An energy efficient Genetic Algorithm based approach for sensor-to-sink binding in multi-sink wireless sensor networks

An energy efficient Genetic Algorithm based approach for sensor-to-sink binding in multi-sink wireless sensor networks

An energy efficient Genetic Algorithm based approach for sensor-to-sink binding in multi-sink wireless sensor networks

 

 

Abstract

Wireless sensor networks (WSNs) are ad-hoc networks in which sensors, that are designed to relay data back to sink nodes and/or Base Stations, are deployed in an area and may be configured in real time. Sensors, however, have limited energy supplies and are often left untouched after deployment, thus making battery replacement very difficult or even impossible. Therefore, energy should be efficiently conserved to extend the WSNs lifetime. One of the existing solutions is to deploy multiple sinks, more capable nodes in comparison to sensors, in the network to increase the coverage area and shorten the communication distance between sensors and sinks. However, this raises the issue concerning which sensors should bind to which sinks in order to avoid overloading particular sinks. In this paper, we devise a Genetic Algorithm based approach to solve the problem of balancing the load of sensors amongst sinks in a multi-sink WSN, while ensuring that the best routes to sinks are found for the sensors that cannot directly reach a sink. We evaluate the performance of our approach and compare it to an existing one using the network simulator NS-2 through measuring several metrics such as the variance of remaining energy among sinks, and energy consumption in sinks. The obtained results show that the proposed approach promising.

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  • Complete Research Assistance

Technology Involved:-

  • MATLAB, Simulink, MATPOWER, GRIDLAB-D,OpenDSS, ETAP, GAMS

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  • Complete Code of this paper
  • Complete Code of the approach to be propose
  • A document containing complete explanation of code and research approach
  • All materials used for this research
  • Solution to all your queries related to your work

Energy-Efficient Communication Protocol for Wireless Microsensor Networks

Energy-Efficient Communication Protocol for Wireless Microsensor Networks

Energy-Efficient Communication Protocol for Wireless Microsensor Networks

 

Abstract

Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multi hop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive  lustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster base stations (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show that LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional routing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.

 

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What we provide:

  • Complete Research Assistance

Technology Involved:-

  • MATLAB, Simulink, MATPOWER, GRIDLAB-D,OpenDSS, ETAP, GAMS

Deliverables:-  

  • Complete Code of this paper
  • Complete Code of the approach to be propose
  • A document containing complete explanation of code and research approach
  • All materials used for this research
  • Solution to all your queries related to your work

 

Maximizing lifetime of a wireless sensor network via joint optimizing sink placement and sensor-to-sink routing

Maximizing lifetime of a wireless sensor network via joint optimizing sink placement and sensor-to-sink routing

Maximizing lifetime of a wireless sensor network via joint optimizing sink placement and sensor-to-sink routing

 

a b s t r a c t
Wireless sensor networks typically contain hundreds of sensors. The sensors collect data and relay it to sinks through single hop or multiple hop paths. Sink deployment signifi-
cantly influences the performance of a network. Since the energy capacity of each sensor is limited, optimizing sink deployment and sensor-to-sink routing is crucial. In this paper, this problem is modeled as a mixed integer optimization problem. Then, a novel layer based diffusion particle swarm optimization method is proposed to solve this large-scaled optimization problem. In particular, two sensor-to-sink binding algorithms are combined as inner layer optimization to evaluate the fitness values of the solutions. Compared to existing methods that the sinks are selected from candidate positions, our method can achieve better performance since they can be placed freely within a geometrical plane. Several numerical examples are used to validate and demonstrate the performance of our method. The reported numerical results show that our method is superior to those existing. Fur-
thermore, our method has good scalability which can be used to deploy a large-scaled sensor network.

 

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What we provide:

  • Complete Research Assistance

Technology Involved:-

  • MATLAB, Simulink, MATPOWER, GRIDLAB-D,OpenDSS, ETAP, GAMS

Deliverables:-  

  • Complete Code of this paper
  • Complete Code of the approach to be propose
  • A document containing complete explanation of code and research approach
  • All materials used for this research
  • Solution to all your queries related to your work

Localization in zigbee network

Determining the location of any wireless node in a wireless network becomes a field of extensive study. The method used in wireless networks like GPS or Wi-Fi are very advanced but when we talk about any indoor environment the scenario is pretty much different. Large wireless networks like GPS, Wi-Fi or Wi-max instruments are able to perform complex calculations and have good amount of power back up. While scenario is different for indoor environments. When we talk about indoor environments the wireless nodes covers a small area due to which they have limited power and computational complexity. Indoor environments have many obstacles which restrict the movement of waves (ex. Walls, tables, chairs, refrigerators, etc.).

Zigbee is the popular wireless network which is commonly used for indoor wireless network. IEEE has given zigbee the protocol stack 802.15.4. It is easily available in the market and on the Internet also its deployment is easy as compared to other wireless network.

Various techniques like time difference of arrival (TDOA), angle of arrival (AOA), etc. has been proposed for the localization in zigbee network . But the localization error (the difference between actual location and calculated location of any new wireless node) in these techniques are high. In our work we try to find the location of any new wireless node in the zigbee network using received signal strength (RSSI) and ANFIS (Adaptive Neuro Fuzzy Inference Systems) technique using triangulation algorithm. The simulations are performed in MATLAB to perfrom localization in zigbee network .