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Nowadays, research and development of various indoor positioning systems (IPS) have been increasing owing to flourishing social and commercial interest in location-based services (LBSs). Among LBS technologies, we used the Bluetooth low energy beacon in our system, which consumes less energy and is embedded in many current smartphones and tablets. In particular, the fingerprinting method has become a prime choice in the design of IPS owing to its good location estimation and the fact that a line-of-sight from access points is not required. We propose an improved two-step fingerprinting localization using multiple fingerprint features to enhance the localization accuracy. The proposed system uses a propagation model to convert RSS of beacons to distance and estimate the weighted centroid (WC) of nearby beacons. The estimated WCs along with signal strength and rank of the nearby beacons are stored in the server database for localization instead of RSS from all the deployed beacons. First, the proposed system makes use of diverse fingerprinting features to increase localization accuracy that also reduces both the physical size of the database and the amount of data communication with the server in the execution phase; second, affinity propagation clustering minimizes the searching space of RPs and reduces the computational cost; third, exponential averaging is introduced to smooth the noisy RSS. The experimental results obtained by real field deployment show that the proposed method significantly improves the performance of the positioning system in both the positioning accuracy and radio-map database size.

The time of arrival (TOA) trilateration is one of the representative location detection technologies (LDT) that determines the true location of a mobile station (MS) using a unique intersection point of three circles based on three radii corresponding to distances between MS and base stations (BSs) and center coordinates of BSs. Since the distance between MS and BS is estimated by using the number of time delays, three circles based on the estimated radii are generally increased and they may not meet at a single point, resulting in the location estimation error. In order to compensate this estimation error and to improve estimation performance, we present two advanced TOA trilateration localization algorithms with detail mathematical expressions. The considered algorithms are the shortest distance algorithm, which calculates an average of three interior intersection points among an entire six intersection points from three intersecting circles, and the line intersection algorithm, which calculates an intersection point of three lines connecting two intersection points of two circles among the three circles, as the estimated location of the MS. In this paper, we present both algorithms with detailed mathematical expressions. The computer simulation results are provided to compare the location estimation performance of both algorithms. In addition, in this paper, mathematical analysis is provided to indicate the relation between the line intersection algorithm and the shortest distance algorithm. In this analysis, we verify that line equations based on the intersection points obtained from the shortest distance algorithm are identical to those obtained from the line intersection algorithm.

Owing to the escalating social and commercial interest on location-based services (LBS), research and development of indoor positioning systems (IPS) have been extended. Since the global navigation satellite system (GNSS) is not available at indoors, IPS research has been focused with alternative wireless technologies. Among them, Wi-Fi and Bluetooth Low Energy (BLE) have been widely used. In this paper, we discuss the RSS features of Wi-Fi and BLE measured under uniform conditions. Then, we show the typical weighted k-nearest neighbor (Wk-NN) fingerprinting positioning using Wi-Fi and BLE beacon individually for the IPS application. In addition, we propose a hybrid Wk-NN fingerprinting employing both BLE and Wi-Fi signals. The experiments were performed through real field deployments. The obtained results show that distance estimation and fingerprinting positioning accuracy using BLE is moderately better than Wi-Fi under identical AP deployment and signal attenuation condition. In addition, the proposed hybrid method shows better location accuracy compared to the BLE beacon and Wi-Fi when used individually.

The Angle-of-Arrival (AOA) has a variety of applications in civilian and military wireless communication fields. Due to the rapid development of the location-based service (LBS) industry, the importance of the AOA estimation technique has increased. Although a large antenna array is necessary to estimate accurate AOA information of many signals, the computational complexity of conventional AOA estimation algorithms, such as Multiple Signal Classification (MUSIC), is dramatically increased. In this paper, we propose a cascade AOA estimation algorithm employing CAPON and Beamspace MUSIC, based on a flexible (on/off) antenna array. First, this approach roughly finds AOA groups, including several signal AOAs using CAPON, by applying some of the antenna elements. Then, it estimates each signal AOA in the estimated AOA groups using Beamspace MUSIC by applying the full size of the antenna array. In addition to extremely low computational complexity, the proposed algorithm also has similar estimation performance to that of MUSIC. In particular, the proposed cascade AOA estimation algorithm is highly efficient when employing a massive antenna array. Representative computer simulation examples are provided to illustrate the AOA estimation performance of the proposed technique.

With the rise of new radio technologies, resource sharing mechanism has become a promising technique to achieve better system performance. Recently, the Fractional frequency reuse (FFR) method has gained more popularity to mitigate intra-cell interference generated by device-to-device (D2D) multicast communications into the traditional cellular networks. In this paper, we propose a weighted bipartite matching mechanism to accommodate the user’s satisfaction. Then, we formulate an optimization problem to maximize system throughput and satisfaction rate while guaranteeing the quality of service (QoS) of both cellular users and D2D multicast users. Simulation results show that the optimal system throughput is obtained by the proposed scheme and D2D multicast communication shows its effectiveness in maximizing overall system throughput, resource allocation satisfaction rate and spectrum efficiency.

The ever-growing mobile station (MS) localization technologies provide an increasingly important role in all aspects of the wireless cellular systems and Internet of Things (IoT). The accurate MS location information is the basis in connection of different devices in IoT. The MS localization techniques based on time of arrival (TOA) trilateration algorithm, which determines the location of MS using an intersection point of three circles based on distances between MS and base stations (BS) and coordinates of BSs, have been actively studied. In general, the distance between the MS and BS is calculated by counting the number of delay samples or measuring the power of the received signal. Since the estimated distance (radius of a circle) between MS and BS is commonly increased, three circles may not meet at a single point, resulting in the estimation error of MS localization. In order to improve this problem, in this paper, we propose the hybrid TOA trilateration algorithm based on the line intersection algorithm for the general case for intersection of three circles and the comparison approach of intersection distances for the specific case where a small circle is located inside the area of two large circles. The line intersection algorithm has an excellent location estimation performance in the general case, but it does not work in the specific case. The comparison approach of intersection distances has good performance only for the specific case. In addition, we propose the mode selection algorithm to efficiently select a proper mode between the general and specific cases. The representative computer simulation examples are provided to verify the localization performance of the proposed algorithm.

This paper proposes a method of estimating the attitude of an underwater vehicle. The proposed method uses two field measurements, namely, a gravitational field and a magnetic field represented in terms of vectors in three-dimensional space. In many existing methods that convert the measured field vectors into Euler angles, the yaw accuracy is affected by the uncertainty of the gravitational measurement and by the uncertainty of the magnetic field measurement. Additionally, previous methods have used the magnetic field measurement under the assumption that the magnetic field has only a horizontal component. The proposed method utilizes all field measurement components as they are, without converting them into Euler angles. The bias in the measured magnetic field vector is estimated and compensated to take full advantage of all measured field vector components. Because the proposed method deals with the measured field independently, uncertainties in the measured vectors affect the attitude estimation separately without adding up. The proposed method was tested by conducting navigation experiments with an unmanned underwater vehicle inside test tanks. The results were compared with those obtained by other methods, wherein the Euler angles converted from the measured field vectors were used as measurements.

In this paper, we propose a novel dynamical love model of Romeo and Juliet, which has an external force with a fuzzy membership function. The external force used in the model has the characteristics of a Gaussian function. The chaotic behavior in the model is demonstrated using time series and phase portraits.

The Global Positioning System (GPS) is designed to accurately estimate the location information of objects, using signals from at least four GPS satellites. When the high-power interference signals exist, the GPS receiver does not have the proper estimation performance. In this case, we need to enhance the desired GPS signals and to efficiently suppress interference signals. For this purpose, the adaptive beamformer based on the angle-of-arrival (AOA) is widely employed, but the AOA information of the GPS signal is not easily estimated. After conventional despreading, GPS AOA can be estimated using various AOA estimation algorithms such as Multiple Signal Classification (MUSIC), but the estimated result includes not only GPS AOA but also interference AOA at the high-power interference environment. In this paper, we propose a GPS AOA estimation algorithm, based on the composite despreader and the null despreader for multiple GPS signals. The AOA estimation result based on the composite despreading output includes AOAs of GPS and interference signals, and that based on the null despreading output includes only AOAs of interference signals. In order to determine GPS AOAs, we finally compare two results. The proposed algorithm efficiently estimates multiple GPS AOAs at a time, unlike the conventional technique.

The time-of-arrival (TOA) trilateration may have the serious estimation error, when three circles do not intersect at single point. In this paper, we classify all possible error cases of estimating the location of the mobile station (MS). In addition, we propose the individual estimation algorithms for each case, which are employed with a hybrid form.

Underlaying Device-to-device (D2D) communication is intimated as an encouraging technology for the next generation cellular network, where nearby devices can directly communicate to one another abstaining the evolved nodeB (eNB). In this paper, we propose a resource sharing mechanism which employs for multiple D2D communications by sharing a single cellular link simultaneously. In order to minimize the interference caused by multiple D2D pairs while sharing single cellular link at a time, we propose a metaheuristic algorithm based on the comprehensive interference minimization with reference to an uplink cellular network. The simulation results demonstrate that the proposed algorithm attains efficient performance compared with basic schemes.

Device-to-Device (D2D) communication provides high data rate requirements within the available access network and is viewed as a promising solution for the increasing mobile data traffic. D2D communication carries out direct communication for users located in close proximity to each other, without passing through the eNB. However, integration of D2D communication in cellular network introduces technical challenges like interference among cellular users and D2D pairs, and D2D pairs and eNB for the uplink cellular system which deteriorates the system performance. In this paper, we propose a distance-based throughput optimization method for D2D communications in a sectored multicell system using fractional frequency reuse (FFR) method. The simulation results show that our proposed scheme significantly improves system performance by mitigating interference.

Wireless sensor networks are being deployed in different event monitoring applications where the location of a source node might indicate information such as the presence of valuable asset or person, the behavior of an event etc. Thus, strong location privacy preservation technique is always preferred by the sensor system. Although the content is secured with different encryption techniques, open wireless channel allows an adversary to analyze traffic patterns, trace back and locate the source node. To achieve source-location privacy, several techniques based on flooding, random walk and dummy traffic transmission are proposed for WSN. In this paper, we propose a new routing method that randomly forwards a packet towards the sink such that an adversary will find difficult in tracing the source location. We evaluate privacy of our proposed method in terms of success rate of an adversary. The simulation results show proposed routing strategy is better in preserving privacy.

Radio-frequency identification (RFID) is a tracking technology that enables immediate automatic object identification and rapid data sharing for a wide variety of modern applications using radio waves for data transmission from a tag to a reader. RFID is already well established in technical areas, and many companies have developed corresponding standards and measurement techniques. In the construction industry, effective monitoring of materials and equipment is an important task, and RFID helps to improve monitoring and controlling capabilities, in addition to enabling automation for construction projects. However, on construction sites, there are many tagged objects and multiple RFID tags that may interfere with each other’s communications. This reduces the reliability and efficiency of the RFID system. In this paper, we propose an anti-collision algorithm for communication between multiple tags and a reader. In order to suppress interference signals from multiple neighboring tags, the proposed algorithm employs the time-division (TD) technique, where tags in the interrogation zone are assigned a specific time slot so that at every instance in time, a reader communicates with tags using the specific time slot. We present representative computer simulation examples to illustrate the performance of the proposed anti-collision technique for multiple RFID tags.

Wireless Body Area Network (WBAN) is a special purpose sensor application network which can be applied for applications relating to the health care. WBAN should support immediate and reliable data transmission for medical service during an emergency situation. Hence this paper proposes a new MAC superframe structure which can handle emergency data as well as a regular periodic data at the time of emergency. Simulation result shows low latency and increased throughput of the proposed superframe.

There has been an upward trend in the requirement of indoor positioning systems using bluetooth low energy (BLE), Wi-Fi, and visible light communication. In order to realize the indoor positioning with these communication systems, techniques such as fingerprinting, trilateration, and triangulation have been widely studied. Even though fingerprinting has been chosen as a representative approach in many literatures, it is known as tedious and time consuming method due to the long-time location learning phase. Therefore, the fingerprinting is expected to be integrated with other methods to enhance the location accuracy and reduce the location estimation procedures. In this work, we discuss one of these methods used for indoor positioning, i.e., weighted centroid localization (WCL) using received signal strength indicator (RSSI) observed from neighboring BLE beacons. The WCL is evaluated in our testbed building and analyzed to configure its parameters for indoor positioning.

The time of arrival trilateration method is one of the representative algorithms for the location detection technology, which estimates the location of mobile station (MS) at a unique intersection point of three circles with radiuses corresponding to distances between MS and base stations (BSs) and centers corresponding to coordinates of BSs. However, there may be serious estimation errors, when they do not meet at a point because the estimated radiuses of them are increased. The solutions for reducing the estimation position error in the main case of meeting three circles with the extended radius have been recently provided as the shortest distance algorithm and the line intersection algorithm. In general, they have good performance for the location estimation, but they may have serious errors in some cases. In this paper, we propose the efficient location estimation algorithm for the specific case of two large circles and one relatively small circle, which is located in the area of two large circles. In this case, there are six intersections in total based on the three extended circles and a small circle has four intersections with two large circles. The proposed approach compares four distances based on four neighboring intersections and selects the shortest one. Finally, it determines the averaged coordinate of two intersections corresponding to the shortest distance, as the location of MS. The location-estimating performance of the proposed algorithm is illustrated by the computer simulation example.