Cooperative robust localization against malicious anchors based on semi-definite programming

We present semi-decentralized and distributed algorithms, designed via a preconditioned forward-backward operator splitting, for solving large-scale, decomposable semi-definite programs (SDPs). We exploit a chordal aggregate sparsity pattern assumption on the original SDP to obtain a set of mutually coupled SDPs defined on positive semidefinite (PSD) cones of reduced dimensions. We show that the proposed algorithms converge to a solution of the original SDP via iterations of reasonable computational cost, numerically comparing their performances with respect to others available in the literature.

This paper is an attempt to exploit the opportunity of Semi Definite Programming (SDP), which is an area of convex and conic optimization. Indeed, Numerous NP-hard problems can be solveby using this approach. Hence, we intend to investigate the strength of SDP to model and provide tight relaxations of combinatorial and quadratic problems in order to present a new polynomial time algorithm for solving this robust model. This algorithm was firstly use to solve the nonlinear programs, the reason for which we search to extend it to the SDP programs. Actually, this algorithm designs the combination of two penalization methods. The first one is a primal-dual interior point (PDIM) method while the second one is a primal-dual exterior point (PDEM) method. Unlike the first method, which converges globally, the second one, also called the primal dual nonlinear rescaling method, has local super linear/quadratic convergence. Therefore, it seems appropriate to use a mixed algorithm based on the interior-exterior point method (IEPM). In fact, this resolution starts from the interior method, and at a certain level of execution, it proceeds to exterior method. Hence, a convergence evaluation function is use to know the level of permutation. Through evaluation, it has been approve that our approach is use to solve some instances of max-cut problem. This problem is a central graph theory model that occurs in many real problems and it is one of many NP-hard problems, which has attracted many researchers over the years. Then, we have used a semi definite programming solver SDPA (Semi Definite Programming Algorithm) that is modify to include the exterior point method subroutine. From the computational performance, we conclude that as the problem size increases, interior-exterior point algorithm gets relatively faster. The numerical results obtained are promising.

Enhanced 911 is a first line of assistance for practically every emergency situation, and many cell phone users today expect the same results from an emergency call no matter where they are-whether on the side of the road, in the woods, or in a building. It is a vital part of our nation's emergency response and disaster preparedness system. In the context of 911 service, demand for providing reliable and accurate mobile station (MS) location estimation has become a high priority and has gained momentum in recent years. A major challenge in mobile station location estimation is locating an emergency caller within desired accuracy in an adverse environment where non-line-of-site (NLOS) propagation exists. This paper develops a methodology to improve the accuracy of mobile station location estimation in an NLOS environment. A unique feature of this methodology development, compared to other approaches in the literature, is the application of the systems engineering process. While there are many definitions, systems engineering as applied here is an approach and process for developing the preferred solution to a set of requirements. The methodology consists of two stages. In the first stage, a series of time-of-arrival range measurements are made from each base station (BS) to the MS. Binary hypothesis testing on the standard deviation of the range measurements at a given BS is used to determine if the measures are taken under NLOS conditions. Then, if possible, any BS deemed to be NLOS is eliminated from the estimation in the second stage, in which the selected time measurements of several BSs are combined through least squares to estimate the location of the mobile station. Based on a simulation study, the methodology appears to have the potential to significantly improve the accuracy of location estimates in certain situations.

To provide valuable services in ubiquitous and pervasive computing, it is necessary to estimate the location of users or objects in the environment. The Global Positioning System (GPS) has supported many applications using outdoor location estimation, but there is still a need for alternative technologies in buildings. Assuming that multiple wireless sensors will be attached to ubiquitous environments, they could also support location estimation and tracking. This paper presents optimizations for an indoor location tracking system using wireless sensors, called LocSens. LocSens works with a minimum number of sensor nodes. It is established and tested in a real indoor scenario over multiple rooms. LocSens could be improved by optimizing algorithms and using more precise sensor boards. Results confirm improved accuracy in location estimation and tracking of moving objects.

In this paper, we introduce a rank-one Semi-Definite Programming (SDP) solution method for mobile source localization in sensor networks. The position and velocity of mobile source are jointly estimated using Time Delay (TD) measurements. To obtain the position and velocity of mobile source, a Relaxed Semi-Definite Programming (RSDP) algorithm is firstly designed by dropping the rank-one constraint. However, dropping the rank-one constraint leads to produce a suboptimal solution. To improve the performance, we further put forward the Penalty Function Semi-Definite Programming (PF-SDP) method to obtain the rank-one solution of the estimation problem by introducing the penalty terms. By adaptively choosing the penalty coefficient, an Adaptive Penalty Function Semi-Definite Programming (APF-SDP) algorithm is also proposed to avoid the excessive penalty. We also conduct experiments in both a simulated environment and a real system to demonstrate the effectiveness of the proposed methods. The results have demonstrated that the proposed APF-SDP outperforms the PF-SDP in terms of the position and velocity estimation whether the noise level is large or not.

Decaying with the increasing of signal propagation distance, Received Signal Strength (RSS) is used in the wireless localization due to its low cost and easily implementation.When the transmit power is unavailable, two convex optimization algorithms including semi definite programming (SDP), second order cone and semi definite programming (SOC/SDP) are designed to estimate the source locations by relaxing the non-convex problem as convex optimization.The corresponding Cramér-Rao lower bound (CRLB) of the problem is derived.The simulations demonstrate that the SOC/SDP algorithm provides the similar accuracy performance compared with the SDP algorithm.However, the computational complexity of SOC/SDP is lower than that of the SDP due to the less variables and equality constraints.When perfect knowledge of the path loss exponent is available, the simulations also show that the accuracy performance of the proposed convex optimization algorithms degrades as the path loss exponent increases.

In wireless sensor networks, location estimation of sensor nodes is important. Therefore some location estimation methods have been proposed. Especially, in wireless sensor networks based on IEEE802.15.4, a method using received signal strength indicator (RSSI) which can be measured by each sensor node is one of the most practicable methods in the way that new hardware is not needed and it is suitable also for the miniaturization. However this technique has a weak point that it needs to investigate the propagation environment of the sensor field to estimate the location of sensor nodes in advance. This paper proposes the method that the decision of the parameter of the propagation environment using only measurements obtained during localization without investigating prior. Evaluation of this method is shown by simulation and experiment that it is able to decide the propagation parameter of an extent that doesnpsilat influence the location estimation accuracy without investigating the propagation environment of the sensor field.

In wireless sensor networks, location estimation of sensor nodes is important. Therefore some location estimation methods have been proposed. Especially, in wireless sensor networks based on IEEE802.15.4, a method using received signal strength indicator (RSSI) which can be measured by each sensor node is one of the most practicable methods in the way that new hardware is not needed and it is suitable also for the miniaturization. However this technique has a weak point that it needs to investigate the propagation environment of the sensor field to estimate the location of sensor nodes in advance. This paper proposes the method that the decision of the parameter of the propagation environment using only measurements obtained during localization without investigating prior. Evaluation of this method is shown by simulation that it is able to decide the propagation parameter of an extent that doesn't influence the location estimation accuracy without investigating the propagation environment of the sensor field.

This study presents an energy-efficient location estimation method aimed for mobile nodes in wireless sensor networks. The proposed method is a combination of two operations. Trilateration method is combined with a vector based incremental updates which is implemented by using a digital compass and a speedometer to estimate the location of the mobile node. This combined operation decreases the power consumed from the mobile node trying to locate itself. The proposed method has been implemented on an arduino-based mobile robot with wireless communication peripherals. The implementation shows that the location estimation accuracy is between 0.69-1.97 m from the actual location of the mobile node. The average location estimation error is comparable to other proposed methods for locating mobile sensor nodes. Based on the actual measurement of the test system, the energy consumption of the proposed method is 20% less than the trilateration method alone.

Sensor positioning is an important task of location-aware wireless sensor networks. In most sensor positioning systems, sensors and beacons need to emit ranging signals to each other. Sensor ranging energy should be low to prolong system lifetime, but sufficiently high to fulfill prescribed accuracy requirements. This motivates us to investigate ranging energy optimization problems. We address ranging energy optimization for an unsynchronized positioning system, which features robust sensor positioning (RSP) in the sense that a specific accuracy requirement is fulfilled within a prescribed service area. We assume a line-of-sight (LOS) channel exists between the sensor and each beacon. The positioning is implemented by time-of-arrival (TOA) based two-way ranging between a sensor and beacons, followed by a location estimation at a central processing unit. To establish a dependency between positioning accuracy and ranging energy, we assume the adopted TOA and location estimators are unbiased and attain the associated Cramer-Rao bound. The accuracy requirement has the same form as the one defined by the Federal Communication Commission (FCC), and we present two constraints with linear-matrix-inequality form for the RSP. Ranging energy optimization problems, as well as a practical algorithm based on semidefinite programming are proposed. The effectiveness of the algorithm is illustrated by numerical experiments.

The global positioning system (GPS) was designed to provide location estimates for various civilian and military applications using at least four satellites. Since GPS signals have a low signal-to-noise ratio (SNR), they also have a low signal-to-jammer ratio so that the accuracy of location estimates is influenced by cochannel interference and intentional jammers. We propose a low-complexity blind adaptive receiver that is based on a novel modified despreader and the constant modulus (CM) array. This system is capable of nulling directional interference and capturing the GPS signal of interest without requiring explicit angle-of-arrival (AOA) information. We also consider the multiple satellite problem and extend the proposed receiver to capture several GPS signals of interest. Representative computer simulation examples are presented to illustrate the performance of the multicomponent system for the suppression of different jammer types.

In indoor location environment, shadowing, multipath and reflection phenomena make the position estimation inaccurate. Large number of Access Points (APs) equipped with single antenna could obtain enough Receive Signal Strength (RSS) information to describe signal characteristics of the target space. However, it results in a large investment costs. This paper provides a novel indoor positioning solution using the multi-antennas architecture to increase the accuracy of location estimation. The system obtains enough RSS to compute the user location and provides a reasonable cost of deployments in indoor location environment. Experimental and numerical results show that the proposed indoor location estimation demonstrates a high accuracy of positioning and outstanding performance when RSS is collected by smart antenna technique. More specifically, the accuracy is increased from 1.34% to 92.4% and the error variance is reduced by 96% as compared to the traditional location fingerprint method.

최근 위치 정보의 이용이 여러 분야에 걸쳐 급격하게 증가하고 있다. 실외에서는 위치 정보의 획득을 위해 일반적으로 GPS 기술을 사용하였으나, 복잡한 실내 공간에서는 벽, 기둥과 같은 물리적인 장애물들로 인해 발생한 다중경로 간섭으로 무선 근거리통신망, RFID, 블루투스 등의 무선 네트워크 기술을 적용한 연구가 진행되었다. 그러나 이러한 위치 측정 기술들은 센싱 인프라스트럭쳐 구축비용이 많이 들며, 측위에 있어 계산 집약적이고, 실내 구조에 따른 정확도의 변화가 발생하는 한계가 존재한다. 본 연구에서는 이러한 한계점을 보완하고자 복합용도건물 내에서 쉽게 획득 및 식별이 가능한 상가, 컨벤션 센터 및 오피스의 전화번호, 방 번호, 상호명과 같은 서술 데이터를 이용하여 위치를 추출한다. 이 과정에서 문자 매칭을 활용하며, 위치 추정에 있어 신속한 계산과 실내 환경에 따른 정확도 변화를 배제하기 위해 지오코딩 방법을 적용한다. 본 연구에서 제안된 방법을 아키텍쳐로 설계하며, 구현을 위해 3차원 가시화 프로그램을 개발한다. 또한 제한된 기법에서의 매칭률, 프로세싱 시간을 통하여 정략적으로 평가한다. Recently, the use of locational information is growing rapidly. GPS technology has been adopted generally for obtaining locational information in outdoor spaces. In the other hand, the researches on indoor positioning have been carried out applying WLAN, RFID or Bluetooth technology because of the multi-path interference of GPS signal caused by the physical obstacles such as walls or columns in buildings. However, such technologies for indoor positioning cost too much to build sensing infrastructure and compute-intensive processes are involved. Furthermore, the accuracy of location estimation is variable caused by interior structures in buildings. In this study, to make up for the limitations, descriptive data such as phone number, unique room numbers, or business names readily available in mixed-use buildings is used for extracting location information. Furthermore, during the process, a geocoding method using character matching is applied to this study enabling prompt location estimation and sublating the fluctuation of accuracy caused by interior structures. Based on the proposed method in this study, an architecture is designed, and three-dimensional viewer program is developed for the implementation of this study. Also, this research is quantitatively analyzed through match rate and processing time of proposed method.

SUMMARYRemote surveillance of large‐scale equipment such as power plants and building complexes is important to prevent serious attacks and troubles. Automatic human action recognition can reduce the burdens of the surveillance. Multiview video is useful for human action recognition, because it provides robustness to the changes of people's appearance by orientation and occlusion. One problem of conventional multiview action recognition is that it requires both detection and tracking before action recognition. Human pose and motion vary depending on the person's action, and such variances may cause detection and tracking error. To solve this problem, previous work has proposed simultaneous action recognition and location estimation for single‐view videos using Hough voting. In this paper, we extend the Hough voting approach to simultaneous multiview action recognition and location estimation. Our proposed method independently casts votes for the action labels and spatiotemporal reference positions of actions in each view and integrates them using homographical transformations in the multiview extension. We evaluated our method and confirmed that it achieved high accuracy in action recognition and location estimation. The contribution of this paper is that it enables multiview action recognition without prior human detection and tracking.

Micro-aerial vehicle (MAV) swarms are a new class of mobile sensor networks with many applications, including search and rescue, urban surveillance, radiation monitoring, etc. These sensing applications require autonomously navigating a high number of low-cost, low-complexity MAV sensor nodes in hazardous environments. The lack of preexisting localization infrastructure and the limited sensing, computing, and communication abilities of individual nodes makes it challenging for nodes to autonomously navigate to suitable preassigned locations. In this paper, we present a collaborative and adaptive algorithm for resource-constrained MAV nodes to quickly and efficiently navigate to preassigned locations. Using radio fingerprints between flying and landed MAVs acting as radio beacons, the algorithm detects intersections in trajectories of mobile nodes. The algorithm combines noisy dead-reckoning measurements from multiple MAVs at detected intersections to improve the accuracy of the MAVs' location estimations. In addition, the algorithm plans intersecting trajectories of MAV nodes to aid the location estimation and provide desired performance in terms of timeliness and accuracy of navigation. We evaluate the performance of our algorithm through a real testbed implementation and large-scale physical feature based simulations. Our results show that, compared to existing autonomous navigation strategies, our algorithm achieves up to 6X reduction in location estimation errors, and as much as 3X improvement in navigation success rate under the given time and accuracy constraints.