Multi-view 3D reconstruction applications are revolutionizing critical use cases that require rapid situational-awareness, such as emergency response, tactical scenarios, and public safety. In many cases, their near-real-time latency requirements and ad-hoc needs for compute resources necessitate adoption of ‘Just-in-time’ edge environments where the system is set up on the fly to support the applications during the mission lifetime. However, reliability issues can arise from the inherent dynamism and operational adversities of such edge environments, resulting in spatiotemporally correlated disruptions that impact the camera operations, which can lead to sustained degradation of reconstruction quality. In this paper, we propose a novel portfolio theory inspired edge resource management strategy for reliable multi-view 3D reconstruction against possible system disruptions. Our proposed methodology can guarantee reconstruction quality satisfaction even when the cameras are prone to spatiotemporally correlated disruptions. The portfolio theoretic optimization problem is solved using a genetic algorithm that converges quickly for realistic system settings. Using publicly available and customized 3D datasets, we demonstrate the proposed camera selection strategy’s benefits in guaranteeing reliable 3D reconstruction against traditional baseline strategies, under spatiotemporal disruptions.

In this paper, we present a novel hypothesis testing framework to model and analyze an attacker’s decision-making during the reconnaissance phase, and subsequently leverage the framework to characterize optimal deception strategies that can be used to defeat the attacker’s reconnaissance efforts. Our developed model and analytical approaches are well capable of diligently addressing the information-centric nature of the involved attack-defense processes while adapting with the defender’s and attacker’s cognitive biases (irrationalities). Employing the developed hypothesis testing framework, we first characterize a cognitively biased attacker’s optimal prospect theoretic decision rule that enables it to best exploit the information that it acquires during reconnaissance. Leveraging such understanding, we design optimal information falsification strategies that can be employed by a cognitively biased defender to strategically deceive the attacker during its reconnaissance phase under varied considerations. Several numerical results have been presented that provide important insights into the developed strategies.

Ad-hoc edge deployments to support real-time complex video processing applications such as, multi-view 3D reconstruction often suffer from spatio-temporal system disruptions that greatly impact reconstruction quality. In this poster paper, we present a novel portfolio theory-inspired edge resource management strategy to ensure reliable multi-view 3D reconstruction by accounting for possible system disruptions.

This paper presents a novel cyber deception technique that can employ fake nodes to deceive attackers. To devise such a technique, the paper uses game theory to model strategic interactions between a defender and an attacker, and prospect theory to model their possible cognitive biases, while diligently accounting for cost factors associated with attack-defense strategies. Nash Equilibrium (NE)-based strategies for deploying our devised deception tactics when the defender and the attacker are fully rational in nature as well as when they may exhibit behavioral irrationalities have been analytically characterized in the paper. Among others, our results delineate how vulnerabilities of conventional defense resources can influence adoption of deception-based defenses. Numerous simulation results have been presented that provide important insights into our developed deception-based defense strategies.

This paper considers the problem of distributed detection in the presence of a Sybil attack where a malicious sensor node can send multiple falsified decisions using multiple fake identities to a Fusion Center (FC) to degrade its decision-making performance. We study the problem under the Neyman-Pearson (NP) setup. We find that, due to the Sybil attack, the decisions received at the FC become correlated and that the degree of correlation is dependent on the number of fake identities used. The paper characterizes the optimal Sybil attack that blinds the FC, i.e., makes the FC incapable of making an informed decision. We find that if the sum of the local detection and false alarm probabilities of the sensor nodes is 1, the FC can be made blind when at least 50% of the decisions are sent using fake identities. However, if this condition is not met, then all decisions would have to be sent using fake identities in order to blind the FC. The paper also investigates strategic interactions between the FC and the Sybil attacker using Game Theory and proves the existence of a Nash Equilibrium (NE). Numerical results are presented to gain important insights.

While there has been extensive work on modeling of human decision-making both for individuals and groups from a cognitive psychology point of view, research on this topic from a signal processing and information fusion perspective is relatively recent. In this work, we consider a distributed detection problem consisting of a number of human local decision makers and a fusion center (FC). Signal detection theory is exploited to answer why promoting heterogeneity could improve the performance of collaborative human decision-making. We consider the following two scenarios: 1) the local decision makers are independent and the level of heterogeneity is measured in terms of the variability of human expertise and 2) humans make correlated local decisions due to their perceptual and behavioral similarities and heterogeneity is measured by the amount of correlation. In both cases, we show that the detection performance of the FC can be improved with the increase of heterogeneity. In particular, in the second scenario, we develop a portfolio theory-based framework to select participants from correlated human agents so that heterogeneity is enhanced resulting in improved decision-making performance. Simulations are provided for illustration and performance comparison.

This paper considers the problem of mitigating jamming attacks by aiming to deceive the jammer. Specifically, in the presence of a jammer, to defend a transmitter-receiver pair sending (real) information, the paper proposes the novel technique of sending fake information over a second transmitter-receiver pair in order to deceive the jammer into investing some of its jamming power budget for jamming the channel carrying fake information. The paper develops a leader-follower model where the jammer (acting as the follower) adopts it's jamming strategy after sensing the communication activities on the channels carrying the real and fake information, while the system (acting as the leader) adopts its power allocation strategy prior to the jammer. The paper characterizes the optimal power allocation strategy of the system considering the jammer to be non-strategic in nature, as well as characterizes the Subgame Perfect Nash Equilibrium (SPNE) strategy of the leader-follower game considering both the system and the jammer to be strategic entities. Extensive simulation results are provided to gain insights into the deception strategies developed in the paper.

This paper aims to characterize deception based attack-defense strategies when a system and an attacker is behavioral in nature under strategic considerations. The paper uses Prospect Theory to model the behavioral nature of the system and the attacker, and Game Theory to model the strategic interactions between them. The paper first considers the availability of multiple computing devices that can be used to deceive a behavioral attacker into attacking a device being unused by a behavioral system and characterizes the Nash Equilibrium (NE) based attack-defense strategy in such a scenario under cost considerations. The paper then considers the problem of protecting a target device that is accessible via a tree network and proposes the idea of deceptive routing to mislead a behavioral attacker towards a fake target present in the network. The NE based attack-defense strategies in the context of the proposed deceptive routing technique is also characterized. Numerical results provide insights into the strategic deception techniques presented in this paper.

This paper considers the design of an auction mechanism for target tracking via crowdsensing. We consider that the crowdsourcing framework consists of a set of sensors, which are embedded in devices belonging to crowd participants, and a fusion center (FC) that uses the quantized measurements from the sensors to track a target. The auction mechanism we develop addresses participatory concerns of the sensors that arise due to energy consumption associated with sensor participation while maximizing the utility of the FC to achieve desired sensing objectives and preventing market manipulations. Moreover, since a crowdsensing environment is typically resource-constrained, in our auction model, we consider that the sensors in the network quantize their private value estimates regarding their energy costs prior to communicating them to the FC. Furthermore, the paper also proposes the concept of selecting a subset of sensors (bidders) to bid (from a set of available sensors) to satisfy resource constraints during the bidding process. Extensive numerical results are provided to gain insights into the proposed mechanism.

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.

This letter considers the design of an auction mechanism to sell the object of a seller when the buyers quantize their private value estimates regarding the object into binary values prior to communicating them to the seller. The designed auction mechanism maximizes the utility of the seller (i.e., the auction is optimal), prevents buyers from communicating falsified quantized bids (i.e., the auction is incentive compatible), and ensures that buyers will participate in the auction (i.e., the auction is individually rational). The letter also investigates the design of the optimal quantization thresholds using which buyers quantize their private value estimates. Numerical results provide insights regarding the influence of the quantization thresholds on the auction mechanism.

In this paper, distributed competitive interactions between a secondary user (SU) transmitter-receiver pair and a jammer are investigated using a game-theoretic framework under physical interference restrictions, power budget constraints, and incomplete knowledge of channel gains. In this game, the SU transmitter is expected to choose its power strategy with the objective of satisfying a minimum signal-to-interference plus noise ratio (SINR) at the corresponding receiver. Similarly, the jammer's objective is to strategically allocate its power so that the SINR constraint of the SU is not satisfied. Due to a lack of complete information, this strategic power allocation problem between the two players is modeled as a Bayesian game for which the self-enforcing strategies of the SU transmitter-receiver pair and the jammer are analyzed. Furthermore, probability distributions are employed by the corresponding players to model the incomplete nature of the game. The solution of the game corresponds to Nash equilibria points. Equilibrium analysis is carried out by considering the mixed strategy solution space and numerical examples are presented for illustration.

In this letter, we consider the problem of distributed Bayesian detection in the presence of Byzantine data. The problem of distributed detection is formulated as a binary hypothesis test at the fusion center (FC) based on 1-bit data sent by the sensors. Adopting Chernoff information as our performance metric, we study the detection performance of the system under Byzantine attack in the asymptotic regime. The expression for minimum attacking power required by the Byzantines to blind the FC is obtained. More specifically, we show that above a certain fraction of Byzantine attackers in the network, the detection scheme becomes completely incapable of utilizing the sensor data for detection. When the fraction of Byzantines is not sufficient to blind the FC, we also provide closed form expressions for the optimal attacking strategies for the Byzantines that most degrade the detection performance.

This paper considers the problem of performance analysis of data fusion schemes in the presence of Byzantine attacks. First, we analyze the security performance of data fusion schemes with Byzantines. We show that when more than a certain fraction of Byzantines are present in the network, the raw data fusion schemes become completely incapable (blind). More specifically, we obtain a closed form expression for the lower bound on the fraction of Byzantines needed to blind the fusion center as a function of attacker's strength. Next, we investigate the global detection performance in the presence of Byzantine attacks, and analytically characterize the effect of Byzantines on detection performance. Numerical results provide insights into our analysis.

This paper considers the problem of optimal distributed detection with independent identical sensors in the presence of Byzantine attacks. By considering the attacker to be strategic in nature, we address the issue of designing the optimal fusion rule and the local sensor thresholds that minimize the probability of error at the fusion center (FC).We first consider the problem of finding the optimal fusion rule under the constraint of fixed local sensor thresholds and fixed Byzantine strategy. Next, we consider the problem of joint optimization of the fusion rule and local sensor thresholds for a fixed Byzantine strategy. Then we extend these results to the scenario where both the FC and the Byzantine attacker act in a strategic manner to optimize their own utilities. We model the strategic behavior of the FC and the attacker using game theory and show the existence of Nash Equilibrium. We also provide numerical results to gain insights into the solution.

In this paper, we address the problem of incentive based routing in Dynamic Spectrum Access (DSA) networks, where each secondary node incurs a cost for routing traffic from a flow and also has a certain capacity that the node can provide to the flow. We propose a path auction scheme in which each secondary node announces its cost and capacity to the routing mechanism, both of which are considered as private information known only to the node. We design a route selection mechanism and a pricing function that can induce nodes to reveal their cost and capacity honestly (making our auction truthful), while minimizing the expected payment that needs to be given to the nodes that relay traffic (making our auction optimal). By considering capacity constraint of the nodes, we explicitly support multiple path routing. For deploying our path auction based routing mechanism in DSA networks, we provide polynomial time algorithms.

In this paper, we address the problem of Optimal Content Delivery (OCD) in Dynamic Spectrum Access (DSA) networks, where the source of a flow sends data traffic to the destination in exchange for some monetary benefit, such as a subscription fee. Also, each intermediate secondary node incurs a cost for routing traffic. We propose a path auction based content delivery mechanism in which each secondary node announces its cost (considered as private information) to the auction mechanism. Based on the announced costs, the optimal flow rate between the end nodes is determined, a multi-path route is chosen and payments are made to the nodes that route traffic, such that the profit of the source node is maximized in sending data to the destination. Furthermore, the auction mechanism is strategy-proof, i.e., it can induce the intermediate nodes to truthfully declare their costs. We provide polynomial time algorithms for implementing our auction based content delivery mechanism in DSA networks.

In this paper, we address the problem of incentive based routing in Dynamic Spectrum Access (DSA) networks, where each secondary node incurs a cost for routing traffic from a flow and also has a certain capacity that it can provide to the flow. We propose a path auction based routing mechanism in which each secondary node announces its privately known cost and capacity, based on which a route is chosen and payments are made to the nodes. We design the route selection mechanism and the pricing function in a manner that can induce nodes to reveal their cost and capacity honestly, while minimizing the payment that needs to be given to the nodes that relay traffic, thereby making our path auction optimal. In our proposed mechanism, the optimal route over which traffic should be routed and the payment that each node should receive can be computed in polynomial time. Simulation results suggest that our mechanism can ensure truthful reporting of both cost and capacity while making a payment less than that required for VCG based least cost path routing.

In this paper, we address the problem of dynamic channel access by a set of cognitive radio enabled nodes (e.g., broadcast access points), where each node acting in a selfish manner tries to access and use as many channels as possible, subject to the interference constraints. We model the dynamic channel access problem as a modified Rubinstein-Ståhl bargaining game. In our model, each node (player) negotiates with the other nodes to obtain an agreeable sharing rule of the available channels, such that, no two interfering nodes use the same channel. We solve the bargaining game by finding Subgame Perfect Nash Equilibrium (SPNE) strategies of the game. First, we consider finite horizon version of the bargaining game and investigate its SPNE strategies. We then extend these results to the infinite horizon bargaining game. Furthermore, we identify Pareto optimal equilibria of the game for improving spectrum utilization. The bargaining solution ensures that no node is starved of channels. We also conduct extensive simulations to study how the “self-gain” maximizing strategy of the players impact system wide performance.

In this paper, we model the problem of dynamic spectrum access by a set of cognitive radio enabled nodes as an infinite horizon bargaining game. Each node (or, player) negotiates with its interfering nodes to obtain an agreeable sharing rule of the available channels. We investigate the subgame perfect equilibrium strategies of the bargaining game, abiding by which, each node can maximize its throughput against all its interfering nodes (opponents). We further study the issue of fairness in this model, in terms of the relative number of channels allocated to the nodes. Moreover, each node bargains with its interfering neighbors independently and in a distributed manner, making the system scalable.

In this paper, we use game theory to mitigate self-interference among cognitive radio based IEEE 802.22 networks such that these networks can efficiently co-exist. When a network experiences interference, it can adopt either one of two choices-switch to a new band hoping to find a non-interfering one, or stay with its current band hoping that the interfering network(s) will move away to a new band. We model the spectrum band switching process as an infinite horizon repeated game where the aim of each network (player) is to find a channel void of interference from it's neighboring networks incurring minimal cost. We investigate both pure and mixed strategy solution space of the game and show that pure strategy solution of the game is infeasible. Thus a mixed strategy is proposed which achieves subgame-perfect Nash Equilibrium. Simulation results reveal that using the proposed strategies, each network can find a clear channel quickly and at the same time incur a low cost.

In this paper, we use game theory to mitigate selfinterference among cognitive radio based IEEE 802.22 networks such that these networks can efficiently co-exist. When a network experiences interference, it can adopt either one of two choices- switch to a new band hoping to find a non-interfering one, or stay with its current band hoping that the interfering network(s) will move away to a new band. We model the spectrum band switching process as an infinite horizon repeated game where the aim of each network (player) is to find a channel void of interference from it's neighboring networks incurring minimal cost. We investigate both pure and mixed strategy solution space of the game and show that pure strategy solution of the game is infeasible. Thus a mixed strategy is proposed which achieves subgame-perfect Nash equilibrium. Until now we have investigated the case where each network is within the interference range of all other networks. We seek to generalize it to the case where networks can have an arbitrary number of potential interferers. Simulation results reveal that using the proposed strategies, each network can find a clear channel quickly and at the same time incur a low cost.

The cognitive radio based IEEE 802.22 wireless regional area network (WRAN) is designed to operate in the under-utilized TV bands by detecting and avoiding primary TV transmission bands in a timely manner. Such networks, deployed by competing wireless service providers, would have to self-coexist by accessing different parts of the available spectrum in a distributed manner. Obviously, the goal of every network is to acquire a clear spectrum chunk free of interference from other IEEE 802.22 networks so as to satisfy the QoS of the services delivered to the end-users. In this paper, we study the distributed WRAN self-coexistence problem from a minority game theoretic perspective. We model the spectrum band switching game where the networks try to minimize their cost in finding a clear band. We propose a mixed strategy that the competing networks must adhere to in order to achieve the Nash equilibrium. Simulation experiments have also been conducted and results corroborate with the theoretical analysis.

The IEEE 802.22 standard for wireless regional area network is the first standard for cognitive radio that tries to harness the idle or under-utilized spectrum allocated for TV bands. Two major challenges that are faced by IEEE 802.22 are (i) the issue of self co-existence and (ii) the hidden incumbent problem. In this paper, we discuss these two challenges and provide enhancements to the existing IEEE 802.22 air-interface. We use a graph theoretic technique and propose utility graph coloring for allocating spectrum to different IEEE 802.22 base stations so that they can co-exist. The allocation is done such that objectives such as throughput maximization, proportional fairness, and complete fairness are met. We also propose the use of dynamic multiple broadcast messages that resolves the contention among various consumer premise equipments and alleviates the hidden incumbent problem. Through simulation results, we show that the proposed techniques increase the system spectrum utilization and reduce connection set-up delay.