Abstract—Global synchronization is important for many sensor network applications that require precise mapping of collected sensor data with the time of the events, for example, in tracking and surveillance. It also plays an important role in energy conservation in MAC layer protocols. This paper describes four methods to achieve global synchronization in a sensor network: a node-based approach, a hierarchical cluster-based method, a diffusion-based method, and a fault-tolerant diffusion-based method. The diffusion-based protocol is fully localized. We present two implementations of the diffusion-based protocol for synchronous and asynchronous systems and prove its convergence. Finally, we show that, by imposing some constraints on the sensor network, global clock synchronization can be achieved in the presence of malicious nodes that exhibit Byzantine failures. Index Terms—Sensor networks, fault tolerance. æ

Abstract — This paper targets the identification of faulty sensors and detection of the reach of events in sensor networks with faulty sensors. Typical applications include the detection of the transportation front line of a contamination and the diagnosis of network health. We propose and analyze two novel algorithms for faulty sensor identification and fault-tolerant event boundary detection. These algorithms are purely localized and thus scale well to large sensor networks. Their computational overhead is low, since only simple numerical operations are involved. Simulation results indicate that these algorithms can clearly detect the event boundary and can identify faulty sensors with a high accuracy and a low false alarm rate when as many as 20 % sensors become faulty. Our work is exploratory in that the proposed algorithms can accept any kind of scalar values as inputs, a dramatic improvement over existing works that take only 0/1 decision predicates. Therefore, our algorithms are generic. They can be applied as long as the “events ” can be modelled by numerical numbers. Though designed for sensor networks, our algorithms can be applied to the outlier detection and regional data analysis in spatial data mining.

Abstract—In this paper, we consider two important problems for distributed fault-tolerant detection in wireless sensor networks: 1) how to address both the noise-related measurement error and sensor fault simultaneously in fault-tolerant detection and 2) how to choose a proper neighborhood size n for a sensor node in fault correction such that the energy could be conserved. We propose a fault-tolerant detection scheme that explicitly introduces the sensor fault probability into the optimal event detection process. We mathematically show that the optimal detection error decreases exponentially with the increase of the neighborhood size. Experiments with both Bayesian and Neyman-Pearson approaches in simulated sensor networks demonstrate that the proposed algorithm is able to achieve better detection and better balance between detection accuracy and energy usage. Our work makes it possible to perform energyefficient fault-tolerant detection in a wireless sensor network. Index Terms—Distributed event detection, fault tolerance, sensor fusion, energy-efficiency, wireless sensor networks. 1

Due to their low cost and small form factors, a large number of sensor nodes can be deployed in redundant fashion in dense sensor networks. The availability of redundant nodes increases network lifetime as well as network fault tolerance. It is, however, undesirable to keep all the sensor nodes active at all times for sensing and communication. An excessive number of active nodes leads to higher energy consumption and it places more demand on the limited network bandwidth. We present an efficient technique for the selection of active sensor nodes in dense sensor networks. The active node selection procedure is aimed at providing the highest possible coverage of the sensor field, i.e., the surveillance area. It also assures network connectivity for routing and information dissemination. We first show that the coverage-centric active nodes selection problem is NP-complete. We then present a distributed approach based on the concept of a connected dominating set (CDS). We prove that the set of active nodes selected by our approach provides full coverage and connectivity. We also describe an optimal coverage-centric centralized approach based on integer linear programming. We present simulation results obtained using an ns2 implementation of the proposed technique.

Wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. The way these data are manipulated by the sensor nodes is a fundamental issue. Information fusion arises as a response to process data gathered by sensor nodes and benefits from their processing capability. By exploiting the synergy among the available data, information fusion techniques can reduce the amount of data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. In this work, we survey the current state-of-the-art of information fusion by presenting the known methods, algorithms, architectures, and models of information fusion, and

Abstract – The main contribution of this paper is an algorithm for autonomous search that minimizes the expected time for detecting multiple targets present in a known built environment. The proposed technique makes use of the probability distribution of the target(s) in the environment, thereby making it feasible to incorporate any additional information, known a-priori or acquired while the search is taking place, into the search strategy. The environment is divided into a set of distinct regions and an adjacency matrix is used to describe the connections between them. The costs of searching any of the regions as well as the cost of travel between them can be arbitrarily specified. The search strategy is derived using a dynamic programming algorithm. The effectiveness of the algorithm is illustrated using an example based on the search of an office environment. An analysis of the computational complexity is also presented. Index Terms – Multiple targets, target search, dynamic programming, topological map, probability distribution

Abstract—We present a new information dissemination protocol for wireless sensor networks. This protocol uses location information to reduce redundant transmissions, thereby saving energy. The sensor network is divided into virtual grids and each sensor node associates itself with a virtual grid based on its location. Sensor nodes within a virtual grid are classified as either gateway nodes or internal nodes. While gateway nodes are responsible for forwarding the data across virtual grids, internal nodes forward the data within a virtual grid. The proposed approach, termed location-aided flooding (LAF), achieves energy savings by reducing the redundant transmissions of the same packet by a node. We study the performance of LAF for different grid sizes and different node densities and compare it to other well-known methods. We show that LAF can save a significant amount of energy compared to prior methods. Index Terms—Communication protocol, location, energy management, information dissemination, flooding. 1

The influence field of an object, a commonly exploited feature in science and engineering applications, is the region where the object is detectable by a given sensing modality. Being spatially distributed, this feature allows us to tradeoff nodal computation with network communication. By the same token, not only is its calculation subject to nodal failures and false detections, but also to channel fading and channel contention. In this paper, we study how to accurately and efficiently estimate the influence fields of objects in such an unreliable setting and how this reliable estimation of influence fields can be used to classify and track different types of objects. We derive, for node and network fault models, the necessary nodal density for reliably estimating the influence fields so that objects can be classified and tracked. We present four algorithmic techniques:

Abstract—A Cyber-Physical System (CPS) integrates physical devices (e.g., sensors, cameras) with cyber (or informational) components to form a situation-integrated analytical system that responds intelligently to dynamic changes of the real-world scenarios. One key issue in CPS research is trustworthiness analysis of the observed data: Due to technology limitations and environmental influences, the CPS data are inherently noisy that may trigger many false alarms. It is highly desirable to sift meaningful information from a large volume of noisy data. In this paper, we propose a method called Tru-Alarm which finds out trustworthy alarms and increases the feasibility of CPS. Tru-Alarm estimates the locations of objects causing alarms, constructs an object-alarm graph and carries out trustworthiness inferences based on linked information in the graph. Extensive experiments show that Tru-Alarm filters out noises and false information efficiently and guarantees not missing any meaningful alarms. I.

Abstract — One of the main challenges in wireless sensor networks is to provide low-cost, low-energy reliable data collection. Reliability against transient errors in sensor data can be provided using the model-based error correction described in [1], in which temporal correlation in the data is used to correct errors without any overheads at the sensor nodes. In the above work it is assumed that a perfect model of the data is available. However, as variations in the physical process are context-dependent and time-varying in a real sensor network, it is infeasible to have an accurate model of the data properties a priori, thus leading to reduced correction efficiency. In this paper, we address this issue by presenting a scalable methodology for improving the accuracy of data modeling through on-line estimation and model updates. Additionally, we propose enhancements to the data correction algorithm to incorporate robustness against dynamic model changes and potential modeling errors. We evaluate our system through simulations using real sensor data collected from different sources. Experimental results demonstrate that the proposed enhancements lead to an improvement of up to a factor of 10 over the earlier approach. I.