Information Theory and Reliable Communication

2017 25th European Signal Processing Conference (EUSIPCO)

Rational Cooperation in the Finitely Repeated Prisoners' Dilemma

Wireless sensor networks (WSNs) are extensively adopted for remote monitoring and tracking scenarios, such as battlefield surveillance, target detection and tracking, traffic condition detection, p...

Resource-efficient and secure distributed state estimation over wireless sensor networks: a survey

Unlike traditional homogeneous single-task wireless sensor networks (WSNs), heterogeneous and multitask WSNs allow the cooperation among multiple heterogeneous devices dedicated to solving different signal processing tasks. Despite their heterogenous nature and the fact that each device may solve a different task, the devices could still benefit from a collaboration between them to achieve a superior performance. However, the design of such heterogeneous WSNs is very challenging and requires the design of scalable algorithms that maximize the performance of the devices without transmitting their raw sensor signals in an uncontrolled fashion. Towards this goal, novel techniques are needed both on the signal processing level and on the network-communication level. In this paper, we give an overview of applications in the field of heterogeneous and multitask WSNs with special focus on the signal processing aspects. Moreover, we provide a general overview of the existing algorithms for distributed node-specific estimation. Finally, we discuss the main challenges that have to be tackled for the design of heterogeneous multitask WSNs.

Heterogeneous and Multitask Wireless Sensor Networks—Algorithms, Applications, and Challenges

In this paper, information sharing over adversarial adaptive networks is considered. Defense against adversarial attacks is enforced by an attack detection mechanism that guides the diffusion strategy in the parameter estimation task and the transmission decisions of agents. The latter are taken to maximize long-term rewards, expressed in terms of estimation performance, communication cost, and illegal gains in a nonzero sum repeated game with imperfect action monitoring. In order to track agents’ behavior and stimulate cooperation, a reputation protocol is designed, along with the deployment of a set of honest nodes whose behavior is consistent with the reputation protocol. An algorithm that promotes cooperation and can achieve secure parameter estimation is described and illustrated by simulations.

Secure Information Sharing in Adversarial Adaptive Diffusion Networks

A common assumption in the social learning literature is that agents exchange information in an unselfish manner. In this work, we consider the scenario where a subset of agents aims at driving the network beliefs to the wrong hypothesis. The adversaries are unaware of the true hypothesis. However, they will "blend in" by behaving similarly to the other agents and will manipulate the likelihood functions used in the belief update process to launch inferential attacks. We will characterize the conditions under which the network is misled. Then, we will explain that it is possible for such attacks to succeed by showing that strategies exist that can be adopted by the malicious agents for this purpose. We examine both situations in which the agents have minimal or no information about the network model.

Social Learning Under Inferential Attacks

In cognitive radio networks, secondary users (SUs) can access the spectrum licensed by primary users (PUs) in an opportunistic fashion provided they cause no harmful interference to primary transmissions. Assuming that selfish and malicious but rational types of SUs are present in the network, we consider a setting where the PUs can also benefit from the cooperation with the SUs. The SUs are autonomous agents, have disparate interests and aim at maximizing their own type-dependent interests. Since misbehaving users can impede the PUs' communications by malicious or selfish actions, we develop a trust management scheme employed by the PUs that rewards cooperative SUs and punishes non-cooperative ones. We study the impact of trust on both types of misbehaving SUs' optimal decision-making process, by utilizing the Markov Decision Process framework, and we derive conditions that provably thwart malicious and selfish behavior for certain model parameters.

Using trust to mitigate malicious and selfish behavior of autonomous agents in CRNs

In this paper, we consider the problem of information sharing over adaptive networks, where a diffusion strategy is used to estimate a common parameter. We introduce a new model that takes into account the presence of both selfish and malicious intelligent agents that adjust their behavior to maximize their own benefits. The interactions among agents are modeled as a stochastic game with incomplete information and partially observable actions. To stimulate cooperation amongst selfish agents and thwart malicious behavior, a trust management system relying on a voting scheme is employed. Agents act as independent learners, using the Q-learning algorithm. The simulation results illustrate the severe impact of falsified information on estimation accuracy along with the noticeable improvements gained by stimulating cooperation and truth-telling, with the proposed trust management mechanism.

Managing trust in diffusion adaptive networks with malicious agents

In this paper we study the problem of information sharing among rational self-interested agents as a dynamic game of asymmetric information. We assume that the agents imperfectly observe a Markov chain and they are called to decide whether they will share their noisy observations or not at each time instant. We utilize the notion of conditional mutual information to evaluate the information being shared among the agents. The challenges that arise due to the inter-dependence of agents' information structure and decision-making are exhibited. For the finite horizon game we prove that agents do not have incentive to share information. In contrast, we show that cooperation can be sustained in the infinite horizon case by devising appropriate punishment strategies which are defined over the agents' beliefs on the system state. We show that these strategies are closed under the best-response mapping and that cooperation can be the optimal choice in some subsets of the state belief simplex. We characterize these equilibrium regions, prove uniqueness of a maximal equilibrium region and devise an algorithm for its approximate computation.

Konstantinos Ntemos

Papers

Secure Information Sharing in Adversarial Adaptive Diffusion Networks

Social Learning Under Inferential Attacks

Using trust to mitigate malicious and selfish behavior of autonomous agents in CRNs

Managing trust in diffusion adaptive networks with malicious agents

Dynamic Information Sharing and Punishment Strategies