Received Signal Strength (RSS)

The effectiveness of secret key extraction, for private communication between two smart sensible wireless nodes i.e. IoT devices [1], from the received signal strength (RSS) variations on the wireless channel between the two smart devices are being evaluated & studied. The real world measurements of RSS in a variety of environments and settings are used. While RSS is included in most commercial hardware platforms, researchers have expressed doubts about the reliability of RSS measurements [3,5,8,10],especially from beacons that are a considerable distance from the localizing node[2]. The study of 802.11 b/g network and using available APs in a real-time work environment, based laptops show that:

Conventional received signal strength (RSS)-based algo- rithms as found in the literature of wireless or acoustic networks assume either that the emitted power is known or that the distance power law exponent is known from calibration. We have considered a network of microphone sensors that is rapidly deployed in an unknown environment where the distance power law exponent is unknown or may vary with time. Also, the emitted power is inherently unknown in the localization and tracking applications under consideration. For loca- lization, both the emitted acoustic energy and the power law exponent are nuisance parameters unique for each target and sensor type, but constant over the sensor nodes.

localize the more than one attacker. To use the received signal strength based spatial correlation, a physical property present in the wireless node that is difficult to falsify and they are not depends on cryptography for detecting spoofing attacks. I concerned with attackers, they have different locations than legitimate wireless nodes, and spatial information is used to detect spoofing attacks that has a power to not only detect the presence of the attacks but also localize the attackers. In the existing system the cryptographic method needs reliable key distribution, management, and maintenance. It is not always possible to apply these methods because of its infrastructural and management overhead. In the existing system, cryptographic method node are said to be easily attacked, which is a serious matter. The wireless nodes are said to be easily accessible, by allowing their memory. The memory is said to be scanned by the attackers. Cryptographic method only protects the data frames. In the proposed system, to detect the attacks on wireless localization, proposed to use the direction of arrived and received signal strength of the signals to localize adversaries. In my work, I choose a group of algorithms based on RSS to perform the work of localizing more than one attacker and calculate the performance in the terms accuracy of localization.

(see [8]) is available on intelligent cell identification in the energy saving SON context. In this paper we present the fingerprinting method as an intelligent, self-organized approach to identify the most appropriate cell(s) to take over the emerging traffic. In contrast to the more common fingerprint applications for User Equipment (UE) positioning (e.g. [9], [10], [11], [12], [13], [14], [15] and [16]), this paper focuses on the identification of cell areas. The cell fingerprints are solely based on the Received Signal Strength (RSS) of the neighbor cells as measured by the UE. The neighbor cell RSS distributions are the input to derive specific cell patterns for the matching algorithm. The proposed cell fingerprinting method is a probabilistic approach, taking individual RSS distributions as well as RSS outliers into account. In the context of this paper, RSS is used interchangeably with Reference Signal Received Power (RSRP), the 3rd Generation Partnership Project (3GPP) term in Long Term Evolution (LTE) and Long Term Evolution - Advanced (LTE-A).

Abstract: The term partial discharge (PD) refers to a partial bridging of insulating material between electrodes that sustain an electric field in high-voltage (HV) systems. Long-term PD activity can lead to catastrophic failures of HV systems resulting in economic, energy and even human life losses. Such failures and losses can be avoided by continuously monitoring PD activity. Existing techniques used for PD localization including time of arrival (TOA) and time difference of arrival (TDOA), are complicated and expensive because they require time synchronization. In this paper, a novel received signal strength (RSS) based localization algorithm is proposed. The reason that RSS is favoured in this research is that it does not require clock synchronization and it only requires the energy of the received signal rather than the PD pulse itself. A comparison was made between RSS based algorithms including a proposed algorithm, the ratio and search and the least squares algorithm to locate a PD source for nine different positions. The performance of the algorithms was evaluated by using two field scenarios based on seven and eight receiving nodes, respectively. The mean localization error calculated for two-field-trial scenarios show, respectively, 1.80 m and 1.76 m for the proposed algorithm for all nine positions, which is the lowest of the three algorithms. Keywords: field trials; localization algorithm; least squares algorithm; partial discharge; ratio and search algorithm; RSS

en goal in man-made environments such as hallways in a building. Navigation services which usually depend on GNSS [1]-[3] are limited to be utilized in open areas with satellite signals. If users or robots are about to move in buildings, another approach must be used to navigate accurately. In this paper, Radio Frequency Identification (RFID) [4] is used to determine the location indoors. In RFID positioning, there are some common approaches to estimate the location. One method is based on signal power. Received signal strength (RSS) [4] [5] will be considered which presents the power of received signal as the measurement. The position will be calculated with certain methods based on the measurements. Several methods have been studied, such as cell based positioning, RFID location fingerprinting and the way using ranges to the tags calculated with RSSI [6]. Another particular method to approximate the location is based on the landmarks [7] [8]. In the landmark-based navigation, land- marks are needed to be set in the building, usually on certain doors and corners. A topological map with nodes corresponding to landmarks issued to do the navigation. System proposes a method using passive Radio Fre- quency Identification (RFID) tags as a landmark. The RFID tags are an unassertive i.e. passive type, non-contact, read-only memory system and can store a unique number for identification of the place. The tag data can be read from one meter away via electromagnetic waves. The tags are pasted on obstacles at any specific area. Robots just pass by the tags. The tags allow the acquisition of location information at mention speeds without the accu- rate control of robot positions for sensing the tags [9]. The passive RFID tags do not require an on-board power supply like a battery and generate operating power from the received electromagnetic waves. The tags are very thin and light. They therefore can be embedded easily in the environment and offer a virtually unlimited opera- tional lifetime.

Radio systems that are capable of localization have many applications [14]. Received signal strength (RSS) based localization can be range-based or range-free [15] [16]. Range based localization is strictly based on time synchronization and changes to the deployed systems are hard to implement due to synchronization. Techniques such as TOA, TDOA and DOA are all range based localization techniques [17]. Range free localization is purely based on the received signal strength and there are no synchronization requirements. For example, the work in [18] is based on localization of the source based on centroid point calculation. Similarly, the work in [19] utilizes the trilateration algorithm to estimate the location of the source.

The existing indoor localization techniques can be divided into two types: one is parametric method, and the other is so-called non-parametric method. The former is to estimate the position of target by measuring some parameters, such as received signal strength (RSS)[4], time-of-arrival (TOA)[5], time-difference-of-arrival (TDOA)[6], and angle-of-arrival (AOA)[7], or their combinations. By transforming these parameters into the distances, one can localize the interested targets by using the so-called trigonometry. The main drawback of these techniques is that the transforming between the measured parameters and distances has big bias due to the model parameters which is used to estimate distances are not precise. And it is always difficult to predict these parameters, for example, path loss factor. So the parametric method shows low accuracy in complex indoor localization environment.

While considering bandwidth like beneficial criteria, the satisfaction is high so utility value equals to u(x) and in case of non-beneficial like delay the utility value equals to {1-u(x)} [6]. In literature survey, for voice application in HWN, sample RSS had been considered for realistic performance analysis and proposed a framework for VHO triggering algorithms focusing on WLAN, and also other aspects such as small-scale fading effects, randomness in user‘s motion, factors that causes call drops were properly considered. However the proposed algorithm was based on RSS only and also other wireless networks were not considered [1]. For VHO in HWNs an optimized algorithm was proposed using an objective function considering load balancing, received signal strength, velocity and price, thus reducing unnecessary handoffs and improving service quality level and capacity of the system, Showed the reduction in handoffs with increase in velocity of UE i.e. for high speed UEs handoff is not required, Also two scenarios are considered with different weighting combinations, under first scenario only RSS and velocity were considered for handoff prediction, while under second scenario RSS, load, velocity and bandwidth were considered for handoff prediction [2]. A VHO decision policy was proposed considering the service quality perceived by the user, provided a modification for ranking networks to select the best radio access terminal, and better quality service among different radio access networks, and also, to avoid the processing delay in computations and multiple handoff ping pong effect by pre- selection of access terminal [3]. Derived an algorithm for VHO in k-tier HWN (consists of GSM, UMTS, LTE and WLAN networks) which suited Indian Urban or sub-urban terrains, by estimating the Path Loss (PL) and the RSS for each tier of k- tier HWN using statistical and empirical models [4]. Analyzed the different Multi Attribute Decision Makings (MADMs), performed seamless handoff to best available network using handoff decision algorithm that considered network related attributes, user preferences and system related attributes, and explored the VHO management problem [5]. A selection algorithm was proposed on the basis of service characteristics and preferences of user in HWNs, integrating utility function, entropy and MADM methods with network attributes (utility values and subjective and objective weights of network ________________________________

This paper apprised the issue of finding the location of a single sensor node with a single beacon in a terrestrial wireless sensor network (WSN). Generally, the localization of a single sensor node in a terrestrial sensor network can be solved using multilateration technique with respect to three or more known beacon nodes. However, there is an area of concern, when the localization of a single sensor node (i.e. mobile station, cell phone) is to be measured with respect to only one known beacon node i.e. base transceiver station (BTS). Such a challenge is aimed to be solved with the help of received signal strength (RSS) survey data for a particular location within the desired environment. A simulated terrain and a model has been created based on RSS Survey data that defines the contours of radio frequency (RF) coverage in a particular test facility under a single beacon node. Simulation results show that our proposed model gives a solution which converges to determine the location of a single sensor node with respect to a single beacon node.

Copyright © 2010 Yuri ´Alvarez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A received signal strength- (RSS-)-based indoor location method (ILS) for person/assets location in indoor scenarios is presented in this paper. Theoretical bases of the method are the integral equations relating the electromagnetic (EM) fields with their sources, establishing a cost function relating the measured field at the receivers and the unknown position of the transmitter. The aim is to improve the EM characterization of the scenario yielding in a more accurate indoor location method. Regarding network infrastructure implementation, a set of receivers are deployed through the coverage area, measuring the RSS value from a transmitter node which is attached to the asset to be located. The location method is evaluated in several indoor scenarios using portable measurement equipment. The next step has been the network hardware implementation using a wireless sensor network: for this purpose, ZigBee nodes have been selected. Finally, RSS measurements variability due to multipath effects and nonline-of- sight between transmitter and receiver nodes is mitigated using calibration and a correction based on the difference between the free space field decay law and the measured RSS.

Copyright © 2017 Zhanyong Tang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Evil-Twin is becoming a common attack in smart home environments where an attacker can set up a fake AP to compromise the security of the connected devices. To identify the fake APs, The current approaches of detecting Evil-Twin attacks all rely on information such as SSIDs, the MAC address of the genuine AP, or network traffic patterns. However, such information can be faked by the attacker, often leading to low detection rates and weak protection. This paper presents a novel Evil-Twin attack detection method based on the received signal strength indicator (RSSI). Our approach considers the RSSI as a fingerprint of APs and uses the fingerprint of the genuine AP to identify fake ones. We provide two schemes to detect a fake AP in two different scenarios where the genuine AP can be located at either a single or multiple locations in the property, by exploiting the multipath effect of the Wi-Fi signal. As a departure from prior work, our approach does not rely on any professional measurement devices. Experimental results show that our approach can successfully detect 90% of the fake APs, at the cost of a one-off, modest connection delay.

positioning of a science and technology museum. The location fingerprint algorithm is used to study the offline acquisition and online positioning stages. The positioning flow of the location fingerprint algorithm is discussed, and the improvement of the location fingerprint algorithm is emphasized. The raw data of the RSSI (received signal strength indication) is preprocessed, which makes the location fingerprint data more effective and reliable, thus improving the positioning accuracy. Three different improvement strategies are proposed for the nearest neighbor classification algorithm: a balanced joint metric based on distance weighting and a compromise between the two. Then, in the experimental simulation, the positioning results and errors of the traditional KNN (k-nearest neighbor) algorithm and three improvement strategy algorithms are analyzed separately, and the effectiveness of the three improved strategy algorithms is verified by experiments.

The purpose of this research is to develop an accurate and energy efficient aquatic localization scheme through the signal ranging parameters. The development of accurate positioning method is based on the Received Signal Strength Indication modeling concept. Accuracy is a crucial property within Underwater WSN applications like offshore-engineering and construction activities because the environment is not accessible and observable. Furthermore, the state-of-the-art sensors appear as promising technology to reduce the cost of such networks. The main advantage of this method is that there is no need to install any extra hardware and it uses the available resources of the sensor node. To obtain the energy efficiency, a new localization algorithm is developed that fulfills the localization in three separate steps including initialization, distance calculation, and position estimation. In initialization, sensor nodes store received signal strength of beacons. Sensors send those information back to sink where the distances are measured. Finally, the sink calculates the position of sensors utilizing the efficient method of Lambert W function. The developed localization helps sensor nodes to have a coarse location estimation and it extracts the accurate position by using the sink as a powerful node. This algorithm uses two-level computational approach as it employes both distributed and centralized calculations. This research is created a general adaptive scheme regarding the changes of affective aquatic parameters.

Abstract--This work presents a comparative analysis of the received signal strength measurement of GSM network Owo, Ondo state Nigeria. Software was developed to create an interface for the computer to aid in the measurement of the RSSI from different networks in Owo at random sampling points. The three networks considered were; MTN, GLO and AIRTEL in the three different locations (Old Ikare Road, Idasen and Rufus Giwa Polytechnic) within Owo. The data collected were analysed using SPSS (Statistical Package for Social Scientists) as the statistical tool. The mean value of the signal strength received using the developed software for MTN, GLO and AIRTEL are - 73.11, -78.74 and -80.75 respectively. The results of the analysis reveal that F-computed (F -Computed ) from the F-Test

The distinction between a new legitimate node and a new Sybil identity can be made based on their neighborhood joining behavior. For example, new legitimate nodes become neighbors as soon as they enter inside the radio range of other nodes; hence their first RSS at the receiver node will be low enough. In contrast a Sybil attacker, which is already a neighbor, will cause its new identity to appear abruptly in the neighborhood. When the Sybil attacker creates new identity, the signal strength of that identity will be high enough to be distinguished from the newly joined neighbor. In order to analyze the difference between a legitimate newcomer and Sybil identity entrance behavior, we setup some experiments in the following. Before we start, it is important to explain how each node collects and maintains the RSS values of the neighboring nodes.

The classic methods to estimate the indoor location are time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), and received signal strength (RSS). TOA method measures travel times of signals between nodes. TDOA method locates by measuring the signals’ arrival time difference between anchor nodes and unknown node. It is able to achieve high ranging accuracy, but requires extra hardware and consumes more energy. As an inexpensive approach, RSS has established the mathematical model on the basis of path loss attenuation with distance and it requires relatively low configuration and energy.

ABSTRACT: Context-aware applications have been gaining huge interest in the last few years. With cell phones becoming ubiquitous computing devices, cell phone positioning has become an important research problem, and numerous localization solutions have been proposed. Effective LBS technology requires accurate location information of the mobile device and demands accurate and reliable mobile positioning technology. Absolute received signal strength values from base station changes with time, but the relative received signal strength values which refer to the relations of the received signal strength values between different base stations are more stable. In this paper we examine the performance of localizing a mobile station in a GSM network based on RSS of practical measurements. The localization method proposed in this paper is based on the fingerprinting method.

The CC2431 IC from Texas Instruments was selected as the testing platform. CC2431 is a 2.4 GHz IEEE802.15.4 / ZigBee System-on-Chip (SoC) IC chip. The CC2431 does not publish RSS value directly. Instead, the RSS value is measured internally and published as Received Signal Strength Indicator (RSSI). After loaded in the ZigBee protocol stack (Z-Stack) from Texas Instruments to the CC2431 Evaluation Module (EM), firmware can access the value from the RSSI register in CC2431.

Abstract—A novel indoor positioning system based on received signal strength (RSS) in wireless networks with high accuracy is presented in this paper. The three improvement mechanisms, called signal strength filter, user location filter and path tracking assistance, are employed to improve the positioning accuracy of the system. The comprehensive performance of the proposed system is analyzed in detail and compared with the Radar system. Experimental results demonstrate that the proposed system in this paper can improve 80% accuracy in 3 meters of Radar system to 93% in typical office building testbed. Therefore, the indoor positioning system presented in this paper has the advantages of high accuracy, low cost and easy expansibility, and it can be used to locate people and assets in the fields of logistics, healthcare, and manufacturing.