Hichem Snoussi

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.

Target Tracking Using Machine Learning and Kalman Filter in Wireless Sensor Networks

This paper describes an original method for target tracking in wireless sensor networks. The proposed method combines machine learning with a Kalman filter to estimate instantaneous positions of a moving target. The targets accelerations, along with information from the network, are used to obtain an accurate estimation of its position. To this end, radio-fingerprints of received signal strength indicators (RSSIs) are first collected over the surveillance area. The obtained database is then used with machine learning algorithms to compute a model that estimates the position of the target using only RSSI information. This model leads to a first position estimate of the target under investigation. The kernel-based ridge regression and the vector-output regularized least squares are used in the learning process. The Kalman filter is used afterward to combine predictions of the targets positions based on acceleration information with the first estimates, leading to more accurate ones. The performance of the method is studied for different scenarios and a thorough comparison with well-known algorithms is also provided.

Distributed Regression in Sensor Networks with a Reduced-Order Kernel Model

Over the past few years, wireless sensor networks received tremendous attention for monitoring physical phenomena, such as the temperature field in a given region. Applying conventional kernel regression methods for functional learning such as support vector machines is inappropriate for sensor networks, since the order of the resulting model and its computational complexity scales badly with the number of available sensors, which tends to be large. In order to circumvent this drawback, we propose in this paper a reduced-order model approach. To this end, we take advantage of recent developments in sparse representation literature, and show the natural link between reducing the model order and the topology of the deployed sensors. To learn this model, we derive a gradient descent scheme and show its efficiency for wireless sensor networks. We illustrate the proposed approach through simulations involving the estimation of a spatial temperature distribution.

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.

Kernel-based localization using fingerprinting in wireless sensor networks

Indoor localization is an important issue in wireless sensor networks for a very large number of applications. Recently, localization techniques based on the received signal strength indicator (RSSI) have been widely used due to their simple and low cost implementation. In this paper, we propose an algorithm for localization in wireless sensor networks based on radio-location fingerprinting and kernel methods. The proposed method is compared to another well-known localization algorithm in the case of real data collected in an indoor environment where RSSI measures are affected by noise and other interferences.

Functional estimation in Hilbert space for distributed learning in wireless sensor networks

In this paper, we propose a distributed learning strategy in wireless sensor networks. Taking advantage of recent developments on kernel-based machine learning, we consider a new sparsification criterion for online learning. As opposed to previously derived criteria, it is based on the estimated error and is therefore is well suited for tracking the evolution of systems over time. We also derive a gradient descent algorithm, and we demonstrate its relevance to estimate the dynamic evolution of temperature in a given region.

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.