Customizing Indoor Wireless Coverage via 3D-Fabricated Reflectors

Judicious control of indoor wireless coverage is crucial in built environments. It enhances signal reception, reduces harmful interference, and raises the barrier for malicious attackers. Existing methods are either costly, vulnerable to attacks, or hard to configure. We present a low-cost, secure, and easy-to-configure approach that uses an easily-accessible, 3D-fabricated reflector to customize wireless coverage. With input on coarse-grained environment setting and preferred coverage (e.g., areas with signals to be strengthened or weakened), the system computes an optimized reflector shape tailored to the given environment. The user simply 3D prints the reflector and places it around a Wi-Fi access point to realize the target coverage. We conduct experiments to examine the efficacy and limits of optimized reflectors in different indoor settings. Results show that optimized reflectors coexist with a variety of Wi-Fi APs and correctly weaken or enhance signals in target areas by up to 10 or 6 dB, resulting to throughput changes by up to -63.3% or 55.1%

WOAIP: Wireless Optimization Algorithm for Indoor Placement Based on Binary Particle Swarm Optimization (BPSO)

تحسين نشر نقطة الوصول (AP) له دور كبير في التطبيقات اللاسلكية بسبب الحاجة إلى توفير اتصال فعال بتكاليف نشر منخفضة. تقترح هذه الدراسة التحقيق في خوارزمية تحسين متعددة المستويات تسمى خوارزمية التحسين اللاسلكي للوضع الداخلي (WOAIP) استنادًا إلى تحسين حشد الجسيمات الثنائية (BPSO). يهدف WOAIP إلى الحصول على وضع AP الأمثل متعدد الطوابق مع تغطية فعالة تجعله أكثر قدرة على دعم جودة الخدمة (QoS). تم أخذ خمسة أزواج (التغطية ، نشر AP) من الأوزان ، عتبات الإشارة وقياسات قوة الإشارة المستقبلة (RSS) المحاكية باستخدام برنامج Wireless InSite (WI) في دراسة حالة الاختبار. من خلال مقارنة النتائج التي تم جمعها من WI مع نشر AP المادي المحاكي اللاسلكي الحالي للمبنى المستهدف - قسم علوم الحاسوب في جامعة بغداد. يُظهر تقييم أداء WOAIP زيادة من حيث موضع AP والتحسين المميز من أجل زيادة نسبة التغطية اللاسلكية إلى 92.93٪ مقارنة بـ 58.5٪ من تغطية AP الحالية (أو تحسين التغطية بنسبة 24.5٪ في المتوسط).Optimizing the Access Point (AP) deployment has a great role in wireless applications due to the need for providing an efficient communication with low deployment costs. Quality of Service (QoS), is a major significant parameter and objective to be considered along with AP placement as well the overall deployment cost. This study proposes and investigates a multi-level optimization algorithm called Wireless Optimization Algorithm for Indoor Placement (WOAIP) based on Binary Particle Swarm Optimization (BPSO). WOAIP aims to obtain the optimum AP multi-floor placement with effective coverage that makes it more capable of supporting QoS and cost-effectiveness. Five pairs (coverage, AP deployment) of weights, signal thresholds and received signal strength (RSS) measurements simulated using Wireless InSite (WI) software were considered in the test case study by comparing the results collected from WI with the present wireless simulated physical AP deployment of the targeted building - Computer Science Department at University of Baghdad. The performance evaluation of WOAIP shows an increase in terms of AP placement and optimization distinguished in order to increase the wireless coverage ratio to 92.93% compared to 58.5% of present AP coverage (or 24.5% coverage enhancement on average)

A Coverage Prediction Technique for Indoor Wireless Campus Network

The placement of an Access Point (AP) is an important key to determine the spread of the signal. To get the optimal spread of signals, a network designer is required to understand how much coverage an AP can generate. A prediction is given to describe the coverage area produced based on AP placement for the wireless campus network, using a coordinate map modeling based on the real size for the indoor environment. The theoretical approach is used to determine the coverage area of an AP device by testing the function of the distance between the AP and the user. The results show that the signal generated by an AP will cover the entire area that is still on the LOS propagation path. The coverage area generated through AP placement in this case study reached 77.5%. The maximum distance between the AP and the user so that it is within the coverage area is 13.851m. There are still areas that are not covered by the AP, especially for the NLOS propagation path because of the obstruction around the AP.The placement of an Access Point (AP) is an important key to determine the spread of the signal. To get the optimal spread of signals, a network designer is required to understand how much coverage an AP can generate. A prediction is given to describe the coverage area produced based on AP placement for the wireless campus network, using a coordinate map modeling based on the real size for the indoor environment. The theoretical approach is used to determine the coverage area of an AP device by testing the function of the distance between the AP and the user. The results show that the signal generated by an AP will cover the entire area that is still on the LOS propagation path. The coverage area generated through AP placement in this case study reached 77.5%. The maximum distance between the AP and the user so that it is within the coverage area is 13.851m. There are still areas that are not covered by the AP, especially for the NLOS propagation path because of the obstruction around the AP

WiPrint: 3D Printing Your Wireless Coverage

Wireless signals are everywhere in residential, commercial and industrial environments. Directing wireless signals to conform to custom physical boundaries is of great importance in improving the performance, security and privacy of a wireless system. Unfortunately current solutions like directional antennas are bulky and expensive for ordinary users. We propose WiPrint, a novel approach to customizing wireless signal maps using 3D printed glossy reflectors. This solution is easily manufactured and adapts easily to different environments. The WiPrint system is highly flexible as it does not require adding additional APs or moving the AP to new locations. This is significant in the field of wireless networking as it provides consumers with an intuitive and novel solution to performance and security problems

Indoor Propagation Channel Models For Wireless Lan Based On 802.11b Standards At 2.4 Ghz Ism Band

The WLAN is a preferred choice of technology for internet connection in the building environment. The indoor models, reported in the literature are mostly studied in the 900 MHz band of cellular standard and quite scarce in the 2.4 GHz frequency band of WLAN 802.11 standard. The frequency band is also dedicated for the WiMAX technology in which deployment in the office environment is essential. In this thesis, the semi-empirical indoor Multi Wall Classic Extended (MWCE) channel model is proposed. The model is compared and evaluated with the empirical OS and other semi-empirical Multi Wall models obtained from the literature; the Multi Wall Classic (MWC) and Multi Wall Linear (MWL). The models are evaluated based on the accuracy of prediction at two floors of office environment in one of the telecommunication company building. The validity of the proposed model is evaluated through comparison with different models of similar type from the literature. The optimized model coefficients for all models, particularly for the wood/glass and brick/concrete the common wall obstacles in the building, are found. The behavior and characterization of all the models studied are investigated by evaluating the variation of the prediction error at several locations of the same propagation condition. The prediction from the MWCE model is significantly improved compared to the OS model. The MWCE model is also observed to have a high and consistent accuracy prediction, comparable with the MWC and MWL models. The accuracy of the MWCE model is also shown to compare closely with different models of similar type from the literature. With simple formulation without invoking too many details and high consistent accuracy prediction, the proposed MWCE model is suitable for prediction of WLAN signal in the indoor environment to be incorporated in the software planning tool

RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table

Role of Wireless technology in mobile augmented reality system (MARS)

Not availabl

A Hybrid Indoor Location Positioning System

Indoor location positioning techniques have experienced impressive growth in recent years. A wide range of indoor positioning algorithms has been developed for various applications. In this work a practical indoor location positioning technique is presented which utilizes off-the-shelf smartphones and low-cost Bluetooth Low Energy (BLE) nodes without any further infrastructure. The method includes coarse and fine modes of location positioning. In the coarse mode, the received signal strength (RSS) of the BLE nodes is used for location estimation while in the fine acoustic signals are utilized for accurate positioning. The system can achieve centimeter-level positioning accuracy in its fine mode. To enhance the system’s performance in noisy environments, two digital signal processing (DSP) algorithms of (a) band-pass filtering with audio pattern recognition and (b) linear frequency modulated chirp signal with matched filter are implemented. To increase the system’s robustness in dense multipath environments, a method using data clustering with sliding window is employed. The received signal strength of BLE nodes is used as an auxiliary positioning method to identify the non-line-of-sight (NLoS) propagation paths in the acoustic positioning mode. Experimental measurement results in an indoor area of 10 m2 indicate that the positioning error falls below 6 cm