An enhanced ordered binary decision diagram （EOBDD） algorithm is proposed to evaluate the reliability of wireless sensor networks （WSNs）, based on the considerations of the common cause failure （CCF） and a large...An enhanced ordered binary decision diagram （EOBDD） algorithm is proposed to evaluate the reliability of wireless sensor networks （WSNs）, based on the considerations of the common cause failure （CCF） and a large number of nodes in WSNs. The EOBDD algorithm analyzes the common cause event （CCE） and the network structure when CCE takes place according to the stochastic graph and the CCF model of WSNs. After constructing the ordered binary decision diagram （OBDD） of the original network with node expansion, it uses a set of OBDD variables （SOV） to guide reliability computations along this OBDD. The two steps about OBDD can decrease the cost of OBDD constructions and storage. Furthermore, the efficient OBDD structure and Hash tables can greatly decrease redundant computations of isomorphs. The experiment results show that the EOBDD can be used to evaluate the reliability of WSN efficiently.展开更多
The optimal semi-matching problem is one relaxing form of the maximum cardinality matching problems in bipartite graphs, and finds its applications in load balancing. Ordered binary decision diagram (OBDD) is a canoni...The optimal semi-matching problem is one relaxing form of the maximum cardinality matching problems in bipartite graphs, and finds its applications in load balancing. Ordered binary decision diagram (OBDD) is a canonical form to represent and manipulate Boolean functions efficiently. OBDD-based symbolic algorithms appear to give improved results for large-scale combinatorial optimization problems by searching nodes and edges implicitly. We present novel symbolic OBDD formulation and algorithm for the optimal semi-matching problem in bipartite graphs. The symbolic algorithm is initialized by heuristic searching initial matching and then iterates through generating residual network, building layered network, backward traversing node-disjoint augmenting paths, and updating semi-matching. It does not require explicit enumeration of the nodes and edges, and therefore can handle many complex executions in each step. Our simulations show that symbolic algorithm has better performance, especially on dense and large graphs.展开更多
基金supported by the National Natural Science Foundation of China (60672086)the Hi-Tech Research and Development Program of China (2007AA01Z2A1, 2008AA01A316)the EUFPT Project EFIPSANS (215547), and the Foundation for Western Returned Chinese Scholars of the Ministry of Education
文摘An enhanced ordered binary decision diagram （EOBDD） algorithm is proposed to evaluate the reliability of wireless sensor networks （WSNs）, based on the considerations of the common cause failure （CCF） and a large number of nodes in WSNs. The EOBDD algorithm analyzes the common cause event （CCE） and the network structure when CCE takes place according to the stochastic graph and the CCF model of WSNs. After constructing the ordered binary decision diagram （OBDD） of the original network with node expansion, it uses a set of OBDD variables （SOV） to guide reliability computations along this OBDD. The two steps about OBDD can decrease the cost of OBDD constructions and storage. Furthermore, the efficient OBDD structure and Hash tables can greatly decrease redundant computations of isomorphs. The experiment results show that the EOBDD can be used to evaluate the reliability of WSN efficiently.
文摘The optimal semi-matching problem is one relaxing form of the maximum cardinality matching problems in bipartite graphs, and finds its applications in load balancing. Ordered binary decision diagram (OBDD) is a canonical form to represent and manipulate Boolean functions efficiently. OBDD-based symbolic algorithms appear to give improved results for large-scale combinatorial optimization problems by searching nodes and edges implicitly. We present novel symbolic OBDD formulation and algorithm for the optimal semi-matching problem in bipartite graphs. The symbolic algorithm is initialized by heuristic searching initial matching and then iterates through generating residual network, building layered network, backward traversing node-disjoint augmenting paths, and updating semi-matching. It does not require explicit enumeration of the nodes and edges, and therefore can handle many complex executions in each step. Our simulations show that symbolic algorithm has better performance, especially on dense and large graphs.
