Fusion Techniques for Reliable Information: A Survey <s> Bayesian Network (BN) <s> We formulate the target tracking based on received signal strength in the sensor networks using Bayesian network representation. Data fusion among the same type of sensors in an active sensor neighborhood is referred to as cross-sensor fusion, conceptualized as "cooperative fusion". This data fusion is embedded in the likelihood function derivation. Fusion of signals collected by multiple types of sensors are referred to as cross-modality fusion. It is "complementary", and represented by the contribution of their likelihood functions to the state update. The tracking algorithm is implemented using particle filter. Very good experimental results are obtained using SensIt data. <s> BIB001 </s> Fusion Techniques for Reliable Information: A Survey <s> Bayesian Network (BN) <s> Many information fusion applications are often characterized by a high degree of complexity because: 1) data are often acquired from sensors of different modalities and with different degrees of uncertainty; 2) decisions must be made efficiently; and 3) the world situation evolves over time. To address these issues, we propose an information fusion framework based on dynamic Bayesian networks to provide active, dynamic, purposive and sufficing information fusion in order to arrive at a reliable conclusion with reasonable time and limited resources. The proposed framework is suited to applications where the decision must be made efficiently from dynamically available information of diverse and disparate sources. <s> BIB002
BN, a probabilistic graphical model, solves decision problems under uncertainty. It represents probabilistic relationships between variables set and independencies. The goal of BN is to make estimations and inferences in multisensor fusion based knowledge-based situation aware of pervasive computing BIB001 , BIB002 . The local computation technique provides a solution to problem of computational complexity which is involved in joint probability distributions. Medical diagnosis is a main application of BN. However, the BN is difficult to deal with temporal variations that should be considered in decision-supporting systems. The dynamic BN, which considers temporal dimensions, is more useful theory in pervasive computing areas  .