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Network localization and navigation (NLN) is a promising paradigm, in which mobile nodes exploit spatiotemporal cooperation, to provide reliable location information for a diverse range of wireless applications. This paper presents a theoretical foundation of NLN, including a mathematical formulation for NLN, an introduction of equivalent Fisher information analysis, and determination of the fundamental limits of localization accuracy. Key ingredients such as spatiotemporal cooperation, array signal processing, and map exploitation are then studied. We also develop a geometric interpretation to provide insights into the essence of NLN for network design. Finally, the paper highlights the connection between the theoretical foundation and algorithm development for NLN, guiding the design and operation of practical localization systems. [ABSTRACT FROM AUTHOR]

Both localization and synchronization of mobile nodes are important for wireless networks. In this paper, we propose new methods for network localization and synchronization (NLS) using full-duplex radios through only two frames of transmission. Specifically, all nodes simultaneously transmit their signature signals in the first frame, while receiving others’ signals via full-duplex radios. In the second frame, nodes transmit either scrambled versions of their received signals in the first frame or a digital packet of the channel parameter estimates of the received signals. We develop distributed algorithms to estimate the arrival times of different components in the received signals. These arrival times are then used to determine the local network geometry and clock offsets. The Cramér-Rao lower bounds for internode distances and clock offsets are derived, and the former can be translated into error bounds of the node positions. Compared with conventional frequency division duplex or time-division duplex, we demonstrate the high efficiency of NLS using full-duplex radios, revealing its potential beyond data communications in future wireless networks. [ABSTRACT FROM AUTHOR]

To localize the wireless sensor networks nodes, only the hop-based information (i.e., hops’ number, average hop size, and so on) has been, so far, exploited by range-free techniques, with poor-accuracy, however. In this paper, we show that localization accuracy may greatly benefit from joint exploitation, at no cost, of the information already provided by the forwarding nodes (i.e., relays) between each anchor (i.e., position aware) and sensor nodes pair. As such, we develop a novel range-free localization algorithm, derive its average location estimation error (LEE) in closed-form, and compare it in LEE performance with the best representative algorithms in the literature. We show that the proposed algorithm outperforms them in accuracy. In contrast to the latter, we further prove that it is able to achieve an LEE average and variance of about 0 when the number of sensors is large enough, thereby achieving an unprecedented accuracy performance among range-free techniques. [ABSTRACT FROM PUBLISHER]

Reliable and accurate position information is of great importance for many mass-market and emerging applications. Network localization and navigation (NLN) is a promising paradigm to provide such information ubiquitously, where a network of nodes is used to aid in localizing its members. This paper explores various network operation strategies, which play an essential role in NLN as they determine the network lifetime and localization accuracy. Efficient network operation requires several functionalities, including node prioritization, node activation, and node deployment. The roles of these functionalities are described and different techniques for implementing respective functionalities via algorithmic modules are introduced. Some important concepts such as cooperative operation, robustness guarantee, and distributed design in the development of the network operation strategies are also introduced. Finally, numerical results are provided to demonstrate the localization performance improvement attributed to the optimized network operation strategies. [ABSTRACT FROM AUTHOR]

In this paper, optimal power allocation strategies are investigated for jammer nodes in a wireless localization system. Building upon the concept of the restricted Bayesian approach, a generalized optimization strategy, called the restricted scheme, is proposed for power allocation of jammer nodes, and its theoretical properties are characterized. In the restricted scheme, the aim is to maximize the average Cramer-Rao lower bound (CRLB) of target nodes while keeping their minimum CRLB above a predefined level in the presence of average (total) and peak power constraints. It is proved that the average CRLB achieved by the restricted scheme is a strictly decreasing and concave function of the constraint on the minimum CRLB level. A closed-form solution is obtained for the restricted scheme when the tradeoff parameter and the total power limit are below certain thresholds. In addition, it is shown that the optimal solution of the restricted scheme corresponds to the use of at most NT jammer nodes, where NT is the number of target nodes, and that the optimal solution of the minimum CRLB maximization scheme is determined by at most NJ target nodes, where NJ is the number of jammer nodes. Extensions of the restricted scheme and an alternative scheme that aims to maximize the number of disabled target nodes (whose CRLBs are above a preset level) are considered, and the corresponding optimal strategies for jammer power allocation are identified. Numerical examples are provided to verify the theoretical derivations for various network configurations. [ABSTRACT FROM AUTHOR]

Existing localization approaches are divided into two groups: 1) range based and 2) range free. The range-free schemes often suffer from poor accuracy and low scalability, whereas the range-based localization approaches heavily depend on extra hardware capabilities or on the absolute received signal strength indicator (RSSI) values, which is far from practical. In this paper, we propose a mobile-assisted localization scheme called Perpendicular Intersection (PI), setting up a delicate tradeoff between range-free and range-based approaches. Instead of directly mapping RSSI values into physical distances, by contrasting RSSI values from the mobile beacon to a sensor node, PI utilizes the geometric relationship of a perpendicular intersection to compute node positions. We have implemented the prototype of PI with 100 TelosB motes and evaluated PI in both indoor and outdoor environments. Through comprehensive experiments, we show that PI achieves high accuracy, significantly outperforming the existing range-based and the mobile-assisted localization schemes. [ABSTRACT FROM PUBLISHER]

Area-based localization algorithms use only the position of some reference nodes, called anchors, to estimate the residence area of the remaining nodes. Existing algorithms use a triangle, a ring or a circle as the geometric shape that defines the node’s residence area. However, existing algorithms suffer from two major problems: (1) in some cases, they might make wrong decisions about a node presence inside a given area, or (2) they require high anchor density to achieve a low location estimation error and high ratio of localizable nodes. In this paper, we overcome these shortcomings by introducing a new approach for determining the node’s residence area that is geometrically shaped as a half-symmetric lens. A novel half symmetric lens based localization algorithm (HSL) is proposed. HSL yields smaller residence areas, and consequently, better location accuracy than contemporary schemes. HSL further employs Voronoi diagram in order to boost the percentage of localizable nodes. The performance of HSL is validated through mathematical analysis, extensive simulations experiments and prototype implementation. The validation results confirm that HSL achieves better location accuracy and higher ratio of localizable nodes compared to competing algorithms. [ABSTRACT FROM AUTHOR]

The information available to connectivity-based positioning algorithms is the radio range of sensor devices and the position estimates of neighbors and neighbors of neighbors. This information creates special graph theoretic structures which impose new constraints on the positions of sensor devices. The new constraints sometimes lead to a feasible set of positions with disconnected regions. These properties can be used to reduce the set of feasible positions for a node. In this paper, a new fully distributed positioning algorithm, called Orbit, which exploits these properties is presented for mobile sensor networks. The algorithm uses additional constraints and trims disconnected regions. These new constraints are generated through cooperation between neighbors. The performance of Orbit is examined for many communication and mobility models, including a probabilistic communication model generated from radio experiments. Computer simulation experiments demonstrate that Orbit outperforms a recently proposed positioning algorithm in terms of positional accuracy under different models with a wide range of parameter values. Orbit is implemented on resource limited sensor devices. This implementation demonstrates the feasibility of the algorithm for sensor devices. The algorithm is tested on deployments of the sensor devices in a field and the results are comparable to those from the simulation experiments. [ABSTRACT FROM AUTHOR]

Transceiver-free object tracking is to trace a moving object that does not carry any communication device in an environment with some monitoring nodes predeployed. Among all the tracking technologies, RF-based technology is an emerging research field facing many challenges. Although we proposed the original idea, until now there is no method achieving scalability without sacrificing latency and accuracy. In this paper, we put forward a real-time tracking system RASS, which can achieve this goal and is promising in the applications like the safeguard system. Our basic idea is to divide the tracking field into different areas, with adjacent areas using different communication channels. So, the interference among different areas can be prevented. For each area, three communicating nodes are deployed on the ceiling as a regular triangle to monitor this area. In each triangle area, we use a Support Vector Regression (SVR) model to locate the object. This model simulates the relationship between the signal dynamics caused by the object and the object position. It not only considers the ideal case of signal dynamics caused by the object, but also utilizes their irregular information. As a result, it can reach the tracking accuracy to around 1 m by just using three nodes in a triangle area with 4 m in each side. The experiments show that the tracking latency of the proposed RASS system is bounded by only about 0.26 m. Our system scales well to a large deployment field without sacrificing the latency and accuracy. [ABSTRACT FROM AUTHOR]

The knowledge of sensors' locations is crucial information for many applications in Wireless Sensor Networks (WSNs). When sensor nodes are deployed in hostile environments, the localization schemes are vulnerable to various attacks, e.g., wormhole attack, pollution attack, range enlargement/reduction attack, and etc. Therefore, sensors' locations are not trustworthy and need to be verified before they can be used by location-based applications. Previous verification schemes either require group-based deployment knowledge of the sensor field, or depend on expensive or dedicated hardware, thus they cannot be used for low-cost sensor networks. In this paper, we propose a lightweight location verification system that performs both “on-spot” and “in-region” location verifications. The on-spot verification intends to verify whether the locations claimed by sensors are far from their true spots beyond a certain distance. We propose two algorithms that detect abnormal locations by exploring the inconsistencies between sensors' claimed locations and their neighborhood observations. The in-region verification verifies whether a sensor is inside an application-specific verification region. Compared to on-spot verification, the in-region verification is tolerable to large errors as long as the locations of sensors don't cause the application to malfunction. We study how to derive the verification region for different applications and design a probabilistic algorithm to compute in-region confidence for each sensor. Experiment results show that our on-spot and in-region algorithms can verify sensors' locations with high detection rate and low false positive rate. They are robust in the presence of malicious attacks that are launched during the verification process. Moreover, compared with previous verification schemes, our algorithms are effective and lightweight because they do not rely on the knowledge of deployment of sensors, and they don't require expensive or dedicated hardware, so our algorithms can be used in any low-cost sensor networks. [ABSTRACT FROM AUTHOR]

Location awareness is highly critical for wireless ad-hoc and sensor networks. Many efforts have been made to solve the problem of whether or not a network can be localized. Nevertheless, based on the data collected from a working sensor network, it is observed that the network is not always entirely localizable. Theoretical analyses also suggest that, in most cases, it is unlikely that all nodes in a network are localizable, although a (large) portion of the nodes can be uniquely located. Existing studies merely examine whether or not a network is localizable as a whole; yet two fundamental questions remain unaddressed: First, given a network configuration, whether or not a specific node is localizable? Second, how many nodes in a network can be located and which are them? In this study, we analyze the limitation of previous works and propose a novel concept of node localizability. By deriving the necessary and sufficient conditions for node localizability, for the first time, it is possible to analyze how many nodes one can expect to locate in sparsely or moderately connected networks. To validate this design, we implement our solution on a real-world system and the experimental results show that node localizability provides useful guidelines for network deployment and other location-based services. [ABSTRACT FROM PUBLISHER]

Wireless sensor networks are an inherent part of decision making, object tracking, and location awareness systems. This work is focused on simultaneous localization of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multimodel auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localization accuracy is demonstrated. [ABSTRACT FROM AUTHOR]