Farah Mourad

Anchor-Based Localization via Interval Analysis for Mobile Ad-Hoc Sensor Networks

Location awareness is a fundamental requirement for many applications of sensor networks. This paper proposes an original technique for self-localization in mobile ad-hoc networks. This method is adapted to the limited computational and memory resources of mobile nodes. The localization problem is solved in an interval analysis framework. The propagation of the estimation errors is based on an interval formulation of a state space model, where observations consist of anchor-based connectivities. The problem is then formulated as a constraint satisfaction problem where a simple Waltz algorithm is applied in order to contract the solution. This technique yields a guaranteed and robust online estimation of the mobile node positions. Observation errors as well as anchor node imperfections are taken into consideration in a simple and computational-consistent way. Multihop anchor-based and backpropagated localizations are also made possible in our method. Simulation results on mobile node trajectories corroborate the efficiency of the proposed technique and show that it outperforms the particle filtering methods.

Controlled Mobility Sensor Networks for Target Tracking Using Ant Colony Optimization

In mobile sensor networks, it is important to manage the mobility of the nodes in order to improve the performances of the network. This paper addresses the problem of single target tracking in controlled mobility sensor networks. The proposed method consists of estimating the current position of a single target. Estimated positions are then used to predict the following location of the target. Once an area of interest is defined, the proposed approach consists of moving the mobile nodes in order to cover it in an optimal way. It thus defines a strategy for choosing the set of new sensors locations. Each node is then assigned one position within the set in the way to minimize the total traveled distance by the nodes. While the estimation and the prediction phases are performed using the interval theory, relocating nodes employs the ant colony optimization algorithm. Simulations results corroborate the efficiency of the proposed method compared to the target tracking methods considered for networks with static nodes.

Wireless sensor networks in biomedical: Body area networks

The rapid growth in biomedical sensors, low-power circuits and wireless communications has enabled a new generation of wireless sensor networks: the body area networks. These networks are composed of tiny, cheap and low-power biomed-ical nodes, mainly dedicated for healthcare monitoring applications. The objective of these applications is to ensure a continuous monitoring of vital parameters of patients, while giving them the freedom of motion and thereby better quality of healthcare. This paper shows a comparison of body area networks to the wireless sensor networks. In particular, it shows how body area networks borrow and enhance ideas from wireless sensor networks. A study of energy consumption and heat absorption problems is developed for illustration.

Interval-Based Localization using RSSI Comparison in MANETs

The knowledge of node positions is essential to many applications of wireless sensor networks. We propose an original model-free technique for localization in mobile ad hoc sensor networks (MANETs). Region constraints are set by a comparison of the received signal strength indicators (RSSIs) at both anchors and nonanchor nodes. The accuracy of this method remains in the way that it overcomes the use of the channel pathloss model. It is thus naturally adapted to nonstationary environments. The proposed approach uses interval analysis and constraints satisfaction techniques to compute accurate locations in a guaranteed way. Simulations are performed on group trajectories of sensors whose movements are generated using a reference point group mobility model. The simulation results confirm the efficiency of the proposed method and show that it outperforms the anchor-based methods in terms of accuracy and estimation errors.

Guaranteed Boxed Localization in MANETs by Interval Analysis and Constraints Propagation Techniques

In this contribution, we propose an original algorithm for self-localization in mobile ad-hoc networks. The proposed technique, based on interval analysis, is suited to the limited computational and memory resources of mobile nodes. The incertitude about the estimated position of each node is propagated in an interval form. The propagation is based on a state space model and formulated by a constraints satisfaction problem. Observations errors as well as anchor nodes imperfections are taken into account in a simple and computational-consistent way. A simple Waltz algorithm is then applied in order to contract the solution, yielding a guaranteed and robust online estimation of the mobile node position. Simulation results on mobile node group trajectories corroborate the efficiency of the proposed technique and show that it compares favorably to particle filtering methods.

A Robust Localization Algorithm for Mobile Sensors Using Belief Functions

One of the main objectives of localization algorithms is to compute accurate estimates of sensor positions. This task is usually performed using measurements exchanged with neighboring sensors. However, when erroneous measurements occur, the localization process may yield wrong estimates, which leads to unreliable information for location-based applications. This paper proposes a robust localization technique that works efficiently, even under unreliable measurements assumptions. The proposed method uses belief function theory to estimate sensors locations. Assuming that the reliability of sensors measurements is known, the method combines all the available information to make a final decision about the positions. Each measurement is then used to define a belief function based on the reliability information. Experiments with simulated data demonstrate the effectiveness of this approach compared with state-of-the-art methods using different combination rules.

Robust interval-based localization algorithms for mobile sensor networks

This paper considers the localization problem in mobile sensor networks. Such a problem is a challenging task, especially when measurements exchanged between sensors may contain outliers, that is, data not matching the observation model. This paper proposes two algorithms robust to outliers. These algorithms perform a set-membership estimation, where only the maximal number of outliers is required to be known. Using these algorithms, estimates consist of sets of boxes whose union surely contains the correct location of the sensor, provided that the considered hypotheses are satisfied. This paper proposes as well a technique for evaluating the number of outliers to be robust to. In order to corroborate the efficiency of both algorithms, a comparison of their performances is performed in simulations using Matlab.

Model-Free Interval-Based Localization in Manets

Self-localization of sensor nodes has become a fundamental requirement for many sensor networks applications. In this paper, we propose an interval-based rings-overlapping technique using the comparison of the received signal strength indicators. The high performance of this method remains in the way that it avoids the estimation of the channel pathloss model. Compared to the guaranteed boxed localization based on connectivity measurements, the proposed method is robust under irregular radio propagation patterns. Simulation results corroborate the efficiency of this method in terms of accuracy and computation time.

Robust bounded-error tracking in wireless sensor networks

A wireless sensor network (WSN) consists of spatially distributed sensors connected via a wireless link. Sensors may be designed for pressure, temperature, sound, vibration, motion... This paper considers the problem of target tracking in a WSN. This problem is especially challenging in presence of measurements which are outliers. Two algorithms for target tracking robust to outliers are proposed. They only assume that the maximum number of outliers is known. Based on interval analysis, these algorithms perform a set-membership estimation using either SIVIA or a combinatorial technique. In both cases, sets of boxes guaranteed to contain the actual target location are provided.

Polar-Interval-Based Localization in Mobile Sensor Networks

The problem of localization in uncontrolled mobility sensor networks (MSN) is considered. Based on connectivity measurements the problem is solved using polar intervals. Computation is performed, in several polar coordinate systems (PCSs), using both polar coordinates and interval analysis. Position estimates are thus partial rings enclosing the exact solution of the problem. Simulation results corroborate the efficiency of the proposed method compared with existing methods, especially with those handling single coordinate systems.