Uncertain Communication Channels Research Articles

Key Agreement Over Inter-Process Communication

Today’s computer is often infected by malwares and conventional communication channels such as inter-process communication (IPC) are attractive attack surface for attackers because important information such as user’s personal data and passwords are transmitted between processes over IPC. In addition, there is no other protection other than the access control mechanism provided by the underlying OS, but it is not always sufficient. To improve the situation, this paper proposes a key agreement protocol between processes using a network socket, which is one of the IPC methods. Our protocol provides a means for legitimate processes to cryptographically communicate over the IPC. We use an uncertain channel for secure key agreement over IPC and we found that the IPC channel behaves as the uncertain communication channel due to the process scheduling of the OS. The proposed protocol is based on random number sharing using the messages that the attacker probabilistically fails to obtain and attacker detection who interrupts the protocol. Our protocol provides secure key sharing against an attacker that interrupts the protocol and impersonates legitimate processes. We experiment on the behavior of the uncertain channel on an actual device and confirm that our protocol achieves 128-bit security in a realistic execution time within 8.5 ms. To our best knowledge, our proposal is the first countermeasure for IPC with cryptographic strength under reasonable assumptions.

Global convergence of the EM algorithm for ARX models with uncertain communication channels

An expectation maximization (EM) algorithm is presented for ARX modeling with uncertain communication channels. The considered model consists of two parts: a dynamic model which is expressed by an ARX model, and an output model, both subject to white Gaussian noises. Since the true outputs of the ARX model are assumed to be unknown, a modified Kalman filter is derived to estimate the output, and then the parameters are estimated by the EM algorithm using the estimated outputs. The Kullback–Leibler divergence and the submartingale are used to prove that the parameter estimates can converge to the true values with the EM algorithm. Furthermore, a simulation example is presented to verify the theoretical results.

Centralized filtering and smoothing algorithms from outputs with random parameter matrices transmitted through uncertain communication channels

The least-squares linear centralized estimation problem is addressed for discrete-time signals from measured outputs whose disturbances are modeled by random parameter matrices and correlated noises. These measurements, coming from different sensors, are sent to a processing center to obtain the estimators and, due to random transmission failures, some of the data packet processed for the estimation may either contain only noise (uncertain observations), be delayed (sensor delays) or even be definitely lost (packet dropouts). Different sequences of Bernoulli random variables with known probabilities are employed to describe the multiple random transmission uncertainties of the different sensors. Using the last observation that successfully arrived when a packet is lost, the optimal linear centralized fusion estimators, including filter, multi-step predictors and fixed-point smoothers, are obtained via an innovation approach; this approach is a general and useful tool to find easily implementable recursive algorithms for the optimal linear estimators under the least-squares optimality criterion. The proposed algorithms are obtained without requiring the evolution model of the signal process, but using only the first and second-order moments of the processes involved in the measurement model.

Minimum information rate for observability of linear systems with stochastic time delay

ABSTRACTThis paper investigates the state estimation problem for linear time-invariant systems where sensors and controllers are geographically separated and connected via stationary memoryless uncertain digital communication channels with the information rate limitation. Such channels introduce data packet dropout, stochastic time delay, and data packet reordering. In the TCP/IP model, the transport layer provides a reliable service on top of an unreliable communication channel. However, the performance of such systems is still affected by packet reordering and stochastic time delay. In particular, we present an optimal encoding and decoding scheme to guarantee observability by employing the minimum information rate, and derive a necessary and sufficient condition on the delay distribution and information rate for observability. Illustrative examples are given to demonstrate the effectiveness of the proposed scheme.

Robust consensus of Lur'e networks with uncertain communications

This study addresses the robust consensus problem of a network of Lur'e systems coordinating over uncertain communication channels. The authors model each communication channel of the network as an ideal transmission system with the unitary transfer function perturbed by a norm-bounded uncertainty, which is particularly susceptible to describe quantisation errors and communication noises. Based on the uncertain relative state information among neighbouring agents, a distributed consensus protocol is proposed and designed in terms of linear matrix inequalities to ensure robust consensus for both undirected connected graphs and leader-follower graphs containing a directed spanning tree.

Robust Distributed Kalman Filter for Wireless Sensor Networks with Uncertain Communication Channels

We address a state estimation problem over a large‐scale sensor network with uncertain communication channel. Consensus protocol is usually used to adapt a large‐scale sensor network. However, when certain parts of communication channels are broken down, the accuracy performance is seriously degraded. Specifically, outliers in the channel or temporal disconnection are avoided via proposed method for the practical implementation of the distributed estimation over large‐scale sensor networks. We handle this practical challenge by using adaptive channel status estimator and robust L1‐norm Kalman filter in design of the processor of the individual sensor node. Then, they are incorporated into the consensus algorithm in order to achieve the robust distributed state estimation. The robust property of the proposed algorithm enables the sensor network to selectively weight sensors of normal conditions so that the filter can be practically useful.

Robust FIR equalization of uncertain communication channels – An LMI approach

This paper proposes a new robust FIR equalizer for discrete time systems subject to parameter uncertainties in the matrices of the system state-space model from an H ∞ perspective. The uncertain parameters are supposed to belong to a given convex bounded polyhedral domain. The discrete algebraic Riccati inequality (DARI) and linear matrix inequality (LMI) provide an extremely superior methodology in the H ∞ control fields. By the H ∞ and robust methods in conjunction with the bounded real lemma, the robust FIR equalizer design is transformed into a standard LMI optimization problem. An example is given to illustrate the proposed methodology.

PERFORMANCE STUDY OF A MINIMAX ROBUST MATCHED FIELD PROCESSOR

In both conventional array processing, where wave front arrivals are assumed planar, and matched field processing, where full field propagation models are used to predict the signal field at the array, detection and localization are carried out by looking for large values of the beamformer response. This approach does not generally model the statistical uncertainty in the signal wave front arrival structure that results from fundamentally stochastic ocean environmental phenomena. The realization that, inevitably, the true environment differs from that used to generate the predicted signal field has motivated interest in robust matched field processors. In a recent paper, we showed how one may adapt the minimax solution to the uncertain communication channel equalization problem to derive robust matched field processors. In this paper we discuss some of the acoustic and computational issues associated with these robust matched field processors and describe the results obtained upon applying these processors to the Acoustic Models in Signal Processing (AMSP) Workshop test data sets.