A Review on Sensor Network Issues and Robotics <s> Quick Response Code. <s> Mobile robots must be able to learn, use and maintain models of their environments. Two fundamental and interrelated problems in mobile robotics are mapping and localization. We present here a new type of image-based map formed by panoramic models enriched with depth and 3D planarity information. We take an advantage of the scene geometry implicitly contained in the model to localize a mobile robot that is moving in the same environment. The novelty of this model compared to the existing image-based maps is that the motion of the robot is not restricted to a predefined path or locations close to the original images. Experimental results demonstrate this approach. <s> BIB001 </s> A Review on Sensor Network Issues and Robotics <s> Quick Response Code. <s> We consider the problem of node positioning in ad-hoc networks. We propose a distributed, infrastructure-free positioning algorithm that does not rely on Global Positioning System (GPS). The algorithm uses the distances between the nodes to build a relative coordinate system in which the node positions are computed in two dimensions. The main contribution of this work is to define and compute relative positions of the nodes in an ad-hoc network without using GPS. We further explain how the proposed approach can be applied to wide area ad-hoc networks. <s> BIB002 </s> A Review on Sensor Network Issues and Robotics <s> Quick Response Code. <s> Mobile robots need an internal representation of their environment to do useful things. Usually such a representation is some sort of geometric model. For our robot, which is equipped with a panoramic vision system, we choose an appearance model in which the sensoric data (in our case the panoramic images) have to be modeled as a function of the robot position. Because images are very high-dimensional vectors, a feature extraction is needed before the modeling step. Very often a linear dimension reduction is used where the projection matrix is obtained from a Principal Component Analysis (PCA). PCA is optimal for the reconstruction of the data, but not necessarily the best linear projection for the localization task. We derived a method which extracts linear features optimal with respect to a risk measure reflecting the localization performance. We tested the method on a real navigation problem and compared it with an approach where PCA features were used. <s> BIB003 </s> A Review on Sensor Network Issues and Robotics <s> Quick Response Code. <s> The ability of a sensor node to determine its physical location within a network (Localization) is of fundamental importance in sensor networks. Interpretating data from sensors will not be possible unless the context of the data is known; this is most often accomplished by tracking its physical location. Existing research has focused on localization in static sensor networks where localization is a one-time (or low frequency) activity. In contrast, this paper considers localization for mobile sensors: when sensors are mobile, localization must be invoked periodically to enable the sensors to track their location. The higher the frequency of localization, the lower the error introduced because of mobility. However, localization is a costly operation since it involves both communication and computation. In this paper, we propose and investigate adaptive and predictive protocols that control the frequency of localization based on sensor mobility behavior to reduce the energy requirements for localization while bounding the localization error. We show that such protocols can significantly reduce the localization energy without sacri ficing accuracy (in fact, improving accuracy for most situations). Using simulation and analysis we explore the tradeoff between energy efficiency and localization error due to mobility for several protocols. <s> BIB004 </s> A Review on Sensor Network Issues and Robotics <s> Quick Response Code. <s> Location Awareness is key capability of Context-Aware Ubiquitous environments. Received Signal Strength (RSS) based localization is increasingly popular choice especially for indoor scenarios after pervasive adoption of IEEE 802.11 Wireless LAN. Fundamental requirement of such localization systems is to estimate location from RSS at a particular location. Multi-path propagation effects make RSS to fluctuate in unpredictable manner, introducing uncertainty in location estimation. Moreover, in real life situations RSS values are not available at some locations all the time making the problem more difficult. We employ Modular Multi-Layer Perceptron (MMLP) approach to effectively reduce the uncertainty in location estimation system. It provides better location estimation results than other approaches and systematically caters for unavailable signals at estimation time. <s> BIB005 </s> A Review on Sensor Network Issues and Robotics <s> Quick Response Code. <s> Localization is an important issue for Wireless Sensor Networks in a wireless sensor network. A mobile sensor may change its position rapidly and thus require localization calls frequently. It is important to control the number of localization calls, as it is rather expensive. The existing schemes for reducing the frequency of localization calls for mobile sensors uses the technique of extrapolation which involves simple arithmetic calculations. We propose a technique to control the localization that gives much better result. The proposed method involves very low arithmetic computation overheads. We find analytical expressions for the estimated error if the rate of localizations is specified. Simulation studies are carried out to compare the performances of the proposed method with the methods proposed by Tilak et al. <s> BIB006 </s> A Review on Sensor Network Issues and Robotics <s> Quick Response Code. <s> Mobile wireless sensors need to know their localizations in many control and monitoring applications. Among all sensors, some know their exact position (i.e., they are equipped with GPS or they are positioned by human intervention). These sensors are called anchors. Some sensors can have different capabilities allowing them to calculate either distances or angles when they receive messages from others nodes. So, they only use anchor positions to obtain an estimated position. However, when sensors are mobile they cannot continuously calculate their position because of the energy constraints. This paper concerns the localization problem in the case where all nodes in the network (anchors and others sensors) are mobile. The authors propose three techniques following the capabilities of nodes. Thus, each node obtains either an exact position or an approximate position with the knowledge of the maximal error born. Also, the authors adapt the periods where nodes invoke their localization. Simulation results show the performances of our methods in term of accuracy and determinate the technique the more adapted related to the network configurations. <s> BIB007 </s> A Review on Sensor Network Issues and Robotics <s> Quick Response Code. <s> This brief paper presents a novel localization algorithm, named discriminant-adaptive neural network (DANN), which takes the received signal strength (RSS) from the access points (APs) as inputs to infer the client position in the wireless local area network (LAN) environment. We extract the useful information into discriminative components (DCs) for network learning. The nonlinear relationship between RSS and the position is then accurately constructed by incrementally inserting the DCs and recursively updating the weightings in the network until no further improvement is required. Our localization system is developed in a real-world wireless LAN WLAN environment, where the realistic RSS measurement is collected. We implement the traditional approaches on the same test bed, including weighted k -nearest neighbor (WKNN), maximum likelihood (ML), and multilayer perceptron (MLP), and compare the results. The experimental results indicate that the proposed algorithm is much higher in accuracy compared with other examined techniques. The improvement can be attributed to that only the useful information is efficiently extracted for positioning while the redundant information is regarded as noise and discarded. Finally, the analysis shows that our network intelligently accomplishes learning while the inserted DCs provide sufficient information. <s> BIB008 </s> A Review on Sensor Network Issues and Robotics <s> Quick Response Code. <s> For the various applications in home automation, the service system requires to precisely estimate user’s locations by certain sensors. It is considered as a challenge to automatically serve a mobile user in the house. However, indoor localization cannot be carried out effectively by the well-know Global Positioning System (GPS). In recent years, Wireless Sensor Networks (WSNs) are thus popularly used to locate a mobile object in an indoor environment. Some physical features are widely discussed to solve indoor localization in WSN. In this paper, we inquired about the RSSI solutions on indoor localization, and proposed a Closer Tracking Algorithm (CTA) to locate a mobile user in the house. The proposed CTA was implemented by using ZigBee CC2431 modules. The experimental results show that the proposed CTA can accurately determine the position with error distance less than 1 meter. At the same time, the proposed CTA has at least 85% precision when the distance is less than one meter. <s> BIB009 </s> A Review on Sensor Network Issues and Robotics <s> Quick Response Code. <s> Reliable localization techniques applicable to indoor environments are essential for the development of advanced location aware applications. We rely on WLAN infrastructure and exploit location related information, such as the Received Signal Strength (RSS) measurements, to estimate the unknown terminal location. We adopt Artificial Neural Networks (ANN) as a function approximation approach to map vectors of RSS samples, known as location fingerprints, to coordinates on the plane. We present an efficient algorithm based on Radial Basis Function (RBF) networks and describe a data clustering method to reduce the network size. The proposed algorithm is practical and scalable, while the experimental results indicate that it outperforms existing techniques in terms of the positioning error. <s> BIB010 </s> A Review on Sensor Network Issues and Robotics <s> Quick Response Code. <s> This paper proposes a new location algorithm based on MCB(Monte-Carlo Localization Boxed),that is,the Weighted-MCB.The MCB algorithm adopts the first-hop anchors and second-hop anchors in prediction step and filtering step,and uses no location information of neighboring nodes.In addition to the first-hop anchors and second-hop anchors,the samples(estimated locations) in the sample set of the first-hop nodes are also exploited in the Weighted-MCB,and thus the Weighted-MCB improves the localization accuracy by 13%～18% as compared with MCB. <s> BIB011 </s> A Review on Sensor Network Issues and Robotics <s> Quick Response Code. <s> The relationship between RSSI (Received Signal Strength Indication) values and distance is the foundation and the key of ranging and positioning technologies in wireless sensor networks. Log-normal shadowing model (LNSM), as a more general signal propagation model, can better describe the relationship between the RSSI value and distance, but the parameter of variance in LNSM is depended on experiences without self-adaptability. In this paper, it is found that the variance of RSSI value changes along with distance regu- larly by analyzing a large number of experimental data. Based on the result of analysis, we proposed the relationship function of the variance of RSSI and distance, and established the log-normal shadowing model with dynamic variance (LNSM-DV). At the same time, the method of least squares(LS) was selected to es- timate the coefficients in that model, thus LNSM-DV might be adjusted dynamically according to the change of environment and be self-adaptable. The experimental results show that LNSM-DV can further reduce er- ror, and have strong self-adaptability to various environments compared with the LNSM. <s> BIB012 </s> A Review on Sensor Network Issues and Robotics <s> Quick Response Code. <s> Fingerprinting techniques have been proved as an effective techniques for determining the position of a mobile user in an indoor environment and in challenging environments such as mines, canyons, and tunnels where common localization techniques based on time of arrival (TOA) or received signal strength (RSS) are subject to big positioning errors. In this paper, a fingerprinting based localization technique using neural networks and ultra-wideband signals (UWB) is presented as an alternative. The fingerprinting database is built with signatures extracted from channel impulse responses (CIR) obtained by processing an IR-UWB indoor propagation measurement campaign. The construction of the neural networks and the adopted approach are described. Positioning performances are evaluated with different selected signatures and different sizes of the fingerprinting database. <s> BIB013 </s> A Review on Sensor Network Issues and Robotics <s> Quick Response Code. <s> In this paper, we propose a new localization technique in a wireless sensor network (wsn) using the Received Signal Strength Indicator (RSSI). This technique is based on a decision tree obtained from a set of empirical experiments on wireless sensors based on XBee modules. The decision tree is used to select the best three neighbour reference nodes that are involved into the estimation of the target sensor node position by applying the Cramer's Rule approach. The tests performed during this study indicate an estimated accuracy of the position around two meters after verifying the validity of our results in an actual environment. This result is acceptable, if we take into consideration the restricted number of nodes used and the application for which the technique will be deployed, this might be the localisation of personnel in high-risk areas in order to enhance their safety in the event of an occurring danger. Streszczenie. W artykule zaproponowano nową metode lokalizacji w sieci bezprzewodowej bazująca na wskaźniku mocy odbieranego sygnalu. Metoda zostala zbadana na przykladzie sieci z modulami XBee. Bazując na drzewie wezlow sieci mozna określac polozenie wezla czujnika stosując metode prawa Cramera. (Lokalizacja wezlow w sieci bezprzewodowej bazując na wskaźniku mocy odbieranego sygnalu RSSI) <s> BIB014 </s> A Review on Sensor Network Issues and Robotics <s> Quick Response Code. <s> Various techniques that employ Global Positioning System(GPS) signals such as A-GPS and GPS transmitters, have been introduced with the hope to provide a solution for indoor positioning detection. Indoor positioning system (IPS) is a term that is used for network devices used to wirelessly locate objects or people inside building. The study is based on the issue in order to determine the position of an object in indoor environment or inside a building. The problems arise when the position of an object inside a building cannot be determined using GPS. We proposed the implementation of trilateration technique to determine the position of users in indoor areas based on Wi-Fi signal strengths from access points (AP) within the indoor vicinity. In this paper, percentage of signal strengths obtained from Wi-Fi analyzer in a smartphone were converted into distance between users and each AP. A user’s indoor position could then be determined using a formula proposed based on trilateration technique. <s> BIB015 </s> A Review on Sensor Network Issues and Robotics <s> Quick Response Code. <s> Collecting the data and forwarding it to its destination is an important function of a sensor network. For many applications, it is also very important to include the location information to the collected data. Localization techniques in wireless sensor networks (WSNs), which is a way of determining the sensor node locations can be utilized for obtaining such information. In this paper, we present an artificial feed-forward neural network based approach for node localization using the RSSI values of the anchor node beacons. The method was applied in a 5 x 4 m indoor environment with beacons installed at each of the corners. Five different training algorithms have been evaluated to obtain the training algorithm that gives the best results for this scenario. The multi-layer Perceptron (MLP) neural network has been obtained using the Matlab software and implemented using the Arduino programming language on the mobile node to evaluate its performance in real time environment. An average 2D localization error of 30 cm is obtained using a 12-12-2 feed-forward neural network structure. The method proposed can be used to implement a trained neural network using other programming languages on other platforms. <s> BIB016
It is a matrix code that keeps a comparative huge amount of location information compared to standard barcode. It can be attached to some key areas of the buildings to wait for scanning to provide its positional information from database  . In this section we present a brief overview of the localization methods used by some researchers. In localization using RSSI, based on Xbee modules in wireless sensor networks, BIB014 proposed a localization technique using RSSI, and the technique is based on decision tree obtained from a set of empirical experiments. By applying the Cramer's rule approach they used the decision tree to select the best three Journal of Sensors 7 neighbor reference nodes that are involved in the estimation of the position of target sensor node. The results obtained based on empirical data and gathered from the experiments indicate accuracy less than 2 meters. By using Zigbee CC2431 modules BIB009 proposed closer tracking algorithm for indoor wireless sensor localization. The proposed algorithm can suitably select an adaptive mode to obtain precise locations. They also improved the fingerprinting algorithm in mean time. The proposed technique CTA can determine the position with error less than 1 meter. Based on artificial neural networks BIB005 presented location estimation system in the indoor environment. This architecture provides robust mechanism for coping with unavailable information in real life situations as they employ modular multilayer perceptron (MMLP) approach to effectively reduce the uncertainty in the location estimation system. Moreover their system does not require runtime searching of nearest neighbors in huge backend database. Yu proposed a measurement and simulation based work of a fingerprinting technique based on neural networks and ultrawideband signals. Their proposed technique is based on the construction of a fingerprinting database of LDPs extracted from an UWB measurement campaign. To learn the database and to locate the targeted positions the feed forward neural network with incremental back backpropagation is used. In order to evaluate the positioning performance, different types of fingerprinting database and different sizes are considered BIB013 . To perform indoor localization, BIB010 evaluated several ANN designs by exploiting RSS fingerprints collected in an office environment. They relied on WLAN infrastructure to minimize the deployment cost. The proposed cRBF algorithm can be a good solution to the location estimation problem in indoor environments. Moreover based on their experimental results, it is found that the proposed algorithm achieves more accuracy compared to sRBF, MLP and GRNN designs, and KNN algorithm. Reference  used RSS fingerprinting technique and artificial neural network for mobile station location in the indoor environment. The proposed system learns offline the location "signatures" from extracted location-dependent features of the measured data for LOS and NLOS situations and then it matches online the observation received from a mobile station against the learned set of "signatures" to accurately determine its position. It was found that location precision of the proposed system is 0.5 meters for 90% of trained data and 5 meters for 45% of untrained data. By using RSSI values of anchor node beacons, researchers presented an artificial feed forward neural network based approach for node localization in sensor networks. They evaluated five different training algorithms to obtain the algorithm that gives best results. The multilayer perceptron (MLP) neural network has been obtained using the Matlab software and implemented using the Arduino programming language on the mobile node to evaluate its performance in real time environment. By using 12-12-2 feed forward neural network structure, an average error of 30 cm is obtained BIB016 . To infer the clients position in the wireless local area network (LAN), researchers presented a novel localization algorithm, namely discriminant adaptive neural network (DANN), which takes, received signal strength (RSS) from access points (APs) as an input. For network learning, they extracted the useful information into discriminative components (DCs). This approach incrementally inserts DCs and recursively updates the weightings in the network until no further improvement is required. Traditional approaches were implemented on the same test bed, including weighted-nearest neighbor (WKNN), maximum likelihood (ML), and multilayer perceptron (MLP), and then results were compared. The results showed that the proposed technique has better results as compared to other examined techniques BIB008 . Ali used neural networks to solve the problem of localization in sensor networks. They compared the performances of three different families of neural networks: multilayer perceptron (MLP), radial basis function (RBF), and recurrent neural networks (RNN). They compared these networks with two variants of Kalman filter which are also used for localization. Resource requirements in terms of computational and memory resources were also compared. The experimental results in BIB001 show that RBF neural network has the best performance in terms of accuracy and MLP neural networks has best computational and memory resource requirement. Another neural network based approach was used by Laslo. For the processing, received signal strength indicator (RSSI) was used and its also used for learning of neural network and preprocessed (mean, median and standard deviation) in order to increase the accuracy of the system. Fingerprint (FP) localization methodology was also applied in the indoor experimental environment which is also presented. The RSSI values used for the learning of the neural network are preprocessed (mean, median, and standard deviation) in order to increase the accuracy of the system. To determine the accuracy of the neural network, mean square error of Euclidean distance between calculated and real coordinates and the histogram was used in BIB003 . Wi-Fi based technique was proposed for detecting users position in an indoor environment. They implemented the trilateration technique for localization. They used mobile phone to obtain RSSI from the access points and then the RSSI data was converted into distance between users and each AP. They also proposed to determine the users position based on trilateration technique BIB015 : where is the percentage of signal strength, is the maximum coverage of signal strength, and = 1, 2, 3. By analyzing a large number of experimental data, it is found that the variance of RSSI value changes along with the distance regularly. They proposed the relationship function of the variance of RSSI and distance and establish the lognormal shadowing model with dynamic variance LNSM-DV based on the result analysis. Results show that LNSM-DV can further reduce error and have strong self-adaptability to various environments compared with LNSM BIB012 . In this study the problem of indoor localization using wireless Ethernet IEEE 802.11 (Wireless Fidelity, Wi-Fi) was analyzed. The main purpose of this work was to examine several aspects of location fingerprinting based on indoor localization that affects positioning accuracy. The results showed that they achieved the accuracy of 2-2.5 meters  . In the mobile 8 Journal of Sensors node localization, a system was proposed for real environment when both anchors and unknown nodes are moving. To figure out the current position, history of anchor information was used. User's movement was modeled by the archived information and for discovering new positions movement models were also used. In complex situations where anchors and nodes are mobile, researchers presented three methods to resolve localization problem BIB007 . The proposed methods take into account the capability of nodes: nodes which can calculate either distances or angles with their neighbors or none of both. When the sensor has at least two anchors in the neighborhood, the proposed methods determine the exact position; else it gives a fairly accurate position and in this case it can compute the generated maximal error. The proposed method also defines periods when a node has to invoke its localization. A GPS free localization algorithm was in MWSNs BIB002 . In order to build the coordinate system, the proposed algorithm uses the distance between the nodes and also nodes positions are computed in two dimensions. Based on dead reckoning, Tilak et al. BIB004 proposed a number of techniques for tracking mobile sensors. Among all the proposed techniques, mobility aware dead reckoning estimates the position of the sensor, instead of localizing the sensor every time it moves. Error in the estimated position is calculated every time the localization is called and with the time error in the estimation grows. And also the next localization time is fixed depending on the value of this error. For a given level of accuracy in position estimation fast mobile sensors trigger localization with higher frequency. Instead of localizing sensor, a technique was proposed to estimate the positions of a mobile sensor BIB006 . It gives higher accuracy for particular energy cost and vice versa. When sensor has some data to be sent, the position of the sensor is required then only. The information of an inactive sensor is ceased to be communicated. In order to reduce the arithmetic complexity of sensors, most calculations are carried out at base station. To solve the set of equations, BIB011 proposed the novel algorithm for MSN localization in which they took three nodes which are neighbors to each other and if the solutions are unique they are the node positions. And, for searching the position of the final node, a scan algorithm was also introduced called a metric average localization error (ALE) (which is the root mean square error of node locations divided by the total number of nodes) to evaluate the localization error: