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A wireless network consists of a large number of nodes with limited resources. The large number of event data will be generated over a period of time in a wireless network. Also, faced with high resource demand variability and with misfit resource renting policies, the current practices is to overprovision for each game tens of owned data. In a wir...
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Citations
... There have been many efforts in the literature toward developing user association rules considering interference avoidance and/or cell load balancing [11][12][13]. To avoid interference when frequency is universally reused and inter-cell interference is severe, centralized approaches have been considered [14,15]. The basic idea is to schedule users across cells so that they do not severely interfere with each other. ...
... The selected edges and the vertices belonging to the new partition are highlighted. The proceeding and result of the first execution show in [15]. Also, the second proceed and result of our proposed scheme is addressed in [15]. ...
... The proceeding and result of the first execution show in [15]. Also, the second proceed and result of our proposed scheme is addressed in [15]. Result of first execution of proposed scheme is shown in Table 2. ...
A typical wireless sensor network is conceived as bring a very large collection of low-powered, homogeneous nodes that remain stativ post-deployment and forward sensed data to a single sink via multi-hop communication. During the recent years, many energy-efficient load balancing protocols have been proposed for wireless sensor networks. Because a wireless networks consists of a large number of nodes with limited resources, the load balancing protocol is one of the key issues which can be solve the tradeoff between the service capacity and energy efficience. Load balancing protocols typically employ only a network capacity oriented approach in the next hop node is selected on adjacent or network information. This approach draw into a large overhead when the accurate adjacent information is needed for efficient and reliable routing. When an application service is caused large interaction between the adjacent nodes, the previous load balancing protocols without considering this issue were re-allocated the adjacent nodes and the other adjacent is re-allocated another region. This is not efficient for network performance because the previous protocols are generated the large overhead by increased routing and overhead. So, we propose a user-oriented load balancing scheme for an energy-efficient load balancing in wireless networks which is based on allocate load on wireless sensor nodes proportionally to each of the agent’s capacity and user-oriented approach. This proposed scheme is combined dynamic provisioning algorithm based on greedy graph and user oriented load balancing scheme for maintain of the performance and stability of distributed system in wireless sensor networks. We address the key functions for our proposed scheme and simulate the efficiency of our proposed scheme using mathematical analyze.
... Apart from these techniques, dynamic power management techniques [32][33][34][35], LSRA [36], HLBS [37], EEOM [38], ELBS [39], and several other techniques [40][41][42][43][44] have been proposed for load balancing in order to enhance network lifetime. The proposed technique in this research paper addresses several issues, with the following advantages over most of the existing algorithms: ...
To provide a reliable and efficient service, load balancing plays an important role in wireless sensor networks (WSNs). There is a need to maximize the network lifetime for WSNs applications with periodic generation of data. Due to the relationship between energy consumption and network sensor node lifetime, energy consumption in a network should be minimized and balanced in order to increase network lifetime. Energy-efficient load-balancing techniques are needed to solve this problem. In this paper, a particle swarm optimization (PSO)-based energy-efficient load-balancing technique is proposed, in which the required number of routing paths and energy consumption of different nodes and paths are calculated. Based on maximum residual energy, paths are selected and further PSO-based load balancing is performed among all the paths for data transfer at a particular point of time. The performance of the proposed technique is evaluated using real testbed and experimental results and shows that the proposed technique performs better than existing techniques in terms of network lifetime, energy consumption, throughput, number of alive nodes, number of data packets received, execution time, and convergence rate.
... The T u is the update of entity states received from or send to another wireless sensor node. We modeling the CPU time T M spends for send and receive messages from agent to each wireless sensor node as [30] ...
... Parameters of the simulation and modelling[30] be different to sensor S j .Table 1, we present the parameters list for simulation and mathematical modeling. ...
The role of load balancing in wireless sensor networks is to provide a constant and reliable service. Applications with periodic data generation for wireless sensor networks require the maximum lifetime of the network. Most research imposes mainly on how to maximize the lifetime of the sensor nodes for the load balancing in order to performance and effeteness in the wireless sensor networks. Because the energy consumption is related to lifetime of the sensor nodes and the energy is a strictly limited resource in wireless sensor networks. Also, energy consumption optimization is required to synchronize the lifetime of the nodes with the whole network lifetime. For this reason, we address the lifetime maximization problem then we improves a novel load balancing scheme that balance the energy consumption of the sensor nodes and maximum network lifetime by load balancing applying the sub-network management in wireless sensor networks. Then, we propose a scheme using analytical models and compare the results with the previous researches. Our simulation result shows that the sensor nodes operate together for full network lifetime and it indicates maximum utilization of the usable energy of the wireless sensor network.
The role of load balancing in wireless sensor networks is to provide a constant and reliable service. Most research imposes mainly on how to maximize the lifetime of the sensor nodes for the load balancing in order to performance and effeteness in the wireless sensor networks. Because the energy consumption is related to lifetime of the sensor nodes and the energy is a strictly limited resource in wireless sensor networks. For this reason, we address the lifetime maximization problem then we improves a novel load balancing scheme that balance the energy consumption of the sensor nodes and maximum network lifetime by load balancing applying the sun-network management in wireless sensor networks. Then, we propose a scheme using analytical models and compare the results with the previous researches.
