Nearest Neighbor Clustering over Partitioned Data

Most clustering algorithms assume that all the relevant data are available on a single node of a computer network. In the emerging distributed and networked knowledge environments, databases relevant for computations may reside on a number of nodes connected by a communication network. These data resources cannot be moved to other network sites due to privacy, security, and size considerations. The desired global computation must be decomposed into local computations to match the distribution of data across the network. The capability to decompose computations must be general enough to handle different distributions of data and different participating nodes in each instance of the global computation. In this paper, we present a methodology and algorithm for clustering distributed data in d-dimensional space, using nearest neighbor clustering, wherein each distributed data source is represented by an agent. Each such agent has the capability to decompose global computations into local parts, for itself and for agents at other sites. The global computation is then performed by the agent either exchanging some minimal summaries with other agents or traveling to all the sites and performing local tasks that can be done at each local site. The objective is to perform global tasks with a minimum of communication or travel by participating agents across the network

A New Mechanism for Tracking a Mobile Target Using Grid Sensor Networks

Tracking moving targets is one of the important problems of wireless sensor networks. We have considered a sensor network where numerous sensor nodes are spread in a grid like manner. These sensor nodes are capable of storing data and thus act as a separate datasets. The entire network of these sensors act as a set of distributed datasets. Each of these datasets has its local temporal dataset along with spatial data and the geographical coordinates of a given object or target. In this paper an algorithm is introduced that mines global temporal patterns from these datasets and results in the discovery of linear or nonlinear trajectories of moving objects under supervision. The main objective here is to perform in-network aggregation between the data contained in the various datasets to discover global spatio-temporal patterns; the main constraint is that there should be minimal communication among the participating nodes. We present the algorithm and analyze it in terms of the communication costs

Learning k-Nearest Neighbors Classifier from Distributed Data

Most learning algorithms assume that all the relevant data are available on a single computer site. In the emerging networked environments learning tasks are encountering situations in which the relevant data exists in a number of geographically distributed databases that are connected by communication networks. These databases cannot be moved to other network sites due to security, size, privacy, or data-ownership considerations. In this paper we show how a k-nearest classifier algorithm can be adapted for distributed data situations. The objective of our algorithms is to achieve the learning objectives for any data distribution encountered across the network by exchanging local summaries among the participating nodes

Decomposable Naive Bayes Classifier for Partitioned Data

Most learning algorithms are designed to work on a single dataset. However, with the growth of networks, data is increasingly distributed over many databases in many different geographical sites. These databases cannot be moved to other network sites due to security, size, privacy, or data ownership consideration. In this paper, we propose two decomposable versions of Naive Bayes Classifier for horizontally and vertically partitioned data. The goal of our algorithms is to achieve the learning objectives for any data distribution encountered across the network by exchanging minimum local summaries among the participating sites

Finding Perimeter of Query Regions in Heterogenous Wireless Sensor Networks

Some applications in wireless sensor networks (WSNs) only need to record the information of a target entering or leaving some specific regions of WSNs perimeter. One important issue in this context is to detect the perimeter of the deployed network to ensure that the sensor nodes cover the target area. In this paper we propose two distributed algorithms to elect the perimeter nodes of query regions in a WSN. We consider the most general case, where every sensor has a different sensing radius. We provide performance metrics to analyze the performance of our approach and show by simulation that the proposed algorithms give good performance

An algorithm for enhancing coverage and network lifetime in cluster-based Wireless Sensor Networks

Majority of wireless sensor networks (WSNs) clustering protocols in literature have focused on extending network lifetime and little attention has been paid to the coverage preservation as one of the QoS requirements along with network lifetime. In this paper, an algorithm is proposed to be integrated with clustering protocols to improve network lifetime as well as preserve network coverage in heterogeneous wireless sensor networks (HWSNs) where sensor nodes can have different sensing radii and energy attributes. The proposed algorithm works in proactive way to preserve network coverage and extend network lifetime by efficiently leveraging mobility to optimize the average coverage rate using only the nodes that are already deployed in the network. Simulations are conducted to validate the proposed algorithm by showing improvement in network lifetime and enhanced full coverage time with less energy consumptio

Agents for Integrating Distributed Data for Complex Computations

Algorithms for many complex computations assume that all the relevant data is available on a single node of a computer network. In the emerging distributed and networked knowledge environments, databases relevant for computations may reside on a number of nodes connected by a communication network. These data resources cannot be moved to other network sites due to privacy, security, and size considerations. The desired global computation must be decomposed into local computations to match the distribution of data across the network. The capability to decompose computations must be general enough to handle different distributions of data and different participating nodes in each instance of the global computation. In this paper, we present a methodology wherein each distributed data source is represented by an agent. Each such agent has the capability to decompose global computations into local parts, for itself and for agents at other sites. The global computation is then performed by the agent either exchanging some minimal summaries with other agents or travelling to all the sites and performing local tasks that can be done at each local site. The objective is to perform global tasks with a minimum of communication or travel by participating agents across the network

Nonlinear Trajectory Discovery of a Moving Target by Wireless Sensor Networks

Target tracking is an important cooperative sensing application of wireless sensor networks. In these networks energy, computing power and communication bandwidth are scarce. In this paper, we consider a randomly deployed sensor network with sensors acting as a set of distributed datasets. Each dataset is assumed to have its local temporal dataset, along with spatial data and the geographical coordinates of a given object. An approach towards mines global temporal patterns from these datasets and to discovers nonlinear trajectories of a moving object is proposed. It is tested in a simulation environment and compared with straightforward method. The results of the experiments clearly show the benefits of the new approach in terms of energy consumption

Agents for Integrating Distributed Data for Function Computations

Many practical problems occur when we wish to manipulate the data in a way that requires information not included explicitly in this data, and where we have to deal with functions of such a nature. In a networked environment, the data may reside in components on a number of geographically distributed sites. These databases cannot be moved to other network sites due to security, size, and privacy consideration. In this paper, we present two self-decomposing algorithms for constructing a function from given discrete data, and finding the extrema of any function whose arguments are stored across a number of distributed databases