In wireless communications, trustworthiness computation has emerged as a crucial aspect of safeguarding modern systems against cybersecurity threats, ensuring reliable data transmission and upholding user trust. However, there is no unified definition of trustworthiness computation in the literature, and it is often presented as a specifically tailored adaptation of attack detection mechanisms. In contrast, this work introduces a general method for trustworthiness computation in wireless networks. It leverages key system characteristics, such as the channel, timing, and packet information to identify measurable Quality of Service (QoS) features with sufficient sensitivity across varying operational conditions. Building on these features, a novel three-step approach is applied. It employs changepoint detection to identify potential trustworthiness issues, calculates indicators based on the observed features, and finally combines them into a quantitative representation of trustworthiness. This systematic method effectively distinguishes between regular statistical variations in QoS features and actual trustworthiness issues. The applicability of the presented approach is demonstrated using a typical IEEE 802.11 wireless link, where different QoS features and scenarios are defined. These scenarios include network attacks, system malfunctions, and typical operational conditions. Our trustworthiness computation method correctly alerts the system to all trustworthiness issues that we challenge it with.

The constraints imposed by the computational and energy capabilities of Internet of Things (IoT) devices are challenging for ensuring secure communication between these devices. One promising solution to address these security challenges is leveraging Physical Layer Security (PHYSEC) principles. This paper investigates in the area of PHYSEC, particularly focusing on the utilization of channel reciprocity for generating a secret key, commonly referred to as Physical Layer Secret Key Generation (PSKG), in Ultra-Wideband (UWB) technology. PSKG usually contain an Information Reconciliation (IR) stage to improve the probability for identical keys, which comes with substantial computational and communication cost. The primary objective of this paper is to eliminate this IR stage and its associated costs while still achieving a high probability for identical keys. To accomplish this goal, we propose a Pre-Processing (PP) stage involving a Discrete Fourier Transform (DFT)-based approach. This approach extracts reciprocal randomness from the Channel Impulse Response (CIR). By exploiting the CIR and our proposed PP stage, we were able to slightly improve the key generation rate while reducing the probability of successful attacks by a significant 87% in dynamic environments compared to the state-of-the-art. Notably, our approach does not require a training phase, making it more efficient for IoT devices.

We present a trustworthiness score for double-sided two-way ranging on ultra-wideband devices. It serves as quantitative measure for assessing the reliability and security aspects of ranging. Leveraging the trustworthiness score, we propose two localization schemes that utilize the weighted non-linear least squares approach. The computation of the trustworthiness score relies on an autoencoder model trained on trustworthy data. To evaluate the performance of our trustworthiness score, we conduct indoor localization experiments that include channel manipulations such as line-of-sight obstructions and timestamp attacks resembling the well-known Cicada attack. The incorporation of trustworthiness led to an improvement in localization accuracy, reducing the root mean square error (RMSE) by up to 50% in the dynamic scenario.

In this paper, we show that our recently proposed method to enhance distance bounding (DB) by time difference of arrival (TDOA) measurements improves the security of current DB protocols by reducing their uncertainty area (UA). DB protocols are employed such that a verifier node can authenticate a prover node and at the same time ensure that the prover is close enough to the verifier. TDOA-Enhanced DB increases the security of existing DB protocols by the use of passively listening verifiers, that are employed additionally to the DB primary verifier. We verify our proposed method with measurements in a laboratory setup with hardware that is equipped with Qorvo DW1000 Ultra-Wideband transceivers. We also derive the Crámer-Rae Lower Bound for TDOA with different noise sources and compare it with the measurements to calculate the TDOA-Enhanced UA. By performing a TDOA-Enhanced location verification, we show that an UA reduction is possible in 78% of positions in a 20m x 20m square. This significantly reduces the probability of a successful attack at the physical layer of DB protocols.

We propose to add a monitoring system consisting of so-called path-and guard nodes to industrial wireless sensor networks (IWSNs), to increase the security level by using receive signal strength indicator (RSSI) measurements. Via these measurements, the monitoring system determines the presence of a mobile sensor node in a predefined area, which can be used to handle access rights and to increase automation capabilities in industrial applications. We add this monitoring system to an IWSN based on the EPhESOS protocol, which has a high degree of flexibility to meet industrial requirements in different applications throughout the lifetime of a sensor node while enabling energy-autonomous operation. Two practical machine learning algorithms for RSSI-based presence detection are presented, namely a support vector machine and a neural network algorithm. They are evaluated in an automotive example and tested for their robustness against malicious attacks. Additionally, a method to find the best node locations of the monitoring system is presented.

In this paper, a new method to secure non-ideal distance bounding techniques using time difference of arrival measurements is presented. Accurate and secure localization is crucial for many applications of real life wireless sensor networks, and extensive research has been dedicated to this topic in recent years. Distance bounding protocols are employed such that a verifier node can authenticate a prover node and at the same time ensure that the prover is close enough to the verifier. In order to increase the robustness of existing distance bounding protocols to attacks such as “early detect” or “late commit the use of passively listening verifiers that are employed additionally to the distance bounding primary verifier is studied. In such a way an extended set of measurements is obtained. The proposed approach is able to prevent those attacks. We study the presence of some vulnerabilities in our method and we show a way to prevent them.

The paper presents a sampling method that dramatically improves the measurement accuracy of the round trip time (RTT) measurement in digital communication systems. For state of the art methods, the accuracy of time measurements in discrete time systems and low noise scenarios is limited to the sampling period T. If the measurement is repeated m-times, the upper bound of the quantization mean square error (MSE) is ~1/m. The proposed sampling scheme reduces the upper bound of the MSE to ~1/m2. This exceptional feature is enabled by applying a prime relation between sampling rate and bit rate of the communication system.

This paper considers a passive localization scenario relying on a single transmitter, several receivers, and multiple moving targets to be located. The so-called “passive” targets equipped with RFID reflectors are capable of reflecting the signals from the transmitter to the receivers. Existing approaches assume that the transmitter and receivers are synchronous or quasi-synchronous, which is not always realistic in practical scenarios. Hence, an asynchronous wireless network is considered, where different clock offsets are assumed at different receivers. We propose a centralized expectation-maximization-based passive localization method for asynchronous receivers (EMpLaR) by treating the clock offsets as hidden variables. Thereby, the proposed algorithm makes use of Taylor expansions to arrive at a closed-form maximization. Furthermore, to improve the robustness to link failures and to reduce the energy consumption, we propose a distributed localization approach based on average consensus formulation to locate the target at each receiver. By applying a quadratic polynomial approximation of the function on which consensus has to be reached, both the computational complexity and the communications overhead are significantly reduced. The Cramér-Rao bound of the target location is derived as a benchmark of our proposed algorithms. Our simulation results show that the proposed centralized and distributed EMpLaR algorithms match the Cramér-Rao bound and significantly improve the localization performance compared with the conventional methods.

The Internet of Things (IoT) will seamlessly integrate a large number of densely deployed heterogeneous devices and will enable new location-aware services. However, fine-grained localization of IoT devices is challenging as their computation and communication resources are typically limited and different devices may have different qualities of internal clocks and different mobility patterns. To address these challenges, we propose a cooperative, scalable, and time-recursive algorithm for network localization and synchronization (NLS). Our algorithm is based on time measurements and supports heterogeneous devices with limited computation and communication resources, time-varying clock and location parameters, arbitrary state-evolution models, and time-varying network connectivity. These attributes make the proposed algorithm attractive for IoT-related applications. The algorithm is furthermore able to incorporate measurements from additional sensors for positioning, navigation, and timing such as receivers for global navigation satellite systems. Based on a factor graph representation of the underlying spatiotemporal Bayesian sequential estimation problem, the algorithm uses belief propagation (BP) for an efficient marginalization of the joint posterior distribution. To account for the nonlinear measurement model and nonlinear state-evolution models while keeping the communication and computation requirements low, we develop an efficient second-order implementation of the BP rules by means of the recently introduced sigma point belief propagation technique. Simulation results demonstrate the high synchronization and localization accuracy as well as the low computational complexity of the proposed algorithm. In particular, in sufficiently dense networks, the proposed algorithm outperforms the state-of-the-art BP-based algorithm for NLS in terms of both estimation accuracy and computational complexity.

A machine type communication (MTC) system for wireless transmission of sampled sensor data between two devices is considered. In order to reduce the latency between collecting the sample at the transmitting device and making it accessible at the receiving device, a novel cross-layer design is proposed and appropriate channel coding is discussed. The novel scheme deviates from traditional packet oriented communication in that the sampling is triggered by the physical layer of the transceiver once the packet transmission has started, instead of commanding a packet transmission at the application layer after the sample is available at the CPU. Thereby, delays from data buffering and packet generation are eliminated. Furthermore, in order to maintain guaranteed transmission latencies, appropriate channel codes are selected and their error correction performance is assessed in industrial environments.

Timestamp free clock synchronization in a master-slave network, i.e., synchronization where no timestamps are exchanged between the nodes, is considered. For highly accurate synchronization, a novel discrete-valued clock model is introduced. It is based on the observation that clocks are discrete counters in digital wireless radios. Considering this model, it is shown that the round-trip time (RTT) measurements follow specific pulse or step shaped functions. The estimated parameters of these RTT functions are used to determine the clock parameters (clock skew and phase) and the propagation delay. Numerical analysis illustrate that when RTT measurements are collected using discrete-valued clocks, the proposed estimation schemes outperform estimators derived from the continuous clock model, which is used in the state-of-the-art methods. Moreover, the presented scheme performs similar to recently presented discrete-valued clock approaches with more stringent hardware assumption. The correctness of the proposed models is validated through hardware experiments.

In this paper we consider a unique word OFDM (UW-OFDM) system with iterative detection, for which we explore two soft-input soft-output (SISO) detection algorithms: A LMMSE detector and a detector based on the generalized approximate message passing (GAMP) algorithm. For the GAMP based detector we propose additional simplifications suitable for UW-OFDM to further reduce the complexity while exhibiting only a small BER performance loss. As verified by computer simulations, both algorithms show a significant BER performance improvement over the iterations. Moreover, the GAMP based detector with the proposed simplifications outperforms the computational more complex LMMSE detector.

Clock synchronization and ranging are important topics in the field of wireless networks, where internode time measurement allows both tasks to be completed in one. Such networks are generally time-variant due to possible changes in environmental conditions and mobility of network nodes. We present a fully distributed filtering algorithm for combined clock synchronization and ranging based on message passing by belief propagation on a factor graph representation of a time-variant wireless network. The resulting message passing equations can be interpreted as a variant of Kalman filtering locally on each network node. Simulation results show that tracking estimation parameters improves estimation accuracy significantly without additional communication effort.

In this paper, we consider a bit-interleaved coded spatial multiplexing MIMO communication system over a frequency-selective MIMO channel. We present a factor-graph-based derivation of two different equalization algorithms. To this end, we propose a cycle-free factor graph representation of the equalizer, to which we apply the sum-product algorithm (SPA). By using different message representations in the SPA, it is shown that the resulting equalization algorithms correspond to the optimal MAP equalizer and the low-complexity LMMSE equalizer, respectively. Both algorithms can be used in turbo processing and we demonstrate that after 3 iterations the BER performance of the LMMSE equalizer is similar to that of the MAP equalizer.