Power Optimization for Network Localization

Reliable and accurate localization of mobile objects is essential for many applications in wireless networks. In range-based localization, the position of the object can be inferred using the distance measurements from wireless signals exchanged with active objects or reflected by passive ones. Power allocation for ranging signals is important since it affects not only network lifetime and throughput but also localization accuracy. In this paper, we establish a unifying optimization framework for power allocation in both active and passive localization networks. In particular, we first determine the functional properties of the localization accuracy metric, which enable us to transform the power allocation problems into second-order cone programs (SOCPs). We then propose the robust counterparts of the problems in the presence of parameter uncertainty and develop asymptotically optimal and efficient near-optimal SOCP-based algorithms. Our simulation results validate the efficiency and robustness of the proposed algorithms.Comment: 15 pages, 7 figure

Localization of Distributed Wireless Sensor Networks using Two Sage SDP Optimization

A wireless sensor network (WSN) may comprise a large distributed set of low cost, low power sensing nodes. In many applications, the location of sensors is a necessity to evaluate the sensed data and it is not energy and cost efficient to equip all sensors with global positioning systems such as GPS. In this paper, we focus on the localization of sensors in a WSN by solving an optimization problem. In WSN localization, some sensors (called anchors) are aware of their location. Then, the distance measurements between sensors and anchors locations are used to localize the whole sensors in the network. WSN localization is a non-convex optimization problem, however, relaxation techniques such as semi-definite programming (SDP) are used to relax the optimization. To solve the optimization problem, all constraints should be considered simultaneously and the solution complexity order is O(n2) where n is the number of sensors. The complexity of SDP prevents solving large size problems. Therefore, it would be beneficial to reduce the problem size in large and distributed WSNs. In this paper, we propose a two stage optimization to reduce the solution time, while provide better accuracy compared with original SDP method. We first select some sensors that have the maximum connection with anchors and perform the SDP localization. Then, we select some of these sensors as virtual anchors. By adding the virtual anchors, we add more reference points and decrease the number of constraints. We propose an algorithm to select and add virtual anchors so that the total solution complexity and time decrease considerably, while improving the localization accuracy

3-D Hybrid Localization with RSS/AoA in Wireless Sensor Networks: Centralized Approach

This dissertation addresses one of the most important issues present in Wireless Sensor Networks (WSNs), which is the sensor’s localization problem in non-cooperative and cooperative 3-D WSNs, for both cases of known and unknown source transmit power PT . The localization of sensor nodes in a network is essential data. There exists a large number of applications for WSNs and the fact that sensors are robust, low cost and do not require maintenance, makes these types of networks an optimal asset to study or manage harsh and remote environments. The main objective of these networks is to collect different types of data such as temperature, humidity, or any other data type, depending on the intended application. The knowledge of the sensors’ locations is a key feature for many applications; knowing where the data originates from, allows to take particular type of actions that are suitable for each case. To face this localization problem a hybrid system fusing distance and angle measurements is employed. The measurements are assumed to be collected through received signal strength indicator and from antennas, extracting the received signal strength (RSS) and angle of arrival (AoA) information. For non-cooperativeWSN, it resorts to these measurements models and, following the least squares (LS) criteria, a non-convex estimator is developed. Next, it is shown that by following the square range (SR) approach, the estimator can be transformed into a general trust region subproblem (GTRS) framework. For cooperative WSN it resorts also to the measurement models mentioned above and it is shown that the estimator can be converted into a convex problem using semidefinite programming (SDP) relaxation techniques.It is also shown that the proposed estimators have a straightforward generalization from the known PT case to the unknown PT case. This generalization is done by making use of the maximum likelihood (ML) estimator to compute the value of the PT . The results obtained from simulations demonstrate a good estimation accuracy, thus validating the exceptional performance of the considered approaches for this hybrid localization system

FracBot: Design of wireless underground sensor networks for mapping hydraulic fractures and determining reservoir parameters in unconventional systems

Wireless underground sensor networks (WUSNs) enable a wide variety of emerging applications that are not possible with current underground monitoring techniques, which require miniaturized wireless sensor systems for mapping hydraulic fractures, monitoring unconventional reservoirs and measuring other wellbore parameters. We call these devices FracBots (Fracture Robots), an extension of RFID (Radio Frequency IDentifcation) tags that realize WUSNs for mapping and characterization of hydraulic fractures in unconventional reservoirs. The objective of this thesis is to design fully integrated magnetic induction (MI)-based FracBots (WUSNs) that enable reliable and e fficient wireless communications in underground oil reservoirs for performing the in-situ monitoring of oil reservoirs. This is very crucial for determining the sweet spot of oil and natural gas reserves. To this end, we have contributed in four areas as follows: fi rst, we develop a novel cross-layer communication framework for MI-based FracBot networks in dynamically changing underground environments. The framework combines a joint selection of modulation, channel coding, power control and a geographic forwarding paradigm. Second, we develop a novel MI-based localization framework that exploits the unique properties of MI- eld to determine the locations of the randomly deployed FracBot nodes in oil reservoirs. Third, we develop an accurate energy framework of a linear FracBot network topology that generates feasible nodes' transmission rates and network topology while always guaranteeing su fficient energy. Then, we design, develop, and fabricate MI-based FracBot nodes. Finally, to validate the performance of our solutions in our produced prototype of FracBot nodes, we develop a physical MI-based WUSN testbed.Ph.D

Enhanced Performance Cooperative Localization Wireless Sensor Networks Based on Received-Signal-Strength Method and ACLM

There has been a rise in research interest in wireless sensor networks (WSNs) due to the potential for his or her widespread use in many various areas like home automation, security, environmental monitoring, and lots more. Wireless sensor network (WSN) localization is a very important and fundamental problem that has received a great deal of attention from the WSN research community. Determining the relative coordinate of sensor nodes within the network adds way more aiming to sense data. The research community is extremely rich in proposals to deal with this challenge in WSN. This paper explores the varied techniques proposed to deal with the acquisition of location information in WSN. In the study of the research paper finding the performance in WSN and those techniques supported the energy consumption in mobile nodes in WSN, needed to implement the technique and localization accuracy (error rate) and discuss some open issues for future research. The thought behind Internet of things is that the interconnection of the Internet-enabled things or devices to every other and human to realize some common goals. WSN localization is a lively research area with tons of proposals in terms of algorithms and techniques. Centralized localization techniques estimate every sensor node's situation on a network from a central Base Station, finding absolute or relative coordinates (positioning) with or without a reference node, usually called the anchor (beacon) node. Our proposed method minimization error rate and finding the absolute position of nodes

Soft-connected Rigid Body Localization: State-of-the-Art and Research Directions for 6G

This white paper describes a proposed article that will aim to provide a thorough study of the evolution of the typical paradigm of wireless localization (WL), which is based on a single point model of each target, towards wireless rigid body localization (W-RBL). We also look beyond the concept of RBL itself, whereby each target is modeled as an independent multi-point three-dimensional (3D), with shape enforced via a set of conformation constraints, as a step towards a more general approach we refer to as soft-connected RBL, whereby an ensemble of several objects embedded in a given environment, is modeled as a set of soft-connected 3D objects, with rigid and soft conformation constraints enforced within each object and among them, respectively. A first intended contribution of the full version of this article is a compact but comprehensive survey on mechanisms to evolve WL algorithms in W-RBL schemes, considering their peculiarities in terms of the type of information, mathematical approach, and features the build on or offer. A subsequent contribution is a discussion of mechanisms to extend W-RBL techniques to soft-connected rigid body localization (SCW-RBL) algorithms