Results 1  10
of
29
LinearRegression Estimation of the PropagationLoss Parameters Using Mobiles ’ Measurements in Wireless Cellular Networks
"... We propose a new linearregression model for the estimation of the pathloss exponent and the parameters of the shadowing from the propagationloss data collected by the mobiles with respect to their serving base stations. The difficulty consists in deriving the parameters of the distribution of the ..."
Abstract

Cited by 12 (6 self)
 Add to MetaCart
(Show Context)
We propose a new linearregression model for the estimation of the pathloss exponent and the parameters of the shadowing from the propagationloss data collected by the mobiles with respect to their serving base stations. The difficulty consists in deriving the parameters of the distribution of the propagation loss with respect to an arbitrary base station from these regarding the strongest one. The proposed solution is based on a simple, explicit relation between the two distributions in
Secure Distancebased Localization in the Presence of Cheating Beacon Nodes
"... Abstract—Secure distancebased localization in the presence of cheating beacon (or anchor) nodes is an important problem in mobile wireless ad hoc and sensor networks. Despite significant research efforts in this direction, some fundamental questions still remain unaddressed: In the presence of chea ..."
Abstract

Cited by 6 (2 self)
 Add to MetaCart
(Show Context)
Abstract—Secure distancebased localization in the presence of cheating beacon (or anchor) nodes is an important problem in mobile wireless ad hoc and sensor networks. Despite significant research efforts in this direction, some fundamental questions still remain unaddressed: In the presence of cheating beacon nodes, what are the necessary and sufficient conditions to guarantee a bounded error during a twodimensional distancebased location estimation? Under these necessary and sufficient conditions, what class of localization algorithms can provide this error bound? In this paper, we attempt to answer these and other related questions by following a careful analytical approach. Specifically, we first show that when the number of cheating beacon nodes is greater than or equal to a given threshold, there do not exist any twodimensional distancebased localization algorithms that can guarantee a bounded error. Furthermore, when the number of cheating beacons is below this threshold, we identify a class of distancebased localization algorithms that can always guarantee a bounded localization error. Finally, we outline three novel distancebased localization algorithms that belong to this class of bounded error localization algorithms. We verify their accuracy and efficiency by means of extensive simulation experiments using both simple and practical distance estimation error models. Index Terms—Wireless networks, distancebased localization, security. 1
Path Loss Exponent Estimation in Large Wireless Networks
"... Abstract—In wireless channels, the path loss exponent (PLE) has a strong impact on the quality of the links, and hence, it needs to be accurately estimated for the efficient design and operation of wireless networks. This paper addresses the problem of PLE estimation in large wireless networks, whic ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
Abstract—In wireless channels, the path loss exponent (PLE) has a strong impact on the quality of the links, and hence, it needs to be accurately estimated for the efficient design and operation of wireless networks. This paper addresses the problem of PLE estimation in large wireless networks, which is relevant to several important issues in communications such as localization, energyefficient routing, and channel access. We consider a large ad hoc network where nodes are distributed as a homogeneous planar Poisson point process and the channels are subject to Nakagamim fading. Under these settings, we propose and study three distributed algorithms for estimating the PLE at each node, which explicitly take into account the interference in the network. Additionally, we provide simulation results to demonstrate the performance of the algorithms and quantify the estimation errors.
Path Loss Exponent Estimation
 in Large Wireless Wetworks,” in Information Theory and Applications Workshop, 2009
"... ar ..."
(Show Context)
Dynamic Path Loss Exponent Estimation in a Vehicular Network using Doppler Effect and Received Signal Strength
"... Abstract — Localization in Adhoc wireless sensor networks is one of the important and demanded fields in engineering research and industry. Among these networks, vehicular Adhoc network, VANET, with emerging applications like Intelligent Transport Systems is considered as the most challenging area ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Abstract — Localization in Adhoc wireless sensor networks is one of the important and demanded fields in engineering research and industry. Among these networks, vehicular Adhoc network, VANET, with emerging applications like Intelligent Transport Systems is considered as the most challenging area. One of the fundamental parameters in VANET applications is the distances between the nodes which must be measured or estimated. Among the variety of radio range measurement techniques, Received Signal Strength (RSS) is very popular due to its simplicity and less cost compared to other methods like Time of Arrival (TOA), Time Difference of Arrival (TDOA), and Angel of Arrival (AOA). The main drawback of RSS based ranging is its considerable inaccuracy which is mostly originated from uncertainty of the path loss exponent which has an exponential effect on distance measurement. Without knowing the environment path loss exponent, which is a time varying parameter in the networks with mobility, RSS is useless for distance estimation. There are many approaches and techniques proposed in literature for dynamic estimation of path loss exponent within a certain environment. Most of these methods are not functional for mobile applications or their efficiency decreases dramatically with increasing the mobility of the nodes. In this paper, we propose a method for dynamic estimation of path loss exponent and distance based on Doppler Effect and RSS. Since this method is fundamentally based on Doppler Effect, it can be implemented within the networks with nodes relative mobility. The higher mobility of the nodes, the better performance of the proposed technique. Also, the proposed method is more suitable for vehicular networks in which, nodes are moving in the road constraint. The importance of this work contribution can be highlighted as the vehicles are going to be equipped with
Secure Distance Indicator Leveraging Wireless Link Signatures
"... Time (RTT) are two common metrics for a wireless receiver to tell the proximity of a remote wireless transmitter. A large RSS or a small RTT normally indicates a close transmitter, and vice versa. Both metrics are effective in a benign environment. However, when the transmitter modifies the send tim ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Time (RTT) are two common metrics for a wireless receiver to tell the proximity of a remote wireless transmitter. A large RSS or a small RTT normally indicates a close transmitter, and vice versa. Both metrics are effective in a benign environment. However, when the transmitter modifies the send time or transmit power to hide its real distance, they may fail to identify the actual proximity of the transmitter. In this paper, we propose a secure physical layer metric that not only reflects the distance between the transmitter and the receiver, but is difficult to manipulate. Our theoretical and experimental studies show that the proposed metric and the distance is inverse proportional, in both the ideal and practical scenarios with shadow fading and channel noise. We also create distance distribution profiles based on the proposed metric, and point out how such profiles can be used to enhance the reliability of the distance estimation. I.
Sensing coverage prediction for wireless sensor networks in shadowed and multipath The Scientific World
 Journal 7 environment,” The Scientific World Journal
"... Sensing coverage problem in wireless sensor networks is a measure of quality of service (QoS). Coverage refers to how well a sensing field is monitored or tracked by the sensors. Aim of the paper is to have a priori estimate for number of sensors to be deployed in a harsh environment to achieve des ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Sensing coverage problem in wireless sensor networks is a measure of quality of service (QoS). Coverage refers to how well a sensing field is monitored or tracked by the sensors. Aim of the paper is to have a priori estimate for number of sensors to be deployed in a harsh environment to achieve desired coverage. We have proposed a new sensing channel model that considers combined impact of shadowing fading and multipath effects. A mathematical model for calculating coverage probability in the presence of multipath fading combined with shadowing is derived based on received signal strength (RSS). Further, the coverage probability derivations obtained using Rayleigh fading and lognormal shadowing fading are validated by node deployment using Poisson distribution. A comparative study between our proposed sensing channel model and different existing sensing models for the network coverage has also been presented. Our proposed sensing model is more suitable for realistic environment since it determines the optimum number of sensors required for desirable coverage in fading conditions.
Efficient Energy Management and Data Recovery in Sensor Networks using Latent Variables Based Tensor Factorization
"... A key factor in a successful sensor network deployment is finding a good balance between maximizing the number of measurements taken (to maintain a good sampling rate) and minimizing the overall energy consumption (to extend the network lifetime). In this work, we present a datadriven statistical m ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
(Show Context)
A key factor in a successful sensor network deployment is finding a good balance between maximizing the number of measurements taken (to maintain a good sampling rate) and minimizing the overall energy consumption (to extend the network lifetime). In this work, we present a datadriven statistical model to optimize this tradeoff. Our approach takes advantage of the multivariate nature of the data collected by a heterogeneous sensor network to learn spatiotemporal patterns. These patterns enable us to employ an aggressive duty cycling policy on the individual sensor nodes, thereby reducing the overall energy consumption. Our experiments with the OMNeT++ network simulator using realistic wireless channel conditions, on data collected from two realworld sensor networks, show that we can sample just 20% of the data and can reconstruct the remaining 80 % of the data with less than 9 % mean error, outperforming similar techniques such is distributed compressive sampling. In addition, energy savings ranging up to 76%, depending on the sampling rate and the hardware configuration of the node.
Article Human Body Parts Tracking and Kinematic Features Assessment Based on RSSI and Inertial Sensor Measurements
, 2013
"... sensors ..."
(Show Context)
Estimating Distances via Connectivity in Wireless Sensor Networks
"... Distance estimation is vital for localization and many other applications in wireless sensor networks. In this paper, we develop a method that employs a maximumlikelihood estimator (MLE) to estimate distances between a pair of neighboring nodes in static wireless sensor networks using their local c ..."
Abstract
 Add to MetaCart
(Show Context)
Distance estimation is vital for localization and many other applications in wireless sensor networks. In this paper, we develop a method that employs a maximumlikelihood estimator (MLE) to estimate distances between a pair of neighboring nodes in static wireless sensor networks using their local connectivity information, namely the numbers of their common and noncommon onehop neighbors. We present the distance estimation method under a generic channel model, including the unit disk (communication) model and the more realistic lognormal (shadowing) model as special cases. Under the lognormal model, we numerically study the bias and standard deviation associated with our method and show that for long distances our method outperforms the method based on received signal strength (RSS); we investigate the impact of the lognormal model uncertainty; we provide a CramérRao lower bound (CRLB) analysis for the problem of estimating distances via connectivity and derive helpful guidelines for implementing our method. Finally, on applying the proposed method based on realistic measurement data and also in connectivitybased sensor localization, the superiority of the proposed method is confirmed.