The shortest-time route recommendations offered by modern navigation systems fuel selfish routing in urban vehicular traffic networks and are therefore one of the main reasons for the growth of congestion. In contrast, intelligent transportation systems (ITS) prefer to steer driver-vehicle systems (DVS) toward system-optimal route recommendations, which are primarily designed to mitigate network congestion. However, due to misalignment in motives, drivers may exhibit a lack of trust in the ITS. This paper models the interaction between a DVS and an ITS as a novel, multi-stage routing game where the DVS exhibits dynamics in its trust towards the recommendations of the ITS based on counterfactual and observed game outcomes. Specifically, the DVS and ITS are modeled as a travel-time minimizer and network congestion minimizer, respectively, each having nonidentical prior beliefs about the network state. A novel approximate algorithm to compute the Bayesian Nash equilibrium, called ROSTER (Recommendation Outcome Sampling with Trust Estimation and Re-evaluation), is proposed based on Monte Carlo sampling with trust belief updating to determine the best response route recommendations of the ITS at each stage of the game. Results of simulations between an ITS and a single additional DVS in a traffic network demonstrate that the developed algorithm is able to both mitigate network congestion and reduce driver travel times more effectively than baseline single-stage route recommendation strategies, while the error in the ITS's prediction of DVS's trust converges to zero as the number of interaction stages increases.

A Stackelberg routing platform (SRP) reduces congestion in one-shot traffic networks by proposing optimal route recommendations to the selfish travelers. Traditionally, Stackel-berg routing is cast as a partial control problem where a fraction of the traveler flow complies with route recommendations, while the remaining responds as selfish travelers. In this paper, we formulate a novel Stackelberg routing framework where the agents exhibit probabilistic compliance by accepting SRP's route recommendations with a trust probability. Specifically, we propose a greedy Trust-Aware Stackelberg Routing algorithm (in short, TASR) for SRP to compute unique path recommendations to each traveler flow with a unique demand. Simulation experiments are designed with random travel demands with diverse trust values on real road networks, such as Sioux Falls, Chicago Sketch, and Sydney networks for both single-commodity and multi-commodity flows. The performance of TASR is compared with state-of-the-art Stackelberg routing methods in terms of traffic congestion and trust dynamics over repeated interaction between the SRP and the travelers. Results show that while it may require several interactions for travelers to reach perfect trust, TASR improves network congestion in the single-commodity and multi-commodity settings when compared to the most well-known Stackelberg routing strategies.

Blockchain systems have been successful in discerning truthful information from interagent interaction amidst possible attackers or conflicts, which is crucial for the completion of nontrivial tasks in distributed networking. However, the state-of-the-art blockchain protocols are limited to resource-rich applications where reliably connected nodes within the network are equipped with significant computing power to run lottery-based Proof-of-Work (PoW) consensus. The purpose of this work is to address these challenges for implementation in a severely resource-constrained distributed network with Internet of Things (IoT) devices. The contribution of this work is a novel lightweight alternative, called weight-based reputation (WBR) scheme, to classify new transactions via modeling blockchain decisions as a distributed machine-learning task. WBR identifies network nodes that are willing to cooperate toward securing ground truth, showing robustness to adversarial subnetworks that are greater than 50% and reducing collaboration error by 50% compared to other similar schemes. This two-step approach of reputation plus transaction classification for generating blockchain data is treated as a novel method of preventing fraud and double-spending attacks in blockchain networks. To capture adversary influence, a Bayesian game is formulated and implemented to show superior performance to the state-of-the-art along with resource consumption metrics.

Selective removal of certain subgraphs called motifs based on the spread function value is one of the most powerful approaches to curb the overall influence spread in any complex network. In this paper, we first prove that any general spread function preserves both monotonicity and submodularity properties even under motif removal operations. Next, we propose a scoring mechanism as a novel spread function that quantifies the relative importance of a given motif within the overall influence spread dynamics on the complex network. We design a novel algorithm that eliminates motifs with high spread scores to curb influence spread. We evaluate the performance of our proposed spread control algorithm using simulation experiments in the context of 3-node motifs called feed forward loops (FFLs) in both real and synthetic network topologies. We demonstrate that high-scoring motifs intercept a high number of short paths from the pre-assigned source and sinks, because of which their elimination results in a significant effect on curbing the influence spread. Furthermore, we empirically evaluate the run-time and cost versus performance trade-off of the proposed algorithm.

Internet-of-Things (IoT) is a networking architecture where promising, intelligent services are designed via leveraging information from multiple heterogeneous sources of data within the network. However, the availability of such information in a timely manner requires processing and communication of raw data collected from these sources. Therefore, the economic feasibility of IoT-enabled networks relies on the efficient allocation of both computational and communication resources within the network. Since fog computing and 5G cellular networks approach this problem independently, there is a need for joint resource-provisioning of both communication and computational resources in the networks. As the solution to this problem, we propose a novel three-dimensional matching based resource provisioning algorithm that minimizes average service latency in the presence of various resource constraints, task deadlines and non-identical preferences at IoT devices, fog access points (FAPs) and small-cell access points (SAPs) in 5G networks. We prove the stability and termination of the proposed algorithm and also demonstrate that our proposed algorithm outperforms other state-of-the-art algorithms through both, simulation and real-world experiments on the laboratory test-bed.

Fog computing in 5G networks has played a significant role in increasing the number of users in a given network. However, Internet-of-Things (IoT) has driven system designers towards designing heterogeneous networks to support diverse demands (tasks with different priority values) with different latency and data rate constraints. In this paper, our goal is to maximize the total number of tasks served by a heterogeneous network, labeled task throughput, in the presence of data rate and latency constraints and device preferences regarding computational needs. Since our original problem is intractable, we propose an efficient solution based on graph-coloring techniques. We demonstrate the effectiveness of our proposed algorithm using numerical results, real-world experiments on a laboratory test-bed and comparing with the state-of-the-art algorithm.

In this paper, we study the effects of subjective biases on strategic information transmission (SIT) within a Stackelberg game setting, where a human transmitter (leader) communicates an encoded source message to a human receiver (follower) so that the receiver decodes back a desired version of the original source signal. We model human decisions using Rieger-Wang's prospect theory, which is an extension of traditional prospect theory to continuous decision spaces. Having found a closed-form expression for the receiver's best response strategy under any general setting, we consider two settings: Gaussian SIT games and exponential SIT games. While the Gaussian SIT games result in strategies that are independent of subjective biases of both the transmitter and the receiver, we show that the equilibrium strategies in exponential SIT games depend on the subjective biases of both the transmitter and the receiver. Numerical results are presented to illustrate results in both Gaussian and exponential settings.

In this letter, we model a complete-information zero-sum game between a centralized detection network with a multiple access channel between the sensors and the fusion center (FC), and a jammer with multiple transmitting antennas. We choose error probability at the FC as the performance metric, and investigate pure strategy equilibria for this game, and show that the jammer has no impact on the FC's error probability by employing pure strategies at the Nash equilibrium. Furthermore, we also show that the jammer has an impact on the expected utility if it employs mixed strategies.

In this paper, we propose a novel sealed-bid auction framework to address the problem of dynamic spectrum allocation in cognitive radio (CR) networks. We design an optimal auction mechanism that maximizes the moderator's expected utility, when the spectrum is not available with certainty. We assume that the moderator employs collaborative spectrum sensing in order to make a reliable inference about spectrum availability. Due to the presence of a collision cost whenever the moderator makes an erroneous inference, and a sensing cost at each CR, we investigate feasibility conditions that guarantee a non-negative utility at the moderator. Since the moderator fuses CRs' sensing decisions to obtain a global inference regarding spectrum availability, we propose a novel strategy-proof fusion rule that encourages the CRs to simultaneously reveal truthful sensing decisions, along with truthful valuations to the moderator. We also present tight theoretical bounds on instantaneous network throughput achieved by our auction mechanism. Numerical examples are presented to provide insights into the performance of the proposed auction under different scenarios.

Rabin's information dispersal algorithm (IDA) simultaneously addresses secrecy and fault-tolerance by encoding a data file and parsing it into unrecognizable data-packets before transmitting or storing them in a network. In this paper, we redesign Rabin's IDA for cognitive-radio networks where the routing paths are available with uncertainty. In addition, we also assume the presence of an attacker in the network which attempts to simultaneously compromise the confidentiality and data-integrity of the source message. Due to the presence of two rational entities with conflicting motives, we model the problem as a zero-sum game between the source and the attacker and investigate the mixed-strategy Nash Equilibrium by decoupling the game into two linear programs which have a primal-dual relationship.

The problem of binary hypothesis testing in a wireless sensor network is considered where observation of the sensors are quantized using identical binary quantizers and encrypted before transmission using a simple probabilistic cipher. The authorized or ally fusion center (AFC) is aware of the encryption process and the encryption parameters, whereas the unauthorized or third party fusion centers (TPFC) are unaware of the encryption parameters. The optimal threshold is evaluated for fixed values of the encryption parameters and numerical results are presented on the error probabilities of the two fusion centers. It is shown that by appropriate selection of the encryption parameters it is possible to degrade the performance of the TPFC significantly compared to that of AFC.