Received Signal Strength Indicator Information Research Articles

Reliable critical nodes detection for Internet of Things (IoT)

The 3D critical node (C-N) detection can play a vital role in algorithm development of security, surveillance, monitoring, topology detection, and situation-aware emergency navigation for the Internet of Things (IoT). However, 3D C-N detection problem in IoT raises some issues and also introduces new challenges. The existing state of the art in 3D C-N detection shows that rely on prior known anchor node, known coordinate, embedding of the 3D situation on a 2D geometrical structure like circles and presence of unreliable node and ignores the energy constraint in Low Power and Lossy Networks IoT. In this paper, we present a practical, distributed, and energy-efficient algorithm for reliable 3D C-N detection. The goal of the proposed mechanism is twofold, firstly a 3D critical nodes (C-N) detection algorithm is proposed which uses only Received Signal Strength Indicator information of neighbor. Secondly, a correlation-based algorithm for the reliability approach is proposed to increases the node resilience against malicious IoT nodes. The complexity of our proposed algorithms has a time complexity of $$\mathcal {O}(\log (N))$$ and computation cost $$\mathcal {O}(\delta (\log N))$$ where N is the number of nodes in networks, and $$\delta $$ is the total number of forward and the backward message from an individual node. To validate our work, we implemented our proposed approach with the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) based IoT routing protocol compare it with RPL and cryptographic approach Version Number and Rank Authentication (VeRa). The result shows that the proposed approach can detect 10–15% more C-N nodes. Result also shows that our proposed algorithm has better PDR than RPL based approach by 12% and less than VeRa (cryptographic approach) by 8% however our proposed approach consumes almost 50% less power than the VeRa.

Building Layout Tomographic Reconstruction via Commercial WiFi Signals

Recently, wireless communication signals are not only used for information transmission but other possibilities are being developed, such as human body state estimation, wireless sensing, etc. In this article, the radio tomographic reconstruction of building layout and interior object is considered only utilizing the received signal strength indication (RSSI) of commercial WiFi signals. Specifically, first, an electromagnetic transmission energy attenuation model is introduced, which considers the case for scanning the entire unknown area using two wireless nodes. Then, we propose a novel algebraic iterative reconstruction method, which takes into account the constraint of prior knowledge and designs a more reasonable initial iteration factor. Finally, a variety of scenarios with different structures is built, and the reconstruction of the multimedia scenario is achieved for the first time with RSSI information. The simulation and experimental results show that the proposed method has better performance than current state-of-the-art algorithms in both the single-medium scenario and multimedia scenario.

Automated UHF RFID‐based book positioning and monitoring method in smart libraries

In this study, a method is proposed for ultra high frequency radio frequency identification (UHF RFID)-based book positioning and counting developed for smart libraries. In the experimental setup created, RFID tags placed in books were automatically detected using three RFID antennas. Using received signal strength indicator information from each antenna for each book, the locations of the books are determined. In addition, classification was made by using machine learning approaches for the study. For this purpose, the best result for sequence determination in the classification study using ensemble trees, K nearest neighbours (KNN), and support vector machine algorithms was obtained with the ensemble subspace KNN algorithm with 94.1%. The best result for cabinet detection was obtained in the study using the ensemble subspace KNN algorithm and a 78.5% accuracy rate was achieved. The best result for rack detection was obtained with the ensemble subspace KNN algorithm with 95.4%. The study is thought to be useful in the automatic determination of the row, cabinet, and rack of books in smart libraries.

Enhancement of RSSI-Based Localization Using an Extended Weighted Centroid Method With Virtual Reference Node Information

In this paper, an extended weighted centroid localization (EWCL) method for a received signal strength indicator (RSSI)-based localization system is proposed. In the traditional WCL method, an unknown target position is estimated using the actual position of each reference node and RSSI information collected from each reference node based on the distance between the target and each reference node. The estimation accuracy is significantly increased when more reference nodes are applied in the localization system. In addition, in the traditional WCL method, four reference nodes deployed in a square area are initialized and tested. Such a configuration produces good localization results. By this information, in this work, the EWCL method is developed to estimate a target’s position taking into consideration information from four reference nodes, where only three actual reference nodes are deployed in a real test area. Here, the fourth reference node position as the virtual reference node position is defined, and a new technique involving a guideline solution is developed to estimate the distance between the target and the fourth reference node using distance information from the three actual reference nodes. By our purpose, the hardware cost and the complexity of the experimental setup can be reduced, while the estimation accuracy can be increased. To verify the EWCL method, both theoretical and experimental studies are performed. The theoretical study first demonstrates the EWCL performance without the effect of radio signals. The experimental study using low-cost low-power 2.4 GHz nodes deployed in a semi-outdoor test area reveals more realistic results. The results indicate that the EWCL method significantly outperforms the WCL method in all test cases. The localization error is reduced by 49.151% in the theoretical test. In the experimental test, the localization errors are reduced by 33.708 and 28.351% for scenarios without and with a human presence, respectively.

LED Dağılımının RSSI Tabanlı Görünür Işık Konum Belirleme Sistemleri Üzerindeki Etkisi

In this paper, the effect of light emitting diode (LED) deployment is investigated based on the received signal strength indication (RSSI) technique for visible light positioning (VLP) systems. The optical code division multiple access (OCDMA) is used as a multiplexing technique to transmit the location and RSSI information of LEDs simultaneously. For different highly reflective indoor scenarios, LEDs are placed on the ceiling of a room considering homogeneous illumination. The illuminance of the room is provided particularly with 400 lux minimally at the desk height. In order to demonstrate the uniformity of lighting for different indoor scenarios, the uniformity illuminance ratio (UIR) values are also obtained in this study. In addition, electrical signal-to-noise ratio (SNR), an important parameter for reliable communication, is calculated at desk height for each scenario considered and an SNR level of 45 dB is obtained at a minimum . The simulation results demonstrate that the mean square error (MSE) of the estimated location is reduced with an increase in the number of visible access points (VAPs). The MSE of the distance for number of VAPs 12 and 16 are the best, however, the scenario with 16 VAPs is preferable with a better uniformity illuminance ratio (UIR) parameter. In terms of illuminance, all the cases have the average illuminance value more than 450 lux.

Spatio-Temporal Fingerprint Localization for Shipboard Wireless Sensor Networks

Location-aware technology has numerous potential applications in safety, living, and services in the shipboard environment. Fingerprint-based methods that use the received signal strength indication (RSSI) to provide high positioning accuracy have been widely adopted for indoor localization. However, the RSSI can be severely disturbed by the steel structure and dynamics of the shipboard environment, which prevents it from being implemented in shipboard localization in practice. In this paper, a novel approach, called spatio-temporal fingerprint localization, is proposed. This approach can alleviate the impact of the dynamic shipboard environment and enhance the localization robustness. To adapt to the environmental dynamics, a radio time series is proposed to filter irrelevant noise from location fingerprints. An extraction method called radio spatial features is proposed to identify the highly location related features from the redundant RSSI information by using linear discriminant analysis and principal component analysis. Extensive experiments on the passenger ship Yangtze 2 demonstrate the effectiveness of the proposed algorithm in providing higher accuracy than previous fingerprint-based methods.

Exploiting Radio Irregularity for Location Verification in Sparse MANETs

Traditional distance-based location verification system (LVS) is ineffective for some attacks in sparse mobile ad hoc networks, e.g., the similar distance-based malicious (SDM) attack. In this letter, we propose to exploit radio irregularity to build a novel LVS. Our system detects attack based on the estimated difference of radio irregularity coefficients and the claimed locations of the malicious node and an assistant node. To the best of our knowledge, our system is the first-of-its-kind that can detect SDM attack and pollution attack with only the received signal strength indicator information transmitted by a detecting node to the suspicious malicious node and an assistant node. Simulation results demonstrate that our proposed system can detect the attacks that cannot be detected by traditional distance-based methods.

Smarttag: An Indoor Positioning System Based on Smart Transmit Power Scheme Using Active Tags

There are no large-scale deployments to navigate the people indoor environments as the GPS for outdoor environments. For this reason, the development of high-performance systems of indoor localization has been considered the potential research areas by both academia and industry in last decade. These indoor localization research studies are divided into two groups in the literature which are reader-based and tag-based systems. Most of these studies are reader-based. These readers and tags can determine the received signal strength levels of the signal using the radio frequency technology. We present a tag-based and cost-effective indoor localization system using NRF24L series radio modules. Different from the available tag-based systems, we propose a smart transmit power scheme run in the tags called SmartTag. The SmartTag provides communication with the nearest reader using different power levels. However, the existing tag-based methods require the received signal strength indicator (RSSI) information to achieve high localization accuracy. Our proposed system eliminates RSSI information with the SmartTag. We have developed a simulation of the prototype system, and we used Monte Carlo simulations in many different scenarios. We have also implemented the system prototype and conducted in an actual indoor environment to demonstrate the performance of the SmartTag by comparing with the Monte Carlo simulations. From the indoor experimental results, we confirm that the detections of room and objects are accurate and positioning accuracy is improved thanks to the SmartTag localization. If we briefly summarize our work, we have determined the position in indoor environments by communicating with the nearest reader in the medium thanks to the scheme on the SmartTag.

An Improved Approach for RSSI-Based only Calibration-Free Real-Time Indoor Localization on IEEE 802.11 and 802.15.4 Wireless Networks.

Assuming a reliable and responsive spatial contextualization service is a must-have in IEEE 802.11 and 802.15.4 wireless networks, a suitable approach consists of the implementation of localization capabilities, as an additional application layer to the communication protocol stack. Considering the applicative scenario where satellite-based positioning applications are denied, such as indoor environments, and excluding data packet arrivals time measurements due to lack of time resolution, received signal strength indicator (RSSI) measurements, obtained according to IEEE 802.11 and 802.15.4 data access technologies, are the unique data sources suitable for indoor geo-referencing using COTS devices. In the existing literature, many RSSI based localization systems are introduced and experimentally validated, nevertheless they require periodic calibrations and significant information fusion from different sensors that dramatically decrease overall systems reliability and their effective availability. This motivates the work presented in this paper, which introduces an approach for an RSSI-based calibration-free and real-time indoor localization. While switched-beam array-based hardware (compliant with IEEE 802.15.4 router functionality) has already been presented by the author, the focus of this paper is the creation of an algorithmic layer for use with the pre-existing hardware capable to enable full localization and data contextualization over a standard 802.15.4 wireless sensor network using only RSSI information without the need of lengthy offline calibration phase. System validation reports the localization results in a typical indoor site, where the system has shown high accuracy, leading to a sub-metrical overall mean error and an almost 100% site coverage within 1 m localization error.

RSSI and Flower Pollination Algorithm Based Location Estimation for Wireless Sensor Networks

Wireless Sensor Networks (WSN’s) have been finding to itself new applications continuously. Many of these applications need location information of nodes. The localization of nodes can be made by range based or range free localization methods conventionally. Angle-of-Arrival (AoA), Time-Difference-of-Arrival (TDoA), Received Signal Strength Indicator (RSSI), Time-of-Arrival (ToA) are well known range based methods. Therefore AoA, ToA and TDoA have some hardware and software difficulties for nodes which have limited processing and power sources. However RSSI based localization doesn’t cost high processing resources or complex hardware modifications. Most of the WSN nodes already have RSSI measurement capability. However RSSI measurements is vulnerable to noise and environmental effects. Therefore error of RSSI based localization can be over to an acceptable level. Centroid, APIT, DV-Hop and Amorphous are some of the range free localization methods. Range free methods can only give location information approximately but they don’t need any extra hardware or high processing capability. In this study WSN nodes are assumed randomly or regularly distributed on a certain area. Some of the nodes are beacon nodes. The beacon nodes are assumed as having higher power resources and GPS receivers. The locations of nodes are assumed as fixed. The beacon nodes send their location information sequentially. Localization of nodes are made through RSSI and location information of beacon nodes. The mean of RSSI is calculated to reduce effect of noise on it. A rough location estimation made by weighted centroid. A probabilistic based location estimation and flower pollination algorithm (FPA) are used separately to make final decision about the location. Rough estimates are used to limit search area of flower pollination algorithm in order to reduce convergence time.

A Robot Localization System Combining RSSI and Phase Shift in UHF-RFID Signals

This paper proposes a global indoor localization system based on the radio frequency identification (RFID) technology. A reader, installed on the robot, measures the received signal strength indication (RSSI) and the phase shift of UHF-RFID signals coming from a set of passive tags deployed on the ceiling of the environment. The position of the tags in the environment is only roughly known at the beginning. Exploiting the complementary features of RSSI and phase-shift information in RFID signals (together with odometry data), a multihypothesis extended and unscented Kalman filter is proposed to localize the robot and to simultaneously improve the initial estimate on the tag coordinates. The simulative and experimental results are reported to illustrate the effectiveness of the proposed approach.

An enhanced location-free Greedy Forward algorithm with hole bypass capability in wireless sensor networks

Greedy Forward is a technique for data routing in Wireless Sensor Networks (WSNs) in which data packets are forwarded to the node that is geographically closer to the destination node. Two main concerns can be found in routing algorithms based on this technique: first, it requires all sensor nodes to know their physical location. Second, this kind of algorithm does not work in cases when a node is located in a network ’hole’, i.e., the node does not have any neighbor closer to the destination node. In this work, we propose a new Greedy Forward algorithm that can be used in routing protocols for WSNs that does not require localization of the nodes and also is able to deal with nodes located near network holes. Differently from current greedy forward algorithms, our approach uses only the Received Signal Strength Indicator (RSSI) of exchanged packets. Based on this observation, we propose the RSSR (Received Signal Strength Routing) algorithm with two variants: RSSR Election and RSSR Selection. In the RSSR Election, the next hop is dynamically elected and no packets are required for the routing task. In the RSSR Selection, neighbors exchange packets with RSSI information and the next hop of the packet is then selected from a routing table. Then, we present a novel technique for dealing with network holes even when the physical location of the nodes is unknown. This technique improves the reliability and applicability of the proposed schemes in most WSN scenarios. Our results indicate clearly that the proposed algorithms have all the benefits of a greedy forward algorithm but with better performance, better packet delivery rate and without requiring position information.

Detection of man‐in‐the‐middle attacks using physical layer wireless security techniques

AbstractCompared with a wired network, a wireless network is not protected by the cable transmission medium. Information is broadcasted over the air and it can be intercepted by anyone within the transmission range. Even though the transmissions could potentially be protected by security authentication mechanisms, malicious users can still intercept the information by mimicking the characteristics of normal user or a legitimate access point. This scenario is referred as a man‐in‐the‐middle (MITM) attack. In the MITM attack, the attackers can bypass the security mechanisms, intercept the unprotected transmission packets, and sniff the information. Because of several vulnerabilities in the IEEE 802.11 protocol, it is difficult to defend against a wireless MITM attack. In this paper, a received signal strength indicator (RSSI)‐based detection mechanism for MITM attacks is proposed. RSSI information is an arbitrary integer that indicates the power level being received by the antenna. The random RSSI values are processed via a sliding window, yielding statistic information about the signal characteristics such as mean and standard deviation profiles. By analyzing those profiles, the detection mechanism can detect if a rogue access point, the key component of an MITM attack, is launched. Our proposed approach has been validated via hardware experimentation using Backtrack 5 tools and MATLAB software suite. Copyright © 2014 John Wiley & Sons, Ltd.

Novel joint TOA/RSSI-based WCE location tracking method without prior knowledge of biological human body tissues.

This paper proposes a novel joint time of arrival (TOA)/received signal strength indicator (RSSI)-based wireless capsule endoscope (WCE) location tracking method without prior knowledge of biological human tissues. Generally, TOA-based localization can achieve much higher localization accuracy than other radio frequency-based localization techniques, whereas wireless signals transmitted from a WCE pass through various kinds of human body tissues, as a result, the propagation velocity inside a human body should be different from one in free space. Because the variation of propagation velocity is mainly affected by the relative permittivity of human body tissues, instead of pre-measurement for the relative permittivity in advance, we simultaneously estimate not only the WCE location but also the relative permittivity information. For this purpose, this paper first derives the relative permittivity estimation model with measured RSSI information. Then, we pay attention to a particle filter algorithm with the TOA-based localization and the RSSI-based relative permittivity estimation. Our computer simulation results demonstrates that the proposed tracking methods with the particle filter can accomplish an excellent localization accuracy of around 2 mm without prior information of the relative permittivity of the human body tissues.

Tracking Mobile Robot in Indoor Wireless Sensor Networks

This work addresses the problem of tracking mobile robots in indoor wireless sensor networks (WSNs). Our approach is based on a localization scheme with RSSI (received signal strength indication) which is used widely in WSN. The developed tracking system is designed for continuous estimation of the robot’s trajectory. A WSN, which is composed of many very simple and cheap wireless sensor nodes, is deployed at a specific region of interest. The wireless sensor nodes collect RSSI information sent by mobile robots. A range-based data fusion scheme is used to estimate the robot’s trajectory. Moreover, a Kalman filter is designed to improve tracking accuracy. Experiments are provided to assess the performance of the proposed scheme.

에드 혹 네트워크에서 노드의 국부 위치 정보를 이용한 최적 계층적 클러스터링 경로 라우팅 알고리즘

We proposed the energy-efficient routing algorithm ALPS (Ad hoc network Localized Positioning System) algorithm that is range-free based on the distance information. The routing coordinate method of ALPS algorithm consists of hierarchical cluster routing that provides immediately relative coordinate location using RSSI(Received Signal Strength Indication) information. Existing conventional DV-hop algorithm also to manage based on normalized the range free method, the proposed hierarchical cluster routing algorithm simulation results show more optimized energy consumption sustainable path routing technique to improve the network management.

Controllable Electrical Power Plug Adapters Made As A ZigBee Wireless Sensor Network

Using Internet communication, new home electronics have functions of monitoring and control from remote. However in many case these electronics work as standalone, and old electronics are not followed. Then, we developed the total remote system include not only new electronics but olds. This systems node is a adapter of electrical power plug that embed relay switch and some sensors, and these nodes communicate with each other. the system server was build on the Internet, and users access to this system from web browsers. To reduce the cost to set up of this system, communication between adapters are used ZigBee wireless network instead of wired LAN cable(3). From measured RSSI(received signal strength indicator) information between each nodes, the system can estimate roughly adapters were mounted on which room, and where in the room. So also it reduces the cost of mapping nodes. Using this system, energy saving and house monitoring are expected.

A Link Heterogeneity-Aware On-Demand Routing (LHAOR) Protocol Utilizing Local Update and RSSI Information

Many routing protocols have been proposed for mobile ad hoc networks. Among these protocols, the on-demand routing protocols are very attractive because they have low routing overhead. However, few of the existing on-demand routing protocols have considered the link heterogeneity, such as the different communication rate, different Packet Error Ratio (PER). As a result, the routes tend to have the shortest hop count and contain weak links, which usually provide low performance and are susceptible to breaks in the presence of mobility. In this paper, we analyze the existing on-demand routing protocols and propose a Link Heterogeneity Aware On-demand Routing (LHAOR) protocol, where the link quality and mobility are considered. Specifically, the Local Update (LU) is proposed and the link metric is inversely related with the Received Signal Strength Indicator (RSSI). By using the LU method and RSSI information, the routes adapt to the topology variation and link quality changes, and reach the local optimum quickly, which contains strong links and has a small metric. Simulation and experiment results show that our LHAOR protocol achieves much higher performance than the classical on-demand routing protocols.