A utility-based distributed data routing algorithm is proposed and evaluated for heterogeneous wireless sensor networks. It is energy efficient and is based on a game-theoretic heuristic load-balancing approach. It runs on a hierarchical graph arranged as a tree with parents and children. Sensor nodes are considered heterogeneous in terms of their generated traffic, residual energy and data transmission rate and the bandwidth they provide to their children for communication. The proposed method generates a data routing tree in which child nodes are joined to parent nodes in an energy-efficient way. The principles of the Stackelberg game, in which parents as leaders and children as followers, are used to support the distributive nature of sensor networks. In this context, parents behave cooperatively and help other parents to adjust their loads, while children act selfishly. Simulation results indicate the proposed method can produce on average more load-balanced trees, resulting in over 30% longer network lifetime compared with the cumulative algorithm proposed in the literature.
In wireless sensor networks, achieving load balancing in an energy-efficient manner to improve the network lifetime as much as possible is still a challenging problem because in such networks, the only energy resource for sensor nodes is their battery supplies. This paper proposes a game theoretical-based solution in the form of a distributed algorithm for constructing load-balanced routing trees in wireless sensor networks. In our algorithm, load balancing is realized by adjusting the number of children among parents as much as possible, where child adjustment is considered as a game between the parents and child nodes; parents are considered as cooperative players, and children are considered as selfish players. The gained utility by each node is determined by means of some utility functions defined per role, which themselves determine the behavior of nodes in each role. When the game is over, each node gains the maximum benefit on the basis of its utility function, and the balanced tree is constructed. The proposed method provides additional benefits when in-network aggregation is applied. Analytical and simulation results are provided, demonstrating that our proposed algorithm outperform two recently proposed benchmarking algorithms [1,2], in terms of time complexity and communication overhead required for constructing the load-balanced routing trees.
In this thesis, two distributed algorithms for the construction of load balanced routing trees in wireless sensor networks are proposed. In such networks load balanced data routing and aggregation can considerably decrease uneven energy consumption among sensor nodes and prolong network lifetime. The proposed algorithms achieve load balancing by adjusting the number of children among parents as much as possible. The solution is based on game theoretical approach, where child adjustment is considered as a game between parents and child nodes, in which parents arc cooperative and children are selfish players. The gained utility by each node is determined through utility functions defined per role. Utility functions determine the behavior of nodes in each role. At the game termination, each individual node gains the maximum benefit based on its utility function, and the network reaches the global goal of forming the balanced tree. The proposed methods are called Utility Driven Balanced Communication (UDBC) algorithm which is designed for homogenous environment, where all nodes are assumed to produce equal amount of information, and Heterogenous Balanced Data Routing (HBDR) algorithm which is proposed for heterogenous environment, where different applications use different aggregation functions, and nodes can be vary in terms of the amount of produced information, energy levels, data transmission rate and available of the amount of produced information, energy levels, data transmission rate and available
bandwidth for transmission. The advantage of this work over similar work in the literature is the construction of more balanced trees which results in prolonging network lifetime, with the capability of adaption according to specific application needs for sensitivity to delay and reliability of data delivery.
While wireless sensor networking plays a critical role in many important applications, it also contributes to the energy footprint - which continues to increase with the proliferation of wireless devices and networks worldwide. Energy-efficiency becomes a major concern in the development of next generation sensor systems and networks. This chapter discusses data management techniques from energy efficiency point of view for green wireless sensor networks.
In this thesis, two distributed algorithms for the construction of load balanced routing trees in wireless sensor networks are proposed. In such networks load balanced data routing and aggregation can considerably decrease uneven energy consumption among sensor nodes and prolong network lifetime. The proposed algorithms achieve load balancing by adjusting the number of children among parents as much as possible. The solution is based on game theoretical approach, where child adjustment is considered as a game between parents and child nodes, in which parents arc cooperative and children are selfish players. The gained utility by each node is determined through utility functions defined per role. Utility functions determine the behavior of nodes in each role. At the game termination, each individual node gains the maximum benefit based on its utility function, and the network reaches the global goal of forming the balanced tree. The proposed methods are called Utility Driven Balanced Communication (UDBC) algorithm which is designed for homogenous environment, where all nodes are assumed to produce equal amount of information, and Heterogenous Balanced Data Routing (HBDR) algorithm which is proposed for heterogenous environment, where different applications use different aggregation functions, and nodes can be vary in terms of the amount of produced information, energy levels, data transmission rate and available of the amount of produced information, energy levels, data transmission rate and available
bandwidth for transmission. The advantage of this work over similar work in the literature is the construction of more balanced trees which results in prolonging network lifetime, with the capability of adaption according to specific application needs for sensitivity to delay and reliability of data delivery.
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