Directional sensor nodes deployment is indispensable to a large number of applications including Internet Things applications. Nowadays, with the recent advances in robotic technology, directional mounted on mobile robots can move toward appropriate locations. Considering probabilistic sensing model along mobility and motility nodes, area coverage such network more complicated than static netwo...

In this paper, we give a probabilistic counterpart of  Mazur-Ulam theorem  in probabilistic normed groups. We show, under some conditions, that every surjective isometry between two probabilistic normed groups is a homomorphism.

Network modeling can be approached using either discriminative or probabilistic models. In the task of link prediction a probabilistic model will give a probability for the existence of a link; while in some scenarios this may be beneficial, in others a hard discriminative boundary needs to be set. Hence the use of a discriminative classifier is preferable. In domains such as image analysis and...

Complex networks, such as biological, social, and communication networks, often entail uncertainty, and thus, can be modeled as probabilistic graphs. Similar to the problem of similarity search in standard graphs, a fundamental problem for probabilistic graphs is to efficiently answer k-nearest neighbor queries (k-NN), which is the problem of computing the k closest nodes to some specific node....

In this paper, performance analysis of different broadcasting methods i.e. flooding or blind broadcast and probabilistic broadcasting inside the geocast region has been done. Our objective is to provide a comparative analysis between flooding and probabilistic methods with varying number of nodes in Vehicular Ad hoc Networks. Simulations have been conducted using the NS-2 simulator. For result ...

Consider the problem of finding the shortest paths from a node source s to a node sink t in a complete network. On any given instance of the problem, only a subset of the intermediate nodes can be used to go from s to t , the subset being chosen according to a given probability law. We wish to find an a priori path from s to f such that, on any given instance of the problem, the sequence of nod...

One of the major task of wireless sensor network is to sense accurate data from the physical environment. Hence in this paper, we develop an estimated data accuracy model for randomly deployed sensor nodes which can sense more accurate data from the physical environment. We compare our results with other information accuracy models and shows that our estimated data accuracy model performs bette...

In this paper, we provide an energy efficient selfhealing mechanism for Wireless Sensor Networks. The proposed solution is based on our probabilistic sentinel scheme. To reduce energy consumption while maintaining good connectivity between sentinel nodes, we compose our solution on two main concepts, node adaptation and link adaptation. The first algorithm uses node adaptation technique and per...

A number of exact algorithms have been developed to perform probabilistic inference in Bayesian belief networks in recent years. These algorithms use graph-theoretic techniques to analyze and exploit network topology. In this paper, we examine the problem of e cient probabilistic inference in a belief network as a combinatorial optimization problem, that of nding an optimal factoring given an a...

In modern decision-support systems, probabilistic networks model uncertainty by a directed acyclic graph quantified by probabilities. Two closely related problems on these networks are the Kth MPE and Kth Partial MAP problems, which both take a network and a positive integer k for their input. In the Kth MPE problem, given a partition of the network’s nodes into evidence and explanation nodes a...