Shahrokh Valaee

Received-Signal-Strength-Based Indoor Positioning Using Compressive Sensing

The recent growing interest for indoor Location-Based Services (LBSs) has created a need for more accurate and real-time indoor positioning solutions. The sparse nature of location finding makes the theory of Compressive Sensing (CS) desirable for accurate indoor positioning using Received Signal Strength (RSS) from Wireless Local Area Network (WLAN) Access Points (APs). We propose an accurate RSS-based indoor positioning system using the theory of compressive sensing, which is a method to recover sparse signals from a small number of noisy measurements by solving an `1-minimization problem. Our location estimator consists of a coarse localizer, where the RSS is compared to a number of clusters to detect in which cluster the node is located, followed by a fine localization step, using the theory of compressive sensing, to further refine the location estimation. We have investigated different coarse localization schemes and AP selection approaches to increase the accuracy. We also show that the CS theory can be used to reconstruct the RSS radio map from measurements at only a small number of fingerprints, reducing the number of measurements significantly. We have implemented the proposed system on a WiFi-integrated mobile device and have evaluated the performance. Experimental results indicate that the proposed system leads to substantial improvement on localization accuracy and complexity over the widely used traditional fingerprinting methods.

Parametric localization of distributed sources

Most array processing algorithms are based on the assumption that the signals are generated by point sources. This is a mathematical constraint that is not satisfied in many applications. In this paper, we consider situations where the sources are distributed in space with a parametric angular cross-correlation kernel. We propose an algorithm that estimates the parameters of this model using a generalization of the MUSIC algorithm. The method involves maximizing a cost function that depends on a matrix array manifold and the noise eigenvectors. We study two particular cases: coherent and incoherent spatial source distributions. The spatial correlation function for a uniformly distributed signal is derived. From this, we find the array gain and show that (in contrast to point sources) it does not increase linearly with the number of sources. We compare our method to the conventional (point source) MUSIC algorithm. The simulation studies show that the new method outperforms the MUSIC algorithm by reducing the estimation bias and the standard deviation for scenarios with distributed sources. It is also shown that the threshold signal-to-noise ratio required for resolving two closely spaced distributed sources is considerably smaller for the new method. >

Wideband array processing using a two-sided correlation transformation

A new method for broadband array processing is proposed. The method is based on unitary transformation of the signal subspaces. We apply a two-sided transformation on the correlation matrices of the array. It is shown that the two-sided correlation transformation (TCT) has a smaller subspace fitting error than the coherent signal-subspace method (CSM). It is also shown that unlike CSM, the TCT algorithm can generate unbiased estimates of the directions-of-arrival, regardless of the bandwidth of the signals. The capability of the TCT and CSM methods for resolving two closely spaced sources is compared. The resolution threshold for the new technique is much smaller than that for CSM. >

Vehicular Node Localization Using Received-Signal-Strength Indicator

Vehicle-to-vehicle communications via dedicated-short-range-communication (DSRC) devices will enable safety applications such as cooperative collision warning. These devices use the IEEE 802.11p standard to support low-latency vehicle-to-vehicle and vehicle-to-infrastructure communications. However, a major challenge for the cooperative collision warning is to accurately determine the location of vehicles. In this paper, we present a novel cooperative-vehicle-position-estimation algorithm which can achieve a higher accuracy and more reliability than the existing global-positioning-system-based positioning solutions by making use of intervehicle-distance measurements taken by a radio-ranging technique. Our algorithm uses signal-strength-based intervehicle-distance measurements, vehicle kinematics, and road maps to estimate the relative positions of vehicles in a cluster. We have analyzed our algorithm by examining its performance-bound, computational-complexity, and communication-overhead requirements. In addition, we have shown that the accuracy of our algorithm is superior to previously proposed localization algorithms.

Reliable Broadcast of Safety Messages in Vehicular Ad Hoc Networks

Broadcast communications is critically important in vehicular networks. Many safety applications need safety warning messages to be broadcast to all vehicles present in an area. Design of a medium access control (MAC) protocol for vehicular networks is an interesting problem because of challenges posed by broadcast traffic, high mobility, high reliability and low delay requirements of these networks. In this article, we propose a topology-transparent broadcast protocol and present a detailed mathematical analysis for obtaining the probability of success and the average delay. We show, by analysis and simulations, that the proposed protocol outperforms two existing protocols for vehicular networks with topology-transparent properties and provides reliable broadcast communications for delivering safety messages under load conditions deemed to be common in vehicular environments.

Distributed source localization using ESPRIT algorithm

A new algorithm based on ESPRIT is proposed for the estimation of the central angle and angular extension of distributed sources. The central angles are estimated using TLS-ESPRIT for both incoherently distributed (ID) and coherently distributed (CD) sources. For CD sources, the extension width is estimated by constructing a one-dimensional (1-D) distributed source parameter estimator (DSPE) spectrum for each source. For ID sources, the extension widths are estimated using the central moments of the distribution. The algorithm can be used for sources with different angular distributions.

Delay aware link scheduling for multi-hop TDMA wireless networks

Time division multiple access (TDMA) based medium access control (MAC) protocols can provide QoS with guaranteed access to the wireless channel. However, in multi-hop wireless networks, these protocols may introduce scheduling delay if, on the same path, an outbound link on a router is scheduled to transmit before an inbound link on that router. The total scheduling delay can be quite large since it accumulates at every hop on a path. This paper presents a method that finds conflict-free TDMA schedules with minimum scheduling delay. We show that the scheduling delay can be interpreted as a cost, in terms of transmission order of the links, collected over a cycle in the conflict graph. We use this observation to formulate an optimization, which finds a transmission order with the min-max delay across a set of multiple paths. The min-max delay optimization is NP-complete since the transmission order of links is a vector of binary integer variables. We devise an algorithm that finds the transmission order with the minimum delay on overlay tree topologies and use it with a modified Bellman-Ford algorithm, to find minimum delay schedules in polynomial time. The simulation results in 802.16 mesh networks confirm that the proposed algorithm can find effective min-max delay schedules.

Mobility-Based Clustering in VANETs Using Affinity Propagation

The recent research in cluster-based MAC and routing schemes for Vehicle Ad Hoc Networks (VANETs) motivates the necessity for a stable VANET clustering algorithm. Due to the highly mobile nature of VANETs, mobility must play an integral role in cluster formation. We present a novel, mobility-based clustering scheme for Vehicle Ad hoc Networks, which utilizes the Affinity Propagation algorithm in a distributed manner. The proposed algorithm considers typical vehicular mobility during cluster formation, which produces clusters with high stability. Simulation results confirm the superior performance of the proposed algorithm, when compared to other accepted mobility-based clustering techniques. Clustering performance is measured in terms of average cluster head duration, average cluster member duration, average rate of cluster head change, and average number of clusters.

Link Scheduling for Minimum Delay in Spatial Re-Use TDMA

Time division multiple access (TDMA) based medium access control (MAC) protocols provide QoS with guaranteed access to wireless channel. However, in multihop wireless networks, these protocols may introduce delay when packets are forwarded from an inbound link to an outbound link on a node. Delay occurs if the outbound link is scheduled to transmit before the inbound link. The total round trip delay can be quite large since it accumulates at every hop in the path. This paper presents a method that finds schedules with minimum round trip scheduling delay. We show that the scheduling delay can be interpreted as a cost collected over a cycle on the conflict graph. We use this observation to formulate a min-max program for the delay across a set of multiple paths. The min-max delay program is NP-complete since the transmission order of links is a vector of binary integer variables. We design heuristics to select appropriate transmission orders. Once the transmission orders are known, a modified Bellman-Ford algorithm is used to find the schedules. The simulation results confirm that the proposed algorithm can find effective min-max delay schedules.

On Minimizing Broadcast Completion Delay for Instantly Decodable Network Coding

In this paper, we consider the problem of minimizing the mean completion delay in wireless broadcast for instantly decodable network coding. We first formulate the problem as a stochastic shortest path (SSP) problem. Although finding the packet selection policy using SSP is intractable, we use this formulation to draw the theoretical properties of efficient selection algorithms. Based on these properties, we propose a simple online selection algorithm that efficiently minimizes the mean completion delay of a frame of broadcast packets, compared to the random and greedy selection algorithms with a similar computational complexity. Simulation results show that our proposed algorithm indeed outperforms these random and greedy selection algorithms.