Function computation over heterogeneous
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In addition, ECS has also widened its wings by the way consummating academic projects especially for the final year professional degree students in India. ECS consist of a technical team that has solved many IEEE papers and delivered world-class solutions .
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Since the location of the sink is remote, considerable energy would be consumed if each node directly
transmits data to the base station. Aggregating data at the intermediate nodes and transmitting using multihops
aids in reducing energy consumption to a great extent. This paper proposes a hybrid protocol
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improve the life span of the network is energy efficiency. to overcome this demerit many research have
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of the WSN can be improved by employing the aggregation techniques. During the process of aggregation
the numbers of transmission are reduced by combining the similar data from the nearby areas. By using
the clustering technique and aggregating the correlated data greatly minimize the energy consumed in
collecting and disseminating the data. In this work, we evaluate the performance of a novel energy
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to study the performance of our proposed aggregation protocol with divergent sink placements such as
when sink is at the centre of the sensing field, corner of the sensing field or at a location selected
randomly in the sensor field. We present experimental results by calculating the lifetime of network in
terms of number of sensing rounds using various parameters such as – average remaining energy of
nodes, number of dead nodes after the specified number of sensing rounds. Finally the performance of
various aggregation algorithms such as LEACH, SEP and our proposed aggregation protocol (EECAP)
are compared with divergent sink placements. The simulation results demonstrates that EECAP exhibits
good performance in terms of lifetime and the energy consumption of the wireless sensor networks and
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effect on network life time with multiple base stations. While evaluating the communication overhead of
various cluster sizes, we observed that the optimal cluster size for a given network is complex, depending
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saving energy, as well as for management of network, because the patterns found can be used for
both decision making in applications and system performance tuning. Node distribution in WSNs is
either deterministic or self-organizing and application dependant. The sensor nodes in WSNs have
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Since the location of the sink is remote, considerable energy would be consumed if each node directly
transmits data to the base station. Aggregating data at the intermediate nodes and transmitting using multihops
aids in reducing energy consumption to a great extent. This paper proposes a hybrid protocol
“Energy efficient Grid and Tree based routing protocol” (EGT) in which the sensing area is divided into
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deployed, the small sensor nodes are usually inaccessible to the user, and thus replacement of the energy
source is not feasible. Hence, one of the most important issues that need to be enhanced in order to
improve the life span of the network is energy efficiency. to overcome this demerit many research have
been done. The clustering is the one of the representative approaches. In this paper, we introduce a
dynamic clustering algorithm using Fuzzy Logic and genetic algorithm. In fact, using fuzzy system design
and system optimization by genetic algorithm is presented approach to select the best cluster head in
sensor networks. Using random data set has been addressed to evaluate of fuzzy-genetic system presented
in this paper and finally, MSE rate or mean error of sending the messages using proposed fuzzy system in
comparison with LEACH method is calculated in select the cluster head. The results of evaluations is
representative of a reduction the MSE metric in proposed method in comparison with LEACH method for
select the cluster head. Reduce of MSE directly is effective on energy consumption and lifetime of wireless
sensor network and can cause the reduce energy consumption and increase network lifetime.
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transmission by combining the data and sending the final required result to the base station. The lifetime
of the WSN can be improved by employing the aggregation techniques. During the process of aggregation
the numbers of transmission are reduced by combining the similar data from the nearby areas. By using
the clustering technique and aggregating the correlated data greatly minimize the energy consumed in
collecting and disseminating the data. In this work, we evaluate the performance of a novel energy
efficient cluster based aggregation protocol (EECAP) for WSN. The main focus in this proposed work is
to study the performance of our proposed aggregation protocol with divergent sink placements such as
when sink is at the centre of the sensing field, corner of the sensing field or at a location selected
randomly in the sensor field. We present experimental results by calculating the lifetime of network in
terms of number of sensing rounds using various parameters such as – average remaining energy of
nodes, number of dead nodes after the specified number of sensing rounds. Finally the performance of
various aggregation algorithms such as LEACH, SEP and our proposed aggregation protocol (EECAP)
are compared with divergent sink placements. The simulation results demonstrates that EECAP exhibits
good performance in terms of lifetime and the energy consumption of the wireless sensor networks and
which can be as equally compared with existing clustering protocols.
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communication between sensor node and base station. In this paper we analyzed the performance of the
zone based clustering protocol [2] under varying position of base stations, different zone sizes and the
effect on network life time with multiple base stations. While evaluating the communication overhead of
various cluster sizes, we observed that the optimal cluster size for a given network is complex, depending
on a range of parameters. Simulation results show that communication overhead decreases as we increase
the number of zone in the network. We show that placing multiple base stations in place of single base
station in zone based routing protocol enhance the network life time.
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saving energy, as well as for management of network, because the patterns found can be used for
both decision making in applications and system performance tuning. Node distribution in WSNs is
either deterministic or self-organizing and application dependant. The sensor nodes in WSNs have
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Ieee 2020 21 ns2 in pondicherry,best project center in pondicherry,final year...
Ieee 2020 21 ns2 in pondicherry,best project center in pondicherry,final year...Nexgen Technology Nexgen Technology Address:
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NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Ieee 2020 21 java dotnet in pondicherry,final year projects in pondicherry,pr...
Ieee 2020 21 java dotnet in pondicherry,final year projects in pondicherry,pr...Nexgen Technology Nexgen Technology Address:
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Mobile: 9791938249,9025656779
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Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
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Near SBI ATM,
Puducherry.
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Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
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No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: mailtonexgentech@gmail.com.
www.nexgenproject.com
Mobile: 9791938249,9025656779
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
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Ieee 2020 21 embedded in pondicherry,final year projects in pondicherry,best...Nexgen Technology This document lists over 100 potential final year project titles related to embedded systems and Internet of Things (IoT) technologies across various application domains, such as medical and healthcare, smart homes, agriculture, transportation, and more. It provides contact information for Praveen Kumar to discuss project details. The titles are organized under categories like embedded handheld products, cyber physical systems, pervasive computing, energy management, bioscience, and others.
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For more interesting videos, please click to subscribe to our YouTube Channel:
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Function computation over heterogeneous
- 1. ECRUITMENT SOLUTIONS (0)9751442511, 9750610101 #1, Ist Cross, Ist Main Road, Elango Nagar,Pondicherry-605 011. tech@ecruitments.com www.ecruitments.com Function Computation over Heterogeneous Wireless Sensor Networks ABSTRACT: The problem of function computation in large scale heterogeneous wireless sensor networks (WSNs) is studied. Suppose n sensors are placed in a disk network area with radius n_, where _ is a positive constant. The sensors are located heterogeneously around the sink node, i.e., the density of sensors decreases as the distance from the sink node increases. At one instant, each sensor is assigned an input bit. The target of the sink is to compute a function f of the input bits, where f is either a symmetric or the identity function. Energy-efficient algorithms based on inhomogeneous tessellation of the network are designed and the corresponding optimal energy consumption scaling laws are derived. We show that the proposed algorithms are indeed optimal (except for some polylogarithmic terms) by deriving matching lower bounds on the energy consumption required to compute f. At last, based on the results obtained in this paper as well as those obtained by previous works, some discussions and comparisons are presented. We observe that 1) the heterogeneity extent has a great impact on the computation of both symmetric function and identity function, and 2) the energy usage of computing symmetric function can be significantly smaller than that of computing identity function under certain parameter condition, i.e., performing in-network computation helps save energy.
- 2. ECRUITMENT SOLUTIONS (0)9751442511, 9750610101 #1, Ist Cross, Ist Main Road, Elango Nagar,Pondicherry-605 011. tech@ecruitments.com www.ecruitments.com EXISTING SYSTEM: WIRELESS sensor networks (WSNs) usually consist of numerous small sensor nodes which are capable of computing and communicating data. Mostly, the WSN has a sink node or a fusion center which is responsible for processing the sensor data and extracting the information it wants from the raw data. Often, the sink is not interested in all the raw data collected by those sensors. The sensors are located heterogeneously around the sink node, i.e., the density of sensors decreases as the distance from the sink node increases. At one instant, each sensor is assigned an input bit. The target of the sink is to compute a function f of the input bits, where f is either a symmetric or the identity function. Energy-efficient algorithms based on inhomogeneous tessellation of the network are designed and the corresponding optimal energy consumption scaling laws are derived. PROPOSED SYTSEM: Proposed algorithms are indeed optimal (except for some polylogarithmic terms) by deriving matching lower bounds on the energy consumption required to compute f. At last, based on the results obtained in this paper as well as those obtained by previous works, some discussions and comparisons are presented. We observe that 1) the heterogeneity extent has a great impact on the computation of both symmetric function and identity function, and 2) the energy usage of computing symmetric function can be significantly smaller than that of computing identity function under certain parameter condition, i.e., performing in-network computation helps save energy. In-network computation is proposed to be a promising paradigm to
- 3. ECRUITMENT SOLUTIONS (0)9751442511, 9750610101 #1, Ist Cross, Ist Main Road, Elango Nagar,Pondicherry-605 011. tech@ecruitments.com www.ecruitments.com reduce the overhead of WSNs. We observe that performing in-network computation sometimes reduce energy usage while sometimes not. Our theoretical results offer fundamental insights into how to take the advantage of in-network computation to design more energy-efficient large scale wireless networks, e.g., WSNs and large scale mobile Internet, etc. CONCLUSION: The energy complexity of function computation in heterogeneous wireless networks is studied in this paper. The target function is either a symmetric function or the identity function, which are both widely accepted very useful and practical functions. We investigate the impact of heterogeneity on the energy consumption in wireless networks and find that the heterogeneity extent greatly influences the minimum total energy usage as well as the optimal scheme for computing functions. We observe that performing in-network computation sometimes reduce energy usage while sometimes not. Our theoretical results offer fundamental insights into how to take the advantage of in-network computation to design more energy- efficient large scale wireless networks, e.g., WSNs and large scale mobile Internet, etc.