The rise of geo-spatial location-based applications for the Internet of Things introduces new location spoofing security risks. To overcome the threat of malicious spoofing attacks, we develop the Secure Location of Things (SLOT) framework which extends current state-of-the-art methods and is able to cope with such threats. The SLOT framework incorporates another piece of information which has not been utilized so far, the audibility information. This information indicates whether a node is able/unable to communicate with the target. By leveraging on this available information, we reformulate the location estimation problem as a stochastic censoring model and derive the maximum likelihood estimator for the node’s location in two different ways: the first algorithm is based on a probabilistic mixture model and assumes knowledge of the Byzantine attack distributional model; and the second algorithm is based on a difference-time-of-arrival, which does not make any distributional assumptions regarding the attack. We show that our algorithms provide significant performance gain over current state-of-the-art algorithms, by mitigating the well-known ambiguity problem of the likelihood surface. Extensive simulations show the significant benefits that the SLOT framework provides compared to current state-of-the-art algorithms.

We develop new algorithms for Geo-Spatial location estimation for Internet of Things (IoT) networks by utilizing a One Way Time of Arrival (OW-TOA) approach. We first demonstrate the limitations of current OW-TOA location estimation algorithms for IoT networks when some of the Anchor Nodes (ANs) are outside the communication range with the source, which results in ambiguous level-sets of the likelihood function, causing inaccurate source localization. We then show that in IoT networks, in addition to using the conventional noisy ranging measurements from nodes which are in the communication range, we can make use of the audibility information (which indicates whether a node is able/unable to communicate with the target) that each node has. By leveraging on this available information, we considerably improve the localization performance, by mitigating the well known ambiguity problem which arises when a few AN are inaudible. The first algorithm we develop is the Joint source location and time offset (J-LoT) Maximum Likelihood Estimation (MLE) and the second is the Differential source location (DLoT) MLE. Our algorithms require no additional hardware and the novelty lies in using the available audibility information to resolve the likelihood ambiguity. Finally, we develop the Cram´er-Rao Bounds (CRLB) of the source location estimate for both algorithms. Extensive simulations show the significant benefits of our algorithms compared to the conventional MLE algorithms.

We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks. We consider the case where two types of sensors are deployed: the first consists of expensive, high quality sensors; and the second, of cheap low quality sensors, which are activated only if the intensity of the spatial field exceeds a pre-defined activation threshold (e.g., wind sensors). In addition, these sensors are powered by means of energy harvesting and their time varying energy status impacts on the accuracy of the measurement that may be obtained. We then address the following two important problems: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from both networks; and (ii) how to perform query based sensor set selection with predictive MSE performance guarantee. To overcome this problem, we solve the first problem by developing a low complexity algorithm based on the spatial best linear unbiased estimator (S-BLUE). Next, building on the S-BLUE, we address the second problem, and develop an efficient algorithm for query based sensor set selection with performance guarantee. Our algorithm is based on the Cross Entropy method which solves the combinatorial optimization problem in an efficient manner. We present a comprehensive study of the performance gain that can be obtained by augmenting the high-quality sensors with low-quality sensors using both synthetic and real insurance storm surge database known as the Extreme Wind Storms Catalogue.

The classification of Internet-of-Things (IoT) devices into different types is of paramount importance, from multiple perspectives, including security and privacy aspects. Recent works have explored machine learning techniques for fingerprinting (or classifying) IoT devices, with promising results. However, the existing works have assumed that the features used for building the machine learning models are readily available or can be easily extracted from the network traffic; in other words, they do not consider the costs associated with feature extraction. In this work, we take a more realistic approach, and argue that feature extraction has a cost, and the costs are different for different features. We also take a step forward from the current practice of considering the misclassification loss as a binary value, and make a case for different losses based on the misclassification performance. Thereby, and more importantly, we introduce the notion of risk for IoT device classification. We define and formulate the problem of cost-aware IoT device classification. This being a combinatorial optimization problem, we develop a novel algorithm to solve it in a fast and effective way using the cross-entropy (CE)-based stochastic optimization technique. Using traffic of real devices, we demonstrate the capability of the CE-based algorithm in selecting features with minimal risk of misclassification while keeping the cost for feature extraction within a specified limit.

We develop a new class of event detection algorithms in Wireless Sensor Networks where the sensors are randomly deployed spatially. We formulate the detection problem as a binary hypothesis testing problem and design the optimal decision rules for two scenarios, namely the Poisson Point Process and Binomial Point Process random deployments. To calculate the intractable marginal likelihood density, we develop three types of series expansion methods which are based on an Askey-orthogonal polynomials. In addition, we develop a novel framework to provide guidance on which series expansion is most suitable (i.e., most accurate) to use for different system parameters. Extensive Monte Carlo simulations are carried out to illustrate the benefits of this framework as well as the quality of the series expansion methods, and the impacts that different parameters have on detection performance via the Receiver Operating Curves (ROC).

Privacy-preserving routing protocols in wireless networks frequently utilize additional artificial traffic to hide the identities of communicating source-destination pairs. Usually, the addition of artificial traffic is done heuristically with no guarantees that the transmission cost, latency, and so on, are optimized in every network topology. In this paper, we explicitly examine the privacy-utility tradeoff problem for wireless networks and develop a novel privacy-preserving routing algorithm called optimal privacy enhancing routing algorithm (OPERA). OPERA uses a statistical decision-making framework to optimize the privacy of the routing protocol given a utility (or cost) constraint. We consider global adversaries with both lossless and lossy observations that use the Bayesian maximum-a-posteriori (MAP) estimation strategy. We formulate the privacy-utility tradeoff problem as a linear program, which can be efficiently solved. Our simulation results demonstrate that OPERA reduces the adversary's detection probability by up to 50% compared to the random Uniform and Greedy heuristics, and up to five times compared to a baseline scheme. In addition, OPERA also outperforms the conventional information-theoretic mutual information approach.