that the proposed routing protocol with transmission power and residual energy control mode can
extend the life-span of network and can achieve higher performance when compared to traditional ad-hoc
on-demand multipath distance vector (AOMDV) routing protocol.
Investigation of Ant Colony Optimization Algorithm for Efficient Energy Utili...IJCNCJournal
Maintaining the energy conservation is considered as an important approach to increase the lifetime of WSN. In fact, an energy reduction mechanism is considered as the main concept to enhance the lifespan of the network. In this paper, the performance analysis/evaluation of optimization technique, specifically, Ant Colony Optimization (ACO) and modified ACO (m-ACO) in the routing method are investigated. This network analysis is done by 100 iterations and differentiated with 50, 75 and 100 numbers of nodes. Finally, experimental results illustrate that the performance of m-ACO algorithm obtained the obvious performance, which is comparatively better than ACO algorithm, because it improves the routing efficiency by pheromone evaporation control and energy threshold value. It demonstrates that m-ACO algorithm gives better results than ACO in terms of throughput (1.41%), transmission delay (1.43%), packet delivery ratio (1.41%), energy consumption (2.05%), and the packet loss (9.70%). The convergence rate is analysed for ACO and m-ACO algorithms with respect to 100 number of iterations for WSNs.
Investigation of Ant Colony Optimization Algorithm for Efficient Energy Utili...IJCNCJournal
Maintaining the energy conservation is considered as an important approach to increase the lifetime of WSN. In fact, an energy reduction mechanism is considered asthe main concept to enhance the lifespan of the network. In this paper, the performance analysis/evaluation of optimization technique, specifically, Ant Colony Optimization (ACO) and modified ACO (m-ACO) in the routing method are investigated. This network analysis is done by 100 iterations and differentiated with 50, 75 and 100 numbers of nodes. Finally, experimental results illustrate that the performance of m-ACO algorithm obtained the obvious performance,which is comparatively better than ACO algorithm, because it improves the routing efficiency by pheromone evaporation control and energy threshold value. It demonstrates that m-ACO algorithm gives better results than ACO in terms of throughput (1.41%), transmission delay (1.43%), packet delivery ratio (1.41%), energy consumption (2.05%), and the packet loss (9.70%). The convergence rate is analysed for ACO and m-ACO algorithms with respect to 100 number of iterations for WSNs.
Address-light and energy aware routing protocol for wireless sensor networkTELKOMNIKA JOURNAL
In recent years, sensor networks applications were used in many criteria. Because of its vast applications, so many researchers studied these networks. Wireless sensor networks contain significant number of sensor nodes; they are suitable medium for collecting and sending data or informing the sink about an event. This study presents a new integrated method for routing in sensor networks which is based on remaining energy of the nodes and existing space between each node and the sink. This method is more suitable for large and medium volume of data. Lifetime enhancement of the network is the main purpose of this method which is obtained by fair division of nodes roles in transmission of data to the sink. In ALERP algorithm (Address Light, Label-Based and Energy-Aware Routing Protocol), in order to reduce network overload and energy consuming as well as enhancement of network lifetime, we used predetermined routes as well as routing based on packet labels. Energy consumption in the entire network is another advantage of this method. The existence of various parameters on this algorithm will lead to more flexibility of it. Generally, conducted simulations indicate higher uniformity in energy consumption of nodes.
In Cognitive Radio Networks (CRN), Cooperative Spectrum Sensing (CSS) is
used to improve performance of spectrum sensing techniques used for detection of licensed
(Primary) user’s signal. In CSS, the spectrum sensing information from multiple unlicensed
(Secondary) users are combined to take final decision about presence of primary signal. The
mixing techniques used to generate final decision about presence of PU’s signal are also
called as Fusion techniques / rules. The fusion techniques are further classified as data
fusion and decision fusion techniques. In data fusion technique all the secondary users
(SUs) share their raw information of spectrum detection like detected energy or other
statistical information, while in decision fusion technique all the SUs take their local
decisions and share the decision by sending ‘0’ or ‘1’ corresponding to absence and presence
of PU’s signal respectively. The rules used in decision fusion techniques are OR rule, AND
rule and K-out-of-N rule. The CSS is further classified as distributed CSS and centralized
CSS. In distributed CSS all the SUs share the spectrum detection information with each
other and by mixing the shared information; all the SUs take final decision individually. In
centralized CSS all the SUs send their detected information to a secondary base station /
central unit which combines the shared information and takes final decision. The secondary
base station shares the final decision with all the SUs in the CRN. This paper covers
overview of information fusion methods used for CSS and analysis of decision fusion rules
with simulation results.
Wireless sensor networks (WSN) have been widely used in various applications.
In these networks nodes collect data from the attached sensors and send their data to a base
station. However, nodes in WSN have limited power supply in form of battery so the nodes
are expected to minimize energy consumption in order to maximize the lifetime of WSN. A
number of techniques have been proposed in the literature to reduce the energy
consumption significantly. In this paper, we propose a new clustering based technique
which is a modification of the popular LEACH algorithm. In this technique, first cluster
heads are elected using the improved LEACH algorithm as usual, and then a cluster of
nodes is formed based on the distance between node and cluster head. Finally, data from
node is transferred to cluster head. Cluster heads forward data, after applying aggregation,
to the cluster head that is closer to it than sink in forward direction or directly to the sink.
This reduction in distance travelled improves the performance over LEACH algorithm
significantly.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Energy efficient data communication approach in wireless sensor networksijassn
Wireless sensor network has a vast variety of applications. The adoption of energy efficient cluster-based
configuration has many untapped desirable benefits for the WSNs. The limitation of energy in a sensor
node creates challenges for routing in WSNs. The research work presents the organized and detailed
description of energy conservation method for WSNs. In the proposed method reclustering and multihop
data transmission processes are utilized for data reporting to base station by sensor node. The accurate use
of energy in WSNs is the main challenge for exploiting the network to the full extent. The main aim of the
proposed method is that by evenly distributing the energy all over the sensor nodes and by reducing the
total energy dissipation, the lifetime of the network is enhanced, so that the node will remain alive for
longer times inside the cluster. The result shows that the proposed clustering approach has higher stable
region and network life time than Topology-Controlled Adaptive Clustering (TCAC) and Low-Energy
Adaptive Clustering Hierarchy (LEACH) for WSNs.
The main aim of energy efficient routing is to
minimize the energy required to transmit or receive packets also
called as active communication energy. Inactive energy is the
energy which not only tries to reduce the energy consumed
when a mobile node stays idle but also listens to the wireless
medium for any possible communication requests from other
nodes. To conserve energy, many energy efficient routing
protocols have been proposed. Networks of small, inexpensive,
disposable, smart sensors are emerging as a new technology with
tremendous potential. Wireless sensor networks can be randomly
deployed inside or close to phenomenon to be monitored. The
advantage of these networks is the fact that they are selfconfiguring,
which means that a sensor network can be deployed
randomly on a battlefield, in a disaster area or in an inaccessible
area without the need for human intervention. The energy
supplies of nodes are not replenished or replaced and therefore
nodes only participate in the network for as long as they have
energy. This fact necessitates energy efficiency in the design of
every aspect of such nodes. Energy consumption in sensor nodes
occurs mainly due to computational processing and, to a greater
extent, communication. The routing protocol employed by these
sensor nodes can minimize the number of transmissions that
nodes make as well as the computational complexity of routing
path selection. It is therefore of critical importance that the
routing protocol be designed with energy efficiency in mind.
Newton-raphson method to solve systems of non-linear equations in VANET perfo...journalBEEI
Nowadays, Vehicular Ad-Hoc Network (VANET) has got more attention from the researchers. The researchers have studied numerous topics of VANET, such as the routing protocols of VANET and the MAC protocols of VANET. The aim of their works is to improve the network performance of VANET, either in terms of energy consumption or packet delivery ratio (PDR) and delay. For this research paper, the main goal is to find the coefficient of a, b and c of three non-linear equations by using a Newton- Raphson method. Those three non-linear equations are derived from a different value of Medium Access Control (MAC) protocol's parameters. After that, those three coefficient is then will be used in optimization of the VANET in terms of energy, PDR, and delay.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Wavelet energy moment and neural networks based particle swarm optimisation f...journalBEEI
In this study, a combined approach of discrete wavelet transform analysis and a feed forward neural networks algorithm to detect and classify transmission line faults. The proposed algorithm uses a multi -resolution analysis decoposition of three-phasecurrents only to calculate the wavelet energy moment of detailed coefficients. In comparison with the energy spectrum, the energy moment could reveal the energy distribution features better, which is beneficial when extracting signal features. Theapproach use particle swarm optimization algorithm to train a feed forward neural network. The goal is the enhancement of the convergence rate, learning process and fill up the gap of local minimum point.The purposed scheme consists of two FNNs, one for detecting and another for classifying all the ten types of faults using Matlab/Simulink. The proposed algorithm have been extensively tested on a system 400 kV, 3 phases, 100 km line consideringvarious fault parameter variations.
ZigBee has been developed to support lower data rates and low power consuming
applications. This paper targets to analyze various parameters of ZigBee physical (PHY).
Performance of ZigBee PHY is evaluated on the basis of energy consumption in
transmitting and receiving mode and throughput. Effect of variation in network size is
studied on these performance attributes. Some modulation schemes are also compared and
the best modulation scheme is suggested with tradeoffs between different performance
metrics.
DESIGNING AN ENERGY EFFICIENT CLUSTERING IN HETEROGENEOUS WIRELESS SENSOR NET...IJCNCJournal
Designing an energy-efficient scheme in a Heterogeneous Wireless Sensor Network (HWSN) is a critical
issue that degrades the network performance. Recharging and providing security to the sensor devices is
very difficult in an unattended environment once the energy is drained off. A Clustering scheme is an
important and suitable approach to increase energy efficiency and transmitting secured data which in turn
enhances the performance in the network. The proposed algorithm Energy Efficient Clustering (EEC)
works for optimum energy utilization in sensor nodes. The algorithm is proposed by combining the
rotation-based clustering and energy-saving mechanism for avoiding the node failure and prolonging the
network lifetime. This shows MAC layer scheduling is based on optimum energy utilization depending on
the residual energy. In the proposed work, a densely populated network is partitioned into clusters and all
the cluster heads are formed at a time and selected on rotation based on considering the highest energy of
the sensor nodes. Other cluster members are accommodated in a cluster based on Basic Cost Maximum
flow (BCMF) to allow the cluster head for transmitting the secured data. Carrier Sense Multiple Access
(CSMA), a contention window based protocol is used at the MAC layer for collision detection and to
provide channel access prioritization to HWSN of different traffic classes with reduction in End to End
delay, energy consumption, and improved throughput and Packet delivery ratio(PDR) and allowing the
cluster head for transmission without depleting the energy. Simulation parameters of the proposed system
such as Throughput, Energy, and Packet Delivery Ratio are obtained and compared with the existing
system.
Energy Efficient Multipath Routing For Mobile Ad Hoc NetworksZac Darcy
Energy consumption is a significant issue in ad hoc networks since mobile nodes are battery powered. In
order to prolong the lifetime of ad hoc networks, it is the most critical issue to minimize the energy
consumption of nodes. In this paper, we propose an energy efficient multipath routing protocol for
choosing energy efficient path. This system also considers transmission power of nodes and residual energy
as energy metrics in order to maximize the network lifetime and to reduce energy consumption of mobile
nodes. The objective of our proposed system is to find an optimal route based on two energy metrics while
choosing a route to transfer data packets. This system is implemented by using NS-2.34. Simulation results
show that the proposed routing protocol with transmission power and residual energy control mode can
extend the life-span of network and can achieve higher performance when compared to traditional ad-hoc
on-demand multipath distance vector (AOMDV) routing protocol.
Energy Efficient Multipath Routing For Mobile Ad Hoc NetworksZac Darcy
Energy consumption is a significant issue in ad hoc networks since mobile nodes are battery powered. In
order to prolong the lifetime of ad hoc networks, it is the most critical issue to minimize the energy
consumption of nodes. In this paper, we propose an energy efficient multipath routing protocol for
choosing energy efficient path. This system also considers transmission power of nodes and residual energy
as energy metrics in order to maximize the network lifetime and to reduce energy consumption of mobile
nodes. The objective of our proposed system is to find an optimal route based on two energy metrics while
choosing a route to transfer data packets. This system is implemented by using NS-2.34. Simulation results
show that the proposed routing protocol with transmission power and residual energy control mode can
extend the life-span of network and can achieve higher performance when compared to traditional ad-hoc
on-demand multipath distance vector (AOMDV) routing protocol.
Investigation of Ant Colony Optimization Algorithm for Efficient Energy Utili...IJCNCJournal
Maintaining the energy conservation is considered as an important approach to increase the lifetime of WSN. In fact, an energy reduction mechanism is considered as the main concept to enhance the lifespan of the network. In this paper, the performance analysis/evaluation of optimization technique, specifically, Ant Colony Optimization (ACO) and modified ACO (m-ACO) in the routing method are investigated. This network analysis is done by 100 iterations and differentiated with 50, 75 and 100 numbers of nodes. Finally, experimental results illustrate that the performance of m-ACO algorithm obtained the obvious performance, which is comparatively better than ACO algorithm, because it improves the routing efficiency by pheromone evaporation control and energy threshold value. It demonstrates that m-ACO algorithm gives better results than ACO in terms of throughput (1.41%), transmission delay (1.43%), packet delivery ratio (1.41%), energy consumption (2.05%), and the packet loss (9.70%). The convergence rate is analysed for ACO and m-ACO algorithms with respect to 100 number of iterations for WSNs.
Investigation of Ant Colony Optimization Algorithm for Efficient Energy Utili...IJCNCJournal
Maintaining the energy conservation is considered as an important approach to increase the lifetime of WSN. In fact, an energy reduction mechanism is considered asthe main concept to enhance the lifespan of the network. In this paper, the performance analysis/evaluation of optimization technique, specifically, Ant Colony Optimization (ACO) and modified ACO (m-ACO) in the routing method are investigated. This network analysis is done by 100 iterations and differentiated with 50, 75 and 100 numbers of nodes. Finally, experimental results illustrate that the performance of m-ACO algorithm obtained the obvious performance,which is comparatively better than ACO algorithm, because it improves the routing efficiency by pheromone evaporation control and energy threshold value. It demonstrates that m-ACO algorithm gives better results than ACO in terms of throughput (1.41%), transmission delay (1.43%), packet delivery ratio (1.41%), energy consumption (2.05%), and the packet loss (9.70%). The convergence rate is analysed for ACO and m-ACO algorithms with respect to 100 number of iterations for WSNs.
Address-light and energy aware routing protocol for wireless sensor networkTELKOMNIKA JOURNAL
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MPC-EAR : Maximal Power Conserved And Energy Aware Routing in Ad hoc Networksijsrd.com
Power preservation in wireless ad hoc networks is a decisive factor as energy resources are inadequate at the electronic devices in use. Power-aware routing strategies are fundamentally route selection strategies built on accessible ad hoc routing protocols. This paper proposed a new Maximal Power Conserved And Energy Aware Routing (MPC-EAR ) topology for mobile ad hoc networks that enhances the network life span. Simulation results prove that the projected protocol has a higher performance other minimal energy usage, energy level aware and energy conserving routing protocols such as MTPR, MMECR and CMMECR.
A New Method for Reducing Energy Consumption in Wireless Sensor Networks usin...Editor IJCATR
Nowadays, wireless sensor networks, clustering protocol based on the neighboring nodes into separate clusters and fault
tolerance for each cluster exists for sensors to send information to the base station, to gain the best performance in terms of increased
longevity and maintain tolerance than with other routing methods. However, most clustering protocols proposed so far, only
geographical proximity (neighboring) cluster formation is considered as a parameter. In this study, a new clustering protocol and fault
tolerance based on the fuzzy algorithms are able to clustering nodes in sensor networks based on fuzzy logic and fault tolerance. This
protocol uses clustering sensor nodes and fault tolerance exist in the network to reduce energy consumption, so that faulty sensors
from neighboring nodes are used to cover the errors, work based on the most criteria overlay neighbor sensors with defective sensors,
distance neighbor sensors from fault sensor and distance neighbor sensors from central station is done. Superior performance of the
protocol can be seen in terms of increasing the network lifetime and maintain the best network tolerance in comparison with previous
protocols such as LEACH in the simulation results.
Wireless sensor networks, clustering, Energy efficient protocols, Particles S...IJMIT JOURNAL
Wireless sensor networks (WSN) is composed of a large number of small nodes with limited functionality.
The most important issue in this type of networks is energy constraints. In this area several researches have
been done from which clustering is one of the most effective solutions. The goal of clustering is to divide
network into sections each of which has a cluster head (CH). The task of cluster heads collection, data
aggregation and transmission to the base station is undertaken. In this paper, we introduce a new approach
for clustering sensor networks based on Particle Swarm Optimization (PSO) algorithm using the optimal
fitness function, which aims to extend network lifetime. The parameters used in this algorithm are residual
energy density, the distance from the base station, intra-cluster distance from the cluster head. Simulation
results show that the proposed method is more effective compared to protocols such as (LEACH, CHEF,
PSO-MV) in terms of network lifetime and energy consumption.
Quadrant Based DIR in CWin Adaptation Mechanism for Multihop Wireless NetworkIJCI JOURNAL
In Multihop Wireless Networks, traffic forwarding capability of each node varies according to its level of contention. Each node can yield its channel access opportunity to its neighbouring nodes, so that all the nodes can evenly share the channel and have similar forwarding capability. In this manner the wireless channel is utilized effectively, which is achieved using Contention Window Adaptation Mechanism (CWAM). This mechanism achieves a higher end-to-end throughout but consumes the network power to a higher level. So, a newly proposed algorithm Quadrant- Based Directional Routing Protocol (Q-DIR) is implemented as a cross-layer with CWAM, to reduce the total network power consumption through limited flooding and also reduce the routing overheads, which eventually increases overall network throughput. This algorithm limits the broadcast region to a quadrant where the source node and the destination nodes are located. Implementation of the algorithm is done in Linux based NS-2 simulator
A scalable and power efficient solution for routing in mobile ad hoc network ...ijmnct
Mobile Ad Hoc Network (MANET) is a very dynamic and infrastructure-less ad hoc network. The actual
network size depends on the application and the protocols developed for the routing for this kind of
networks should be scalable. MANET is a resource limited network and therefore the developed routing
algorithm for packet transmission should be power and bandwidth efficient. These kinds of dynamic
networks should operate with minimal management overhead. The management functionality of the
network increases with number of nodes and reduces the performance of the network. Here, in this paper,
we have designed all identical nodes in the cluster except the cluster head and this criterion reduces the
management burden on the network. Graph theoretic routing algorithm is used to develop route for packet
transmission by using the minimum resources. In this paper, we developed routing algorithm for cluster
based MANET and finds a path from source to destination using minimum cumulative degree path. Our
simulation results show that this routing algorithm provide efficient routing path with the increasing
number of nodes and uses multi-hop connectivity for intra-cluster to utilize minimum power for packet
transmission irrespective of number of nodes in the network.
Performance Evaluation of Consumed Energy-Type-Aware Routing (CETAR) For Wire...ijwmn
This work evaluates the performance of Consumed-Energy-Type-Aware Routing (CETAR) which incorporates the amount of energy consumed per type of operation for routing decision to extend the lifetime of the Wireless Sensor Networks (WSNs). CETAR makes routing decision using statistics of the energy consumed for each type of node activities including sensing, data processing, data transmission as a source node, and routing operations. In particular, CETAR encourages a node which seldom plays a role of source node as a routing node to preserve the energy of active source nodes to prolong the functionality of the WSNs. Extensive simulation study demonstrates that the lifetime of the Geographic and Energy Aware Routing (GEAR) can be significantly extended with CETAR. With its adaptability to deployed sensor node behaviors, the significance of CETAR to extend the lifetime of WSNs is clear.
M-EPAR to Improve the Quality of the MANETsIJERA Editor
In MANET, power aware is important challenge issue to improve the communication energy efficiency at individual nodes. We propose modified efficient power aware routing (M-EPAR), a new power aware routing protocol that increases the network lifetime of MANET. Designing a power aware routing algorithm or technique is one of the most important point considered in Mobile Ad Hoc Networks. These nodes are driven by reactive protocols where broadcasting is mandatory to form a path between two nodes. So in case of death of the node resulting out of less battery calls for re-routing. Since many existing techniques focuses on energy aware routing this paper presents combination of energy aware routing merged with link quality determined by packet drop rate. The proposed scheme has outperformed the existing technique in terms of packet delivery ratio, throughput and energy consumption.
IMPLEMENTING PACKET BROADCASTING ALGORITHM OF MIMO BASED MOBILE AD-HOC NETWOR...IJNSA Journal
With the rapid growth of wireless communication infras,,tructure over the recent few years, new challenges has been posed on the system and analysis on wireless adhoc networking. Implementation of MIMO communication in such type of network is enhancing the packet transmission capabilities. There are different techniques for cooperative transmission and broadcasting packet in MIMO equipped Mobile Adhoc Network. We have employed a model network in the OPNET environment and propose a new scheduling algorithm based on investigating the different broadcasting algorithm. The new broadcasting algorithm improves the packet transmission rate of the network based on energy performance of the network and minimizes the BER for different transmission mode which is illustrated in this paper. The simulations are done in MATLAB and OPNET environment and the simulated result for the packet transmission rate are collected and shown in the tabular form. Also simulate the network for generating a comparative statement for each mobile node. And performance analysis is also done for the model network. The main focus is to minimize BER and improve information efficiency of the network.
Nearest Adjacent Node Discovery Scheme for Routing Protocol in Wireless Senso...IOSR Journals
The broad significance of Wireless Sensor Networks is in most emergency and disaster rescue
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routing algorithm. It is plays an important role in energy conservation. It finds the best location of nearest
adjacent nodes by involving the least number of nodes in transmission of data and set large number of nodes to
sleep in idle mode. Based on simulation result we shows the significant improvement in energy saving and
enhance the life of the network
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IMPROVE ENERGY EFFICIENCY ROUTING IN WSN BY USING AUTOMATA
1. International Journal of Ad hoc, Sensor & Ubiquitous Computing( IJASUC ) Vol.1, No.2, June 2010.
10.5121/ijasuc.2010.1201 1
IMPROVE ENERGY EFFICIENCY ROUTING IN WSN
BY USING AUTOMATA
Elham Hajian and Kamal Jamshidi and Ali Bohlooli
Department of Computer Engineering, Isfahan University, Isfahan, Iran
e_hajian@eng.ui.ac.ir,jamshidi@eng.ui.ac.ir,bohlooli@eng.ui
.ac.ir
ABSTRACT
Low power and limited processing are characteristics of nodes in Wireless sensor networks. Therefore, optimal
consumption of energy for WSN protocols seems essential. In a number of WSN applications, sensor nodes sense data
periodically from environment and transfer it to the sink. Because of limitation in energy and selection of best route,
for the purpose of increasing network remaining energy a node with most energy level will be used for transmission
of data. The most part of energy in nodes is wasted on radio transmission; thus decreasing number of transferred
packets in the network will result in increase in node and network lifetimes. In algorithms introduced for data
transmission in such networks up to now, a single route is used for data transmissions that results in decrease in
energy of nodes located on this route which in turn results in increasing of remaining energy. In this paper a new
method is proposed for selection of data transmission route that is able to solve this problem. This method is based
on learning automata that selects the route with regard to energy parameters and the distance to sink. In this method
energy of network nodes finishes rather simultaneously preventing break down of network into two separate parts.
This will result in increased lifetime. Simulation results show that this method has been very effective in increasing of
remaining energy and it increases network lifetime.
KEYWORDS
WSN, energy, distance, learning automata
1. INTRODUCTION
A Wireless sensor network includes sensor nodes that are distributed in a geographical region to
show physical phenomena like temperature, vibrations, earthquake, etc[1,2]. Placement of nodes
in improper places and difficulty in changing or recharging batteries have made researchers to
do investigations on reduction of energy consumption [3]. Experiments show that transmission
of data takes more energy compared with processing data [4,5]. Energy Cost of transmitting one
separate bit of information equals to processing of thousands of functions in a sensor node [4,6].
Consumption of energy in sensing subsystems depends on sort of sensor. In many cases, the
energy used by processing and connective subsystems is considerable. Main sources of energy
waste in WSN include: idle listening, retransmission caused by injury, control packet overhead,
transmission of data to sleeping node. Half-optimal utilization from available resources, and
traffic changes and fluctuations.
In this paper in order to increase network lifetime, energy waste in half-optimal utilization of
resources is prevented. This is done via energy conservation technique in network unit. This
paper uses an optimal route for data transmission from sensor node to the sink so as to increase
network remaining energy. Routing algorithms like accumulation algorithms need to be aware
of network topology and each moment they select a route that is optimal in terms of energy. Of
course, this route also should be acceptable in terms of delay and number of paces to the sink. In
former algorithms either only energy parameter was used alone or delay parameter was
considered. If energy parameter alone is used it is possible that an unacceptable delay occurs
and if delay parameter alone is taken into account, network lifetime may shorten due to early
run out of nodes energy. Also in those algorithms when a route from sender to receiver was set,
the route remained to the end until nodes energy finished that resulted in decreased network
lifetime; but this algorithm selects the best modes from among all modes using learning
2. International Journal of Ad hoc, Sensor & Ubiquitous Computing( IJASUC ) Vol.1, No.2, June 2010.
2
automata that has trainable deciding structure and after selecting the best mode and its effect on
the environment and feedback from the environment, each moment it selects the best route. The
basis of this process is that each node has learning automata.
2. LITERATURE REVIEW
In [7] for increasing remaining energy, a route is selected for data transmission in which the
number of errors and conflicts is low and hence the number of sending messages decreases in
the network and network remaining energy increases. In [8] a routing algorithm is presented that
is aware of power for networks which guarantees quality of service and at the same time
decreases energy use through calculating remaining capacity of nodes batteries. In [9] for
increasing remaining energy of network broadcasting improvement is used. In this method a
backbone broadcasting of efficient energy has been used. In [10] clustering based on leach
protocol is used. In this protocol, cluster heads are selected by a probability based on rate of
remaining energy of node and the total remaining energy of all nodes in the network. In [11]
spanning tree algorithm is used for energy aware routing in which the node with highest
remaining energy as root and other nodes as father based on remaining energy for creating
connection for other nodes are selected. [12] Uses only distance parameter for creating spanning
tree that may lack sufficient energy for transmitting data.
In this paper we use both energy and distance parameters and select the best route to reach the
sink. This route is updated by automata using information received from network feedback.
3. LEARNING AUTOMATA
Learning automata[13,14] is a machine that is able to do a limited number of actions. Each
selected action is evaluated by a probability environment and the result is given to automata in
the form of a positive or negative signal to affect automata in selection of next action. The final
goal is for automata to learn to select the best action from among its actions. The best action is
one that maximizes the probability of receiving reward from environment. Function of learning
automata in interaction with environment can be seen in Figure 1.
The environment may be represented by triple E={α, β, τ} where α is total inputs, β is total
outputs, and τ is total fine probabilities.
Automata is represented by set {α, β, F, G} where α is total inputs, and F, probability vector for
selection of each action and G is related to automata learning algorithm [15].
For each input, selection probability parameter is determined and automata selects the input
with highest probability as automata output. The selected input is evaluated by automata in the
environment and the result is given back to automata to be used by it in learning. In case that
evaluation result is negative based on automata required parameters, the selected input will be
incurred fine that means the probability of its selection decreases and the probability of selection
of rest of members as the best one increase for next stage. In case that result is evaluated as
positive, the probability of selection of this mode as the best mode against other members will
increase in next stage. This process is referred to as automata learning.
Figure 1. Learning automata
3. International Journal of Ad hoc, Sensor & Ubiquitous Computing( IJASUC )
4. THE PROPOSED ALGORITHM
In automata based methods, at the beginning of network functioning routing packets are
distributed across whole network. Each node places in its routing table all those nodes that can
be selected for next step transmission and assigns each node a numeral value as selection
probability of the node as next pace. For sending data, each node selects a entry
most probability as the node for nest pace and sends data trough this node. In this reference to
update automata, the destination node after receiving data sends the confirmation message to
data sending node. Based on information of this
awards it in this way that if the selected route in this stage is an optimal route according to
network parameters, its selection probability for nest stage increases. On the contrary, of the
route is not optimal, its selection probability for next stage decreases. This process is called
automata training. For next stages this process will continue. With this feature of automata the
spread of routing packets at the beginning of each period in the network is preven
they are always aware of network status based on data received from network by nodes and data
related to routes and network topological changes are updated.
The proposed algorithm using this feature of automata acts as follows: first, routing me
are dispersed across the network in this way that each node sends an amount of energy and its
distance to the sink to all neighbours
across the network. This packet includes parameter of distanc
energy that will be set on 1 in sink node parameter. After other nodes receive this packet,
sending node ID along with its energy and parameter of distance to the sink is saved in the table
and one is added to parameter of distance to sink, and ID of new node as well as its energy are
placed in packet and dispersed again across the network. For the route to be registered in routing
table, only registering the node of next pace is sufficient. This process is performed by
network nodes. At the end of this process, each node saves some data related to adjacent nodes
in its routing table.
Routing table entries form automata input and automata considers quotient of division of
amount of entry energy to its distance to sink
probability of this entry as node of the next pace. The value of this numeral constant depends on
total energy parameter and parameter of distance of routing table entries of each node that in the
proposed algorithm is quotient division of total table energies to total distances of routing table
related to each node. Relation Equation 1 shows this probability:
Selection probability =
Here selection probability will be a positive number greater than 1. Each node for transmitting
data toward sink selects entry with biggest probability value as node of nest pace. In order to
update automata, the node is required to receive some data from network so as to be aware of
status of network. In [16] each node after receiving data sends confirmation message to data
sending node for the purpose of awareness from status of network. This message includes some
information about nodes of the route. Using information of these messages, network nodes will
be aware of network status and the amount of energy with route nodes. Then automata fines or
rewards the selected route of this node. Transmission of these message
accompanied by overhead. In [15] to decrease overhead of confirmation messages, the messages
are sent after transmission of every several data units. In case that the number of pace is variable
i.e., if nodes are mobile, it is required
sink node to the given node. In this algorithm, however, it is not necessary and only node
energy is needed as nodes are constant and the number of their pace is not variable. In [17]
updating node automata occurs locally that does not need sending and receiving a bulk volume
of routing packets at the beginning of each period. But in this algorithm it is possible that a node
is selected again since delay is low and selection probability is high. The pr
International Journal of Ad hoc, Sensor & Ubiquitous Computing( IJASUC ) Vol.1, No.2, June 2010.
4. THE PROPOSED ALGORITHM
In automata based methods, at the beginning of network functioning routing packets are
across whole network. Each node places in its routing table all those nodes that can
be selected for next step transmission and assigns each node a numeral value as selection
probability of the node as next pace. For sending data, each node selects a entry of table with the
most probability as the node for nest pace and sends data trough this node. In this reference to
update automata, the destination node after receiving data sends the confirmation message to
data sending node. Based on information of this message, node automata fines this route or
awards it in this way that if the selected route in this stage is an optimal route according to
network parameters, its selection probability for nest stage increases. On the contrary, of the
, its selection probability for next stage decreases. This process is called
automata training. For next stages this process will continue. With this feature of automata the
spread of routing packets at the beginning of each period in the network is preven
they are always aware of network status based on data received from network by nodes and data
related to routes and network topological changes are updated.
The proposed algorithm using this feature of automata acts as follows: first, routing me
are dispersed across the network in this way that each node sends an amount of energy and its
neighbours. Sink node forms a packet route request and spreads it
across the network. This packet includes parameter of distance to sink as well as node ID and its
energy that will be set on 1 in sink node parameter. After other nodes receive this packet,
sending node ID along with its energy and parameter of distance to the sink is saved in the table
of distance to sink, and ID of new node as well as its energy are
placed in packet and dispersed again across the network. For the route to be registered in routing
table, only registering the node of next pace is sufficient. This process is performed by
network nodes. At the end of this process, each node saves some data related to adjacent nodes
Routing table entries form automata input and automata considers quotient of division of
amount of entry energy to its distance to sink multiplied by a constant number as selection
probability of this entry as node of the next pace. The value of this numeral constant depends on
total energy parameter and parameter of distance of routing table entries of each node that in the
rithm is quotient division of total table energies to total distances of routing table
related to each node. Relation Equation 1 shows this probability:
Selection probability =
Here selection probability will be a positive number greater than 1. Each node for transmitting
data toward sink selects entry with biggest probability value as node of nest pace. In order to
e is required to receive some data from network so as to be aware of
status of network. In [16] each node after receiving data sends confirmation message to data
sending node for the purpose of awareness from status of network. This message includes some
nformation about nodes of the route. Using information of these messages, network nodes will
be aware of network status and the amount of energy with route nodes. Then automata fines or
rewards the selected route of this node. Transmission of these messages within network is
accompanied by overhead. In [15] to decrease overhead of confirmation messages, the messages
are sent after transmission of every several data units. In case that the number of pace is variable
i.e., if nodes are mobile, it is required that some data about network and its route be sent from
sink node to the given node. In this algorithm, however, it is not necessary and only node
energy is needed as nodes are constant and the number of their pace is not variable. In [17]
tomata occurs locally that does not need sending and receiving a bulk volume
of routing packets at the beginning of each period. But in this algorithm it is possible that a node
is selected again since delay is low and selection probability is high. The proposed algorithm
Vol.1, No.2, June 2010.
3
In automata based methods, at the beginning of network functioning routing packets are
across whole network. Each node places in its routing table all those nodes that can
be selected for next step transmission and assigns each node a numeral value as selection
of table with the
most probability as the node for nest pace and sends data trough this node. In this reference to
update automata, the destination node after receiving data sends the confirmation message to
message, node automata fines this route or
awards it in this way that if the selected route in this stage is an optimal route according to
network parameters, its selection probability for nest stage increases. On the contrary, of the
, its selection probability for next stage decreases. This process is called
automata training. For next stages this process will continue. With this feature of automata the
spread of routing packets at the beginning of each period in the network is prevented though
they are always aware of network status based on data received from network by nodes and data
The proposed algorithm using this feature of automata acts as follows: first, routing messages
are dispersed across the network in this way that each node sends an amount of energy and its
route request and spreads it
e to sink as well as node ID and its
energy that will be set on 1 in sink node parameter. After other nodes receive this packet,
sending node ID along with its energy and parameter of distance to the sink is saved in the table
of distance to sink, and ID of new node as well as its energy are
placed in packet and dispersed again across the network. For the route to be registered in routing
table, only registering the node of next pace is sufficient. This process is performed by all
network nodes. At the end of this process, each node saves some data related to adjacent nodes
Routing table entries form automata input and automata considers quotient of division of
multiplied by a constant number as selection
probability of this entry as node of the next pace. The value of this numeral constant depends on
total energy parameter and parameter of distance of routing table entries of each node that in the
rithm is quotient division of total table energies to total distances of routing table
(1)
Here selection probability will be a positive number greater than 1. Each node for transmitting
data toward sink selects entry with biggest probability value as node of nest pace. In order to
e is required to receive some data from network so as to be aware of
status of network. In [16] each node after receiving data sends confirmation message to data
sending node for the purpose of awareness from status of network. This message includes some
nformation about nodes of the route. Using information of these messages, network nodes will
be aware of network status and the amount of energy with route nodes. Then automata fines or
s within network is
accompanied by overhead. In [15] to decrease overhead of confirmation messages, the messages
are sent after transmission of every several data units. In case that the number of pace is variable
that some data about network and its route be sent from
sink node to the given node. In this algorithm, however, it is not necessary and only node
energy is needed as nodes are constant and the number of their pace is not variable. In [17]
tomata occurs locally that does not need sending and receiving a bulk volume
of routing packets at the beginning of each period. But in this algorithm it is possible that a node
oposed algorithm
4. International Journal of Ad hoc, Sensor & Ubiquitous Computing( IJASUC ) Vol.1, No.2, June 2010.
4
also acts on this basis and whenever node energy reaches a given threshold, data sending node
selects another node for sending data in this way that upon transmission of data, automata of
these nodes is automatically updated and the route changes only at that spot. Entry of routing
table only changes in that node. To prevent reselection of this node by neighbours node,
selection probability of this node should be fined. The node will be fined or rewarded with
respect to relations Equation 2 and Equation 3:
Pi(n+1)=pi(n)+α.(1-pi(n))
(2)
Pj(n+1)=pj(n)-α.pj(n)
(3)
Relation Equation 2 shows update of selection probability of node in reward mode and relation
Equation 3 shows the update in fine mode. In these relations α is update coefficient. The first
relation results in reward and higher selection probability while the second relation decreases
selection probability for remaining nodes. α value is positive and smaller than 1.
Sending node sends local messages to neighbouring nodes to update routing table and
neighbouring nodes in response to this message send their new energy. The amount of new
receiving energy from the neighbour nodes for updating routing table is liable to reward and
increased selection provided that it is higher than threshold limit. However, if it is equal to or
lower than the threshold limit, it will be fined. Then data sending node from updated routing
table selects the highest probability for node of next pace. Hence the route is updated locally
without any need to entirely spread of routing packets. Thus, with decrease in sent and received
messages consumed energy of network will decrease and probability of selection a node with
energy equal to threshold energy will reduce considerably.
5. EVALUATION ALGORITHM
In this section the proposed algorithm is evaluated by computer-based simulations. For this
purpose, routing with automata is compared with routing without automata. The parameters
involved in this comparison are lifetime, number of active nodes, remained energy, and delay.
For simulating the algorithm and evaluating its efficiency, MATLAB software has been used. In
this simulation a network with random topology having some desirable nodes and districts is
developed. To do simulation and evaluation, N number sensor nodes are located randomly
within a square border of 600*600. N is supposed to be 300. The initial energy of nodes is taken
0.01 J. frequencies of reiterations regarded for simulation is 700. Connective range of nodes is
60 meter; that means each 2 nodes located in a distance less than 60 m are regarded as
neighbours and are able to exchange data. Moreover, a range in which a node can sense an event
is half of connective range that is 30 meter. Events are sensed periodically and in each period
one event occurs. Value of α for update of selection probability of a node is taken 0.001. Placing
spot of sink in simulation is in (300,300). Figure 2 shows a comparison of lifetime of proposed
algorithm with and without automata. Table 1 shows their properties.
Table 1. Property of simulation
Square border 600*600
N 300
Initial energy 0.01
frequency 700
Connective range 60
Sensing range 30
sink (300,300)
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Figure 2. Comparison of lifetime of proposed algorithm with and without automata
Figure 3 shows a comparison of remaining energy in proposed algorithm with and without
automata.
Figure 3. Comparison of remaining energy in proposed algorithm with and without
automata
Figure 4 shows comparison of number of active nodes in proposed algorithm with and without
automata.
Figure 4. Comparison of number of active nodes in proposed algorithm with and
without automata
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Finally Figure 5 presents passed paces for both algorithms.
Figure 5. Passed paces for proposed algorithm and without automata
6. CONCLUSION AND FUTURE WORKS
As mentioned already, a main problem with wireless sensor networks is energy. In order to
increase lifetime of these networks and to reduce energy use for nodes routing with automata
has been used in this paper that is supported by local update. In this update process, in order not
to reselect a node, selection probability of nodes with lower than threshold energy will decrease
by relation two and for nodes upper than threshold energy award is conferred. Network lifetime
and number of active nodes will increase with this algorithm. This has been proved by
simulation. Our simulation results show that using automata increases network lifetime. As
future works, improving this algorithm in delay parameter as well as utilizing it in other routing
protocols is to be examined.
ACKNOWLEGMENT
This study was completed at the University of Isfahan and supported by the Office of Graduate
Studies. The authors are grateful to the Office for their support.
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