Top PDF Signal Strength Based Indoor and Outdoor Localization Scheme in Zigbee Sensor Networks

Signal Strength Based Indoor and Outdoor Localization Scheme in Zigbee Sensor Networks

Signal Strength Based Indoor and Outdoor Localization Scheme in Zigbee Sensor Networks

The indoor positioning experiments are conducted in the 6 th floor of building 7 at Yuan Ze University (YZU). Two different placements and the corresponding results are shown in Fig. 8(a) and 8(b). For comparing the actual position to the sensed position, we move the sensed node to the door of each room and observe what area it is placed in by scheme 1. The cluster of the sensed position determined by scheme 1 is marked by the color of the sensing node. However, for positions which have same receiving strength from two sensing nodes, they are given two colors. As shown in Fig. 8(a), we find that under this placement of the sensing nodes, some positions are placed into wrong area. The rooms in the bottom are close to either SN1 or SN3. However, they are detected in area 2 since SN2 senses the strongest receiving strength. This is because the center of the building is hollow, which lets the signal can directly pass through. Although it takes longer to walk from SN2 to these rooms, their line-of-sight distance is shorter. To fix this problem, we adjust the position of sensing nodes and conduct another position test but still using scheme 1, and the results are shown in Fig. 8(b). By moving the position of SN2, the rooms in the bottom are not classified to area 2 anymore, and the building can be perfectly clustered into four areas. The difference between Fig. 8(a) and 8(b) shows that the position of the sensing nodes is critical and may severely affect the positioning accuracy.
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Target Localization and Tracking in Wireless Sensor Networks

Target Localization and Tracking in Wireless Sensor Networks

Global Navigation Satellite System. A very popular way for determining target’s location nowadays is through a global navigation satellite system (GNSS). It can deliver to its user the latitude and longitude position in real-time [1]. GNSS utilizes satellites orbiting the Earth, which broadcast signals using very precise frequencies and highly-accurate atomic clocks for time measurements. Any receiver on the ground can pick up the GNSS signal as long they are codded to read its signal. As the GNSS signals travel through the Earth’s atmosphere, they can become distorted, leading to a reduced positional accuracy delivered to the receiver. Also, GNSS signals that are low on the horizon, i.e., the ones that have low zenith are more likely to deliver error because they are traveling through more atmosphere. GNSS uses groups of satellites, called constellations, for their systems, see Fig. 1.2. For a receiver to establish its location, it must be able to pick up a signal from at least four of the satellites [43]. Currently, there are two globally operational GNSSs: American GPS (constellation of 32 satellites, fully operational since 1995) and Russian GLONASS (constellation of 24 satellites, restored in 2011). Also, The European Union’s Galileo GNSS, as well as China’s BeiDou-2 GNSS are scheduled to be fully operational by 2020. These systems can be used for providing location, navigation or for tracking the location of a receiver. The signals also allow the electronic receiver to calculate the current local time to high precision, which allows time synchronization. Although technologies like telephonic or internet reception could be used to further enhance the localization performance of GNSSs, they usually operate independently of any of them. Also, even though these systems represent today a standard solution for outdoor localization, they have very limited or no functionality in harsh propagation environments, such as dense urban, underground, underwater and indoor to name a few [43].
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A Distributed Method to Localization for Mobile Sensor Networks based on the convex hull

A Distributed Method to Localization for Mobile Sensor Networks based on the convex hull

This paper deals with the problem of localization in wireless sensor networks when sensors are mobile. There are three scenarios of mobility: sensors and anchors are mobile; sensors are mobile and anchors are static; sensors are static and anchors are mobile. For the last case, some methods have been proposed [12], [13]. In these methods, mobile anchors can be robots, humains, or other, equipped GPS which are used in order to locate others static sensors. In this paper, we present a new method to resolve the localization problem in the complex scenario where nodes and anchors are mobile. However, this method can be used for the two others cases of mobility. Three schemes are proposed following the capabilities of sensors. Sensors can be equipped with techniques like ToA/TdoA (Time of arrival / Time difference of arrival) or RSSI (Received Signal Strength Indicator) allowing computing distance between a pair of neighbor sensors. They may also be equipped with AoA (Angle of arrival) technique allowing computing angle between a pair of neighbor sensors. Finally, sensors may be equipped by none of these techniques. Our method determines an exact position for a sensor when it has at least two anchors in its neighborhood. Otherwise, it gives an approximate position and can compute in this case the generated maximal error. The localization problem with mobile sensors introduces a new problem: in fact, the energy of sensors being weak, each node cannot compute continually its localization in order to maintain accuracy position during its move.
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Range Free Localization Schemes for Wireless Sensor Networks

Range Free Localization Schemes for Wireless Sensor Networks

117 obtained based on the Received Signal Strength Information (RSSI), which requires no additional hardware in sensor node, as it is available as part of physical layer of sensor node. In case of ANFIS trained Sugeno weighted centroid localization technique; fuzzy membership functions have been optimized using ANFIS trained Sugeno fuzzy inference system to calculate the weights of anchor nodes. Further, weighted centroid method has been used to obtain the localization information of sensor nodes. In case of Combined Sugeno-Mamdani fuzzy approach of localization, the edge weights of anchor nodes are calculated by averaging the edge weights obtained from Sugeno and Mamdani fuzzy approach. The accuracy of localization and extent of localization i.e. percentage of nodes localized in sensing field after completion of localization cycle depends upon number of location aware anchor nodes deployed in the sensing field. Reduction in number of anchor nodes lowers the accuracy of localization as well as extent of localization in the network. Since, each location aware anchor node is equipped with GPS system to ascertain its position; it escalates the cost of anchor node. Therefore, the overall cost of sensor network depends on the number of anchor nodes deployed in the sensing field. Cooperative localization scheme is proposed to considerably reduce the requirement of location aware anchor nodes, thus reducing the cost of sensor network deployment, without compromising the localization accuracy and extent of localization in WSN. Cooperative localization technique consists of cycles of localization, in each cycle, the nodes which are within radio range of three or more anchor nodes, localize themselves and once localized; they act as anchor node to localize remaining nodes during next cycle.
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3-D Hybrid Localization with RSSAoA in Wireless Sensor Networks: Centralized Approach

3-D Hybrid Localization with RSSAoA in Wireless Sensor Networks: Centralized Approach

The global positioning system (GPS) is the most accurate localization system used worldwide, but it is not very common to use it in every sensor node of a WSN. This is due to the fact that requires high precision, a complex process of timing and synchronization, and it cannot operate at indoor applications [1]. Instead of this system and in order to get low-cost solutions, it is common to implement systems using different types of measurements, such as received signal strength (RSS), time of arrival (AoA), etc. The accuracy of these systems are much worse than a GPS-based localization system. Recently, instead of using just one type of the referred measurements, hybrid systems that fuse more than one type of these measurements were presented [4, 5, 6, 7]. These systems increase significantly the estimation accuracy while keeping the computational complexity low.
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Distributed Algorithm for Target Localization in Wireless Sensor Networks Using RSS and AoA Measurements

Distributed Algorithm for Target Localization in Wireless Sensor Networks Using RSS and AoA Measurements

This paper addresses target localization problem in a cooperative 3-D wireless sensor network (WSN). We employ a hybrid system that fuses distance and angle measurements, extracted from the received signal strength (RSS) and angle-of- arrival (AoA) information, respectively. Based on range measurement model and simple geometry, we derive a novel non-convex estimator based on the least squares (LS) criterion. The derived non-convex estimator tightly approximates the maximum likelihood (ML) one for small noise levels. We show that the developed non-convex estimator is suitable for distributed implementation, and that it can be transformed into a convex one by applying a second-order cone programming (SOCP) relaxation technique. We also show that the developed non-convex estimator can be transformed into a generalized trust region sub- problem (GTRS) framework, by following the squared range (SR) approach. The proposed SOCP algorithm for known transmit powers is then generalized to the case where the transmit powers are different and not known. Furthermore, we provide a detailed analysis of the computational complexity of the proposed
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Distributed Algorithms for Target Localization in Wireless Sensor Networks Using Hybrid Measurements

Distributed Algorithms for Target Localization in Wireless Sensor Networks Using Hybrid Measurements

Abstract—This paper presents a performance analysis of two recently proposed distributed localization algorithms for cooperative 3-D wireless sensor networks (WSNs) in a more realistic scenario. The tested algorithms rely on distance and angle measurements obtained from received signal strength (RSS) and angle-of arrival (AoA) information, respectively. The mea- surements are then used to derive a convex estimator, based on second order cone programming (SOCP) relaxation techniques, and a non-convex one that can be formulated as a generalized trust region sub-problem (GTRS). Both estimators have shown excellent performance assuming a static network scenario, giving accurate location estimates in addition to converging in few iterations. Here, we test their performance considering differ- ent probabilities of communication failure between neighbour nodes at the broadcast phase. Our simulations show that their performance holds for high probability of communication failure and that convergence is still achieved in a reasonable number of iterations.
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Distributed Algorithms for Target Localization in Wireless Sensor Networks Using Hybrid Measurements

Distributed Algorithms for Target Localization in Wireless Sensor Networks Using Hybrid Measurements

Abstract—This paper presents a performance analysis of two recently proposed distributed localization algorithms for cooperative 3-D wireless sensor networks (WSNs) in a more realistic scenario. The tested algorithms rely on distance and angle measurements obtained from received signal strength (RSS) and angle-of arrival (AoA) information, respectively. The mea- surements are then used to derive a convex estimator, based on second order cone programming (SOCP) relaxation techniques, and a non-convex one that can be formulated as a generalized trust region sub-problem (GTRS). Both estimators have shown excellent performance assuming a static network scenario, giving accurate location estimates in addition to converging in few iterations. Here, we test their performance considering differ- ent probabilities of communication failure between neighbour nodes at the broadcast phase. Our simulations show that their performance holds for high probability of communication failure and that convergence is still achieved in a reasonable number of iterations.
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Lightweight Intrusion Detection Scheme for Wireless Sensor Networks

Lightweight Intrusion Detection Scheme for Wireless Sensor Networks

Abstract — Wireless sensor networks are increasingly used in a wide range of potential applications, including security and surveillance, control, actuation and maintenance of complex systems and fine-grain monitoring of indoor and outdoor environments. The nature of wireless sensor networks makes them very vulnerable to attack. The mobile nodes are randomly distributed, there are no physical obstacles for the adversary, therefore, they can be easily captured, and attacks can come from all directions and target any node. Consequently, security of wireless sensor networks (WSN) is the most challenging for this type of network. Intrusion Detection Systems (IDSs) can play an important role in detecting and preventing security attacks. In this paper, we propose a lightweight intrusion detection system for sensor networks. Our intrusion detection model exploits advantage of support vector machine (SVM) and signature model to detect malicious behaviors and provide a global lightweight IDS in cluster based topology. The proposed model can detect and prevent most of routing attacks.
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EFFICIENT AND ACCURATE INDOOR LOCALIZATION USING LANDMARK GRAPHS

EFFICIENT AND ACCURATE INDOOR LOCALIZATION USING LANDMARK GRAPHS

The advent of sensor-equipped smartphones has enabled a wide range of applications such as museum and shopping guides (Bih- ler et al., 2011; Shang et al., 2011), emergency response (Renau et al., 2007), personal task reminder (Lin & Hung, 2014), asset tracking (Boustani et al., 2011), search and rescue (Zorn et al., 2010), advertising (Dhar & Varshney, 2011; Dao et al., 2012), and location-enabled social networks (Cho et al., 2011). The fun- damental task of these applications is determining the location of mobile objects (e.g., a person or an asset). A number of solutions for indoor positioning have been proposed and developed. They differ from each other in terms of positioning techniques used, coverage, accuracy, cost of deployment and maintenance. WiFi-based indoor positioning is one of the most widely used indoor localization techniques since it can make use of existing WiFi infrastructure. However, it has the need to collect finger- prints, which is time-consuming and labor-intensive, or to know the exact locations of access points (APs), which is infeasible in some cases. Also, WiFi technology is recognized as an energy- hungry technology (Thiagarajan et al., 2011), resulting in a re- markable reduction in battery life. In order to deal with these problems, Dead Reckoning (DR) is often used, which can es- timate the current location in real time given an initial location. The popularity of smart devices equipped with inertial sensors en- ables DR to be widely used. It is especially useful for localization and tracking in the wireless signal denied areas. However, the ac- cumulated error of DR grows over time, resulting in the long-term DR practically being useless. Combining DR with other absolute positioning techniques such as WiFi (Jin et al., 2013) and UWB (De Angelis et al., 2010) can eliminate both the accumulated lo- cation error of DR and the jumping estimations by absolute po- sitioning techniques for a short time. However, these absolute localization techniques are not always available and often need to spend extra cost on the deployment and maintenance.
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Mobile platform-independent solutions for body sensor network interface

Mobile platform-independent solutions for body sensor network interface

In this approach, data flows in streams. Each stream has a specific data format. By default, the sink node captures and sends the streams with a frequency of 1 second. Since the first received data until the final value, it passes through several algorithms. The goal is to convert the received data, in bytes, to the inherent correct graphical presentation. This step has several algorithms linked together to reach the end result that is the correct point draw on the mobile device monitor. The first step is to strip the correct range of bytes from the stream received to post analysis. This step is essential to security improvement because the received stream have some associated flags. After that, and if the bytes number are correct, we must strip the correct value from the stream. After this, we have guarantees that we are working with the correct data and we can go to the next step. Now, the data conversion to an appropriate temperature value, in Celsius degrees, should be performs with another algorithm. All the transmitted data is organized in bytes, and then a transformation is needed. The algorithm must convert a 12-byte value, which corresponds to 4096 single values, in the equivalent temperature value. Afterwards, the temperature is striped and ready for display drawing. This step is very robust and has several related activities because all these activities are done in real time and they are used each time that new data is received. These activities will be reproduced on the display by informing the user of the real temperature, maximum and minimum values or even the data frequency.
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Identity-based Trusted Authentication in Wireless Sensor Networks

Identity-based Trusted Authentication in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) is network consisting of sensor nodes or motes communicating wirelessly with each other. Advancement in sensor, low power processor, and wireless communication technology has greatly contributed to the tremendous wide spread use of WSNs applications in contemporary living. Example of these applications include environmental monitoring, disaster handling, traffic control and various ubiquitous convergence applications and services[1]. Low cost and without the need of cabling are two key motivations towards future WSN applications. These applications however demand for considerations on security issues especially those regarding nodes authentications, data integrity and confidentiality. Commonly, the sensor nodes are left unattended, and are vulnerable to intruders. The situation becomes critical when the nodes are equipped with cryptographic materials such as keys and other
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Energy Efficient Routing Scheme for Sensor Networks Using Connected Dominating Set

Energy Efficient Routing Scheme for Sensor Networks Using Connected Dominating Set

The job of the sensor node is to periodically sense its nearby environment and tries to generate the data that has to be sent to the sink. Transmission occurs only once in the sensor in a round robin fashion and saves its energy. Then we have to wait for all the data coming from its child nodes to aggregate the data coming from them together with its own data and then the aggregated data is send to the parent node [14]. Whenever the power-aware functions are used while considering route maintenance, there is a possibility of changes to takes place in routing plan and route re- computation [15]. Next we broadcast a new ROUTE- DISCOVERY packet with a new sequence ID. In the node addition process new node broadcasts a HELLO message [3]. The neighboring node updates the local structure and message is being replied to its neighboring node which helps to determine its neighbors.
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Rev Bras Med Esporte  vol.12 número5

Rev Bras Med Esporte vol.12 número5

In the present sample, considering the number of sunburns, it w as observed that about one fourth of the athletes had suffered burns in the sports activities and approximately one third in lei- sure. The outdoor athletes had suffered more sunburn in training than the indoor ones, w hile in leisure they occurred in similar indi- ces. In the multivariate analysis, this supposed increased risk of the outdoor athletes for sunburns during training did not have sta- tistically significant value. It only presented a tendency; w e be- lieve may be significant in a larger sample. These data suggest that the outdoors athletes are more prone to sunburns during their professional activities. Such scenario does not seem to occur in leisure, in w hich the groups level, independently of the practiced sports modality. It suggests thus, that their behavior is similar con- cerning the sun.
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Mobility and Signal Strength- Aware Handover Decision in Mobile IPv6 based Wireless LAN

Mobility and Signal Strength- Aware Handover Decision in Mobile IPv6 based Wireless LAN

Generally, a mobility management system includes two essential elements: a handover-decision management phase and a handover execution phase. These two phases are supposed to be executed in a short time to allow the MN to be reached on any AP to which it wants to move [2]. Therefore, the IEIF has developed another mobility protocol extension to manage the handover process, which is called Fast Mobile IPv6 [3]. FMIPv6 is one of the IPv6 extensions, which tackles mobility management in wireless networks. Unfortunately, this protocol also has some weaknesses in the handover process [4]. FMIPv6 has two modes of operation to perform during the handover: the proactive and reactive modes [4]. In the proactive mode, the fast handover would be able to perform several signals (triggers) during the L2 (link-layer) handover prior to L3 between the current and potential router while it is still connecting with the current AP. Under this proactive mode, two variations occur: hard proactive (HP) and soft proactive (SP) [4], [5]. A MN can execute the SP mode in case there is one or more AP, which has a better signal from the current point of attachment. However, usually, a MN cannot know what the potential handover will be during its roaming period without having a good quality intelligence system. There are some parameters that can provide a timely and correct handover decision while the MN is still connected to the current AP. The horizontal handover process (within this paper’s scope) starts when the link quality parameters decrease, for example, the Received Signal Strength Indicator (RSSI), Signal-to-noise ratio (SNR), Data rate ratio, and so on. Based on the range of these variables, a MN will build its own handover decision, which identifies when an AP must be activated. For this handover decision to take place, a MN normally depends on one parameter, which is generated
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Estimating mortality derived from indoor exposure to particles of outdoor origin.

Estimating mortality derived from indoor exposure to particles of outdoor origin.

This method for estimating mortality derived from indoor exposure to particles of outdoor origin has some disadvantages (e.g., the composition issue). Wilson et al. [28] suggested a rea- sonable, epidemiological approach to estimate such mortality. For the purpose of controlling aerosol particle pollution, we are eager to learn from the epidemiological data, e.g. mortality de- rived from indoor exposure to particles of outdoor origin, from the method suggested by Wil- son et al. [28] in order to support strategies for managing indoor air quality. However, to date, no such studies have been presented. Therefore, through this paper, we also wish to encourage epidemiologists to further consider this important issue.
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Analysis of Gait Pattern to Recognize the Human Activities

Analysis of Gait Pattern to Recognize the Human Activities

We employed a model based approach to extract the features. The extracted foreground that supposed to be a human is segmented into centroid and two leg components. We use Mean-shift algorithm again for computing the similar regions below the centroid of the human body for each leg components that will serve for tracking legs. We assume that with only these three components of human model the four basic actions could be identified correctly. The human model constraints are used for noise suppression. The three components namely centroid, left leg and right leg (i.e. vm1, vm2, vm3 respectively), are used in order to model parametric approach. The threshold concept is also used along with the defined method. Threshold calculation is applied as follows: Video sequences from the KTH and Weizmann datasets are normalized on the basis of number of frames and the time of a particular sequence for an activity. The threshold is calculated on the basis of a case study given in [29]. To recognize the actions, we establish the features of each action from the parameters of human model as follows: Walking feature: In case of walking action, every part of human move generally and approximately in the same direction and speed. Therefore, the walking activity can then be identified by the velocities of all components superior to zero but lesser than a predefined threshold for walking. Note that the significant difference between running and walking strides is that at least one of the feet will be in contact with the principal axis (ground) at any given time as shown in Figure 2 (a). Jumping feature: In case of jumping activity, every part of human moves only vertically and in the same direction either up or down [30-39]. Therefore, jumping action can be identified by the velocities of all the three components to be near or equal to zero in horizontal direction but greater than zero in vertical direction as shown in Figure 2(b). Jogging feature: The only differences between jogging and running activities were that travelling speed of running is greater than jogging and other difference is of distance ratio between the leg components to the axis of ground as shown in Figure 2(c). Running feature: Similarly in case of running activity, speed of travelling is greater than jogging and the other difference is of distance ratio between leg components to the axis of ground as shown in Figure 2 (d).
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Autonomous navigation with simultaneous localization and mapping in/outdoor

Autonomous navigation with simultaneous localization and mapping in/outdoor

With the technological advances of the last few decades, there has been a push to make intelli- gent wheelchairs as a viable product to the people who might need them. The project IntellWheels is a platform for developing new IWs, which makes use of modular architectures that facilitate the integration of new methods and hardware. One of the most significant characteristics of an IW is the capability of providing the user with autonomous behaviors. There have been various naviga- tion modes applied to intelligent wheelchairs, from semi-autonomous to completely autonomous ones. Notwithstanding, to make use of autonomous navigation with path planning, most intel- ligent wheelchairs systems require some restrictions, like knowing the environment it is moving beforehand. IntellWheels makes use of visual cameras to apply methods studied in probabilistic robotics, allowing the platform to navigate autonomously in a known environment.
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Whole blood donor deferral analysis at Genera l hospital blood bank – A retrospective study.

Whole blood donor deferral analysis at Genera l hospital blood bank – A retrospective study.

Blood Transfusion Services (BTS) is the vital part of modern health care system without which efficient medical care is impossible. The aim of Blood Transfusion Services should be to provide effective blood and blood products, which are as safe as possible and adequate to meet patients’ need 1 . Many measures have been taken to make the blood transfusion safe by blood transfusion committee. The most important is the selection of blood donors by donor selection criteria 2 . A large number of blood donors are not able to donate blood successfully for several reasons, either temporarily or permanently. Individuals disqualified from donating blood are known as deferred donors. Blood donor deferral is a painful and sad experience for the blood donor as well as the blood centre screening the donor. Deferral leads to loss of precious whole blood donors (WBD) and blood units available for transfusion purposes. Additionally these donors are less likely to return for blood donation in future 3 . Knowledge of rate and causes of donor deferral can guide the recruitment strategy for WBD 3,4 . The few studies done in India in the past have provided different common reasons for deferral of whole blood donors highlighting differing demographic profile in different parts of the country 5 . The present study was undertaken to analyse the deferral incidence and pattern among blood donors in blood bank, Ahmedabad with an objective to review the centre’s policy for recruitment and retention of blood donors.
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A bounded heuristic for collection-based routing in wireless sensor networks

A bounded heuristic for collection-based routing in wireless sensor networks

NOELIA CORREIA received the B.Sc. and M.Sc. degrees in computer science and Ph.D. degree in optical networks (computer science) from the Uni- versity of Algarve, Faro, Portugal, in 1995 and 1998, respectively, and the Ph.D. degree in optical networks (computer science) from the University of Algarve, in collaboration with University Col- lege London, London, U.K., in 2005. She is cur- rently a Lecturer with the Faculty of Science and Technology, University of Algarve. Her research interests include the application of optimization techniques to several net- work design problems, in the optical, wireless, and sensor networks fields, and development of algorithms. She is a founding member of the Cen- ter for Electronics, Optoelectronics and Telecommunications, University of Algarve, a research center supported by the Portuguese Foundation for Science and Technology. She is also the Networks and Systems Group Coordinator.
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