On geometric upper bounds for positioning algorithms in wireless sensor networks

This paper studies the possibility of upper bounding the position error for range-based positioning algorithms in wireless sensor networks. In this study, we argue that in certain situations when the measured distances between sensor nodes have positive errors, e.g., in non-line-of-sight (NLOS) conditions, the target node is confined to a closed bounded convex set (a feasible set) which can be derived from the measurements. Then, we formulate two classes of geometric upper bounds with respect to the feasible set. If an estimate is available, either feasible or infeasible, the position error can be upper bounded as the maximum distance between the estimate and any point in the feasible set (the first bound). Alternatively, if an estimate given by a positioning algorithm is always feasible, the maximum length of the feasible set is an upper bound on position error (the second bound). These bounds are formulated as nonconvex optimization problems. To progress, we relax the nonconvex problems and obtain convex problems, which can be efficiently solved. Simulation results show that the proposed bounds are reasonably tight in many situations, especially for NLOS conditions

Robust Distributed Positioning Algorithms for Cooperative Networks

The problem of positioning targets based on distance estimates is studied for cooperative wireless sensor networks when there is limited a priori information about measurements noise. To solve this problem, two different methods of positioning are considered: statistical and geometrical. Based on a geometric interpretation, we show that the positioning problem can be rendered as finding the intersection of a number of convex sets. To find this intersection, we propose two different methods based on projection onto convex sets and outer-approximation. In the statistical approach, a partly novel two-step linear estimator is proposed which can be expressed in a closed-form solution. We also propose a new constrained non-linear least squares algorithm based on constraints derived in the outer-approximation approach. Simulation results show that the geometrical methods are more robust against non-line-of-sight measurements than the statistical approaches while in dense networks with line-of-sight measurements statistical approaches outperform geometrical methods

Role of regulatory T cells in acute myeloid leukemia patients undergoing relapse-preventive immunotherapy

Regulatory T cells (Tregs) have been proposed to dampen functions of anti-neoplastic immune cells and thus promote cancer progression. In a phase IV trial (Re:Mission Trial, NCT01347996, http://www.clinicaltrials.gov ) 84 patients (age 18-79) with acute myeloid leukemia (AML) in first complete remission (CR) received ten consecutive 3-week cycles of immunotherapy with histamine dihydrochloride (HDC) and low-dose interleukin-2 (IL-2) to prevent relapse of leukemia in the post-consolidation phase. This study aimed at defining the features, function and dynamics of Foxp3+CD25highCD4+ Tregs during immunotherapy and to determine the potential impact of Tregs on relapse risk and survival. We observed a pronounced increase in Treg counts in peripheral blood during initial cycles of HDC/IL-2. The accumulating Tregs resembled thymic-derived natural Tregs (nTregs), showed augmented expression of CTLA-4 and suppressed the cell cycle proliferation of conventional T cells ex vivo. Relapse of AML was not prognosticated by Treg counts at onset of treatment or after the first cycle of immunotherapy. However, the magnitude of Treg induction was diminished in subsequent treatment cycles. Exploratory analyses implied that a reduced expansion of Tregs in later treatment cycles and a short Treg telomere length were significantly associated with a favorable clinical outcome. Our results suggest that immunotherapy with HDC/IL-2 in AML entails induction of immunosuppressive Tregs that may be targeted for improved anti-leukemic efficiency

Clock-offset Cancellation Methods for Positioning in Asynchronous Sensor Networks

For most applications of wireless sensor networks, knowledge about the position of sensors relative to other sensors in the network, or to some global coordinate system, is a key ingredient to successful operation of the network. Estimation of relative node coordinates, based on measured time-of-flight between neighbouring nodes, has been suggested as a means to provide position-awareness in sensor networks where satellite based systems are not available. However, to directly measure the inter-node distances, based on RF or ultra-sound propagation delay, requires the nodes to be tightly synchronized in time. This is an assumption that is not easily justified in sensor networks operating under complexity, latency, power consumption or bandwidth constraints. Joint ML estimation of clock-offsets and node coordinates has been suggested, but, although this approach shows great promise in terms of coordinate estimation accuracy, it does not scale well as sensor networks grow in size. In this paper, we present two linear preprocessing operations that cancels the effect of unknown clock-offsets from the estimation problem. For both operations, we show that the Fisher information on node coordinates in the original data-set remains unchanged after preprocessing. Novel ML estimators of relative node coordinates are proposed, that are of significantly lower complexity, while their performance equals that of the joint ML estimator. \ua9 2005 IEEE

Low Complexity Tracking for Ad-hoc Automotive Sensor Networks

The main focus of this paper is to investigate how the co-operative nature of an ad-hoc sensor network can be exploited in order to reduce the complexity of accurate node locationing algorithms in sensor networks. We propose a new approach to target tracking called nodeaided tracking that, unlike radar-like methods, exploits the two-way communication link of an ad-hoc sensor network. Further, we present a novel way of evaluating the performance of tracking filters used in automotive safety applications. This new measure, called the time margin difference, takes not only the mean-squared-error into account but also latency in providing location estimates and other important filter characteristics for a fair comparison between different tracking algorithms designed for automotive safety applications

Node-aided Locationing and Tracking for Low Cost Ad-hoc Automotive Sensor Networks

The main focus of this paper is to investigate how the co-operative nature of an ad-hoc sensor network can be exploited in order to reduce the complexity of accurate node locationing algorithms in sensor networks. We propose a new approach to target tracking called node-aided tracking that, unlike radar-like methods, exploits the two-way communication link of an ad-hoc sensor network