Secret Key Generation Based on AoA Estimation for Low SNR Conditions

In the context of physical layer security, a physical layer characteristic is used as a common source of randomness to generate the secret key. Therefore an accurate estimation of this characteristic is the core for reliable secret key generation. Estimation of almost all the existing physical layer characteristic suffer dramatically at low signal to noise (SNR) levels. In this paper, we propose a novel secret key generation algorithm that is based on the estimated angle of arrival (AoA) between the two legitimate nodes. Our algorithm has an outstanding performance at very low SNR levels. Our algorithm can exploit either the Azimuth AoA to generate the secret key or both the Azimuth and Elevation angles to generate the secret key. Exploiting a second common source of randomness adds an extra degree of freedom to the performance of our algorithm. We compare the performance of our algorithm to the algorithm that uses the most commonly used characteristics of the physical layer which are channel amplitude and phase. We show that our algorithm has a very low bit mismatch rate (BMR) at very low SNR when both channel amplitude and phase based algorithm fail to achieve an acceptable BMR

Effects of Spatial Randomness on Locating a Point Source with Distributed Sensors

Most studies that consider the problem of estimating the location of a point source in wireless sensor networks assume that the source location is estimated by a set of spatially distributed sensors, whose locations are fixed. Motivated by the fact that the observation quality and performance of the localization algorithm depend on the location of the sensors, which could be randomly distributed, this paper investigates the performance of a recently proposed energy-based source-localization algorithm under the assumption that the sensors are positioned according to a uniform clustering process. Practical considerations such as the existence and size of the exclusion zones around each sensor and the source will be studied. By introducing a novel performance measure called the estimation outage, it will be shown how parameters related to the network geometry such as the distance between the source and the closest sensor to it as well as the number of sensors within a region surrounding the source affect the localization performance.Comment: 7 Pages, 5 Figures, To appear at the 2014 IEEE International Conference on Communications (ICC'14) Workshop on Advances in Network Localization and Navigation (ANLN), Invited Pape

Localization for Anchoritic Sensor Networks

We introduce a class of anchoritic sensor networks, where communications between sensor nodes is undesirable or infeasible, e.g., due to harsh environment, energy constraints, or security considerations

Doctor of Philosophy

dissertationLow-cost wireless embedded systems can make radio channel measurements for the purposes of radio localization, synchronization, and breathing monitoring. Most of those systems measure the radio channel via the received signal strength indicator (RSSI), which is widely available on inexpensive radio transceivers. However, the use of standard RSSI imposes multiple limitations on the accuracy and reliability of such systems; moreover, higher accuracy is only accessible with very high-cost systems, both in bandwidth and device costs. On the other hand, wireless devices also rely on synchronized notion of time to coordinate tasks (transmit, receive, sleep, etc.), especially in time-based localization systems. Existing solutions use multiple message exchanges to estimate time offset and clock skew, which further increases channel utilization. In this dissertation, the design of the systems that use RSSI for device-free localization, device-based localization, and breathing monitoring applications are evaluated. Next, the design and evaluation of novel wireless embedded systems are introduced to enable more fine-grained radio signal measurements to the application. I design and study the effect of increasing the resolution of RSSI beyond the typical 1 dB step size, which is the current standard, with a couple of example applications: breathing monitoring and gesture recognition. Lastly, the Stitch architecture is then proposed to allow the frequency and time synchronization of multiple nodes' clocks. The prototype platform, Chronos, implements radio frequency synchronization (RFS), which accesses complex baseband samples from a low-power low-cost narrowband radio, estimates the carrier frequency offset, and iteratively drives the difference between two nodes' main local oscillators (LO) to less than 3 parts per billion (ppb). An optimized time synchronization and ranging protocols (EffToF) is designed and implemented to achieve the same timing accuracy as the state-of-the-art but with 59% less utilization of the UWB channel. Based on this dissertation, I could foresee Stitch and RFS further improving the robustness of communications infrastructure to GPS jamming, allow exploration of applications such as distributed beamforming and MIMO, and enable new highly-synchronous wireless sensing and actuation systems

Enhanced Performance Cooperative Localization Wireless Sensor Networks Based on Received-Signal-Strength Method and ACLM

There has been a rise in research interest in wireless sensor networks (WSNs) due to the potential for his or her widespread use in many various areas like home automation, security, environmental monitoring, and lots more. Wireless sensor network (WSN) localization is a very important and fundamental problem that has received a great deal of attention from the WSN research community. Determining the relative coordinate of sensor nodes within the network adds way more aiming to sense data. The research community is extremely rich in proposals to deal with this challenge in WSN. This paper explores the varied techniques proposed to deal with the acquisition of location information in WSN. In the study of the research paper finding the performance in WSN and those techniques supported the energy consumption in mobile nodes in WSN, needed to implement the technique and localization accuracy (error rate) and discuss some open issues for future research. The thought behind Internet of things is that the interconnection of the Internet-enabled things or devices to every other and human to realize some common goals. WSN localization is a lively research area with tons of proposals in terms of algorithms and techniques. Centralized localization techniques estimate every sensor node's situation on a network from a central Base Station, finding absolute or relative coordinates (positioning) with or without a reference node, usually called the anchor (beacon) node. Our proposed method minimization error rate and finding the absolute position of nodes

Connection Between System Parameters and Localization Probability in Network of Randomly Distributed Nodes

This article deals with localization probability in a network of randomly distributed communication nodes contained in a bounded domain. A fraction of the nodes denoted as L-nodes are assumed to have localization information while the rest of the nodes denoted as NL nodes do not. The basic model assumes each node has a certain radio coverage within which it can make relative distance measurements. We model both the case radio coverage is fixed and the case radio coverage is determined by signal strength measurements in a Log-Normal Shadowing environment. We apply the probabilistic method to determine the probability of NL-node localization as a function of the coverage area to domain area ratio and the density of L-nodes. We establish analytical expressions for this probability and the transition thresholds with respect to key parameters whereby marked change in the probability behavior is observed. The theoretical results presented in the article are supported by simulations.Comment: To appear on IEEE Transactions on Wireless Communications, November 200

Target localization using RSS measurements in wireless sensor networks

The subject of this thesis is the development of localization algorithms for target localization in wireless sensor networks using received signal strength (RSS) measurements or Quantized RSS (QRSS) measurements. In chapter 3 of the thesis, target localization using RSS measurements is investigated. Many existing works on RSS localization assumes that the shadowing components are uncorrelated. However, here, shadowing is assumed to be spatially correlated. It can be shown that localization accuracy can be improved with the consideration of correlation between pairs of RSS measurements. By linearizing the corresponding Maximum Likelihood (ML) objective function, a weighted least squares (WLS) algorithm is formulated to obtain the target location. An iterative technique based on Newtons method is utilized to give a solution. Numerical simulations show that the proposed algorithms achieves better performance than existing algorithms with reasonable complexity. In chapter 4, target localization with an unknown path loss model parameter is investigated. Most published work estimates location and these parameters jointly using iterative methods with a good initialization of path loss exponent (PLE). To avoid finding an initialization, a global optimization algorithm, particle swarm optimization (PSO) is employed to optimize the ML objective function. By combining PSO with a consensus algorithm, the centralized estimation problem is extended to a distributed version so that can be implemented in distributed WSN. Although suboptimal, the distributed approach is very suitable for implementation in real sensor networks, as it is scalable, robust against changing of network topology and requires only local communication. Numerical simulations show that the accuracy of centralized PSO can attain the Cramer Rao Lower Bound (CRLB). Also, as expected, there is some degradation in performance of the distributed PSO with respect to the centralized PSO. In chapter 5, a distributed gradient algorithm for RSS based target localization using only quantized data is proposed. The ML of the Quantized RSS is derived and PSO is used to provide an initial estimate for the gradient algorithm. A practical quantization threshold designer is presented for RSS data. To derive a distributed algorithm using only the quantized signal, the local estimate at each node is also quantized. The RSS measurements and the local estimate at each sensor node are quantized in different ways. By using a quantization elimination scheme, a quantized distributed gradient method is proposed. In the distributed algorithm, the quantization noise in the local estimate is gradually eliminated with each iteration. Simulations show that the performance of the centralized algorithm can reach the CRLB. The proposed distributed algorithm using a small number of bits can achieve the performance of the distributed gradient algorithm using unquantized data