Based on the impressive features that network coding and compressed sensing paradigms have separately brought, the idea of bringing them together in practice will result in major improvements and influence in the upcoming 5G networks. In this context, this paper aims to evaluate the effectiveness of these key techniques in a cluster-based wireless sensor network, in the presence of temporal and spatial correlations. Our goal is to achieve better compression gains by scaling down the total payload carried by applying temporal compression as well as reducing the total number of transmissions in the network using spatial compression and real field network coding. Furthermore, we compare our approach with benchmark schemes. As expected, our numerical results run on NS3 simulator show that overall our scheme dramatically drops the number of transmitted packets in the considered cluster topology by almost 94 % with a very high reconstruction SNR.

Strategic users in a wireless network cannot be assumed to follow the network algorithms blindly. Moreover, some of these users aim to use their knowledge about network algorithms to maliciously gain more resources and also to create interference to other users. We consider a scenario, in which the network and legitimate users gather probabilistic information about the presence of malicious users by observing the network over a long time period. The network (mechanism designer) and legitimate users modify their actions according to this Bayesian information. We consider Bayesian mechanisms, both pricing schemes and auctions, and obtain the Bayesian Nash Equilibrium (BNE) points. The BNE points provide conditions under which, the uncertainty about user's nature (type) is better for regular (legitimate) users. To derive these conditions, we compare the Bayesian case to the complete information case. We obtain the optimal prices and allocations, which counter the malicious users. We also provide detection methods based on machine learning algorithms for the detection of malicious users, by observing the prices and rate allocations. In addition, we provide detection using regression learning by observing the anomalies in the utility functions of malicious users from prices, which is implemented along with the pricing mechanism itself. For the designer and the regular users, in a complementary fashion, the results of the detections provide a better estimate of the statistics of malicious users to implement the pricing mechanisms. We have also proposed a truthful Bayesian mechanism in the presence of malicious users. The numerical studies for malicious user detection are carried out with the model proposed in the paper as well as using real Botnet dataset.

When there are strategic and malicious users in a wireless network, the resource allocation is complicated due to the information limitation about the nature of users and network parameters. Bayesian games are appropriate tools to analyze the network resource allocation with heterogeneous users. We consider a scenario with arbitrary number of malicious users in the network, in which individual users gather probabilistic information about the density of malicious users. Users and the base station observe the network over a long time period and modify their actions accordingly. The power allocation in wireless networks which we consider in this paper, is subject to Quality of Service (QoS) requirements. We consider Bayesian pricing mechanisms where the prices are modified using the Bayesian information about types of the users to satisfy the QoS requirements. We also give detection methods based on regression learning algorithms which are used for forming the probability of a user being malicious. The utilities of the users are formed by observing the power strategies of the users and the anomalies are detected. We obtain numerically, the Bayesian Nash Equilibrium (BNE) points of the Bayesian games. We also evaluate the effect of incomplete information on the satisfaction of the QoS requirements of the users in the mechanisms. These mechanisms are with prices which were originally developed for networks with complete information.

Strategic users in a wireless network cannot be assumed to follow the network algorithms blindly. Moreover, some of these users could be controlled by powerful Botnets, which aim to use their knowledge about network algorithms to maliciously gain more resources and also to create interference to other users. We consider a scenario; in which a mechanism designer and legitimate users together, in a wireless network, gather probabilistic information about the presence of malicious users and modify their actions accordingly. The probabilistic information is gathered by observing the network over a long time period. We study Bayesian mechanisms, both pricing schemes and auctions, and obtain the Nash Equilibrium (NE) points of the underlying Bayesian games. The NE points provide conditions indicating when it is better for users to to hide or reveal their nature (types). The prices and allocations in the mechanisms are later modified using the Bayesian information about the type of the users. The numerical studies show the NE points and illustrate the results.

In this paper, we consider a multihop wireless network, where Femto Base Stations (FBSs) act as relay nodes, and are incentivized to carry traffic from a Macro Base Station (MBS) to Macro Users (MUs).We first examine the the global problem of jointly optimal allocation of traffic flow and transmission power in the multihop wireless network. We then examine a game in which selfish and strategic relays submit charging functions to the source and choose transmission powers over a MAC channel from the relays to the user. Relay charging functions are considered which yield efficient allocation at the Nash Equilibrium (NE) of the game. We observe that for efficiency, relays should be taxed for the interference it creates to other relays. We also observe that inefficient equilibria occur when the charging function is a function only of the traffic flow rate through the relay. Numerical studies demonstrate the variation of inefficiency with network structure.

We develop the noncooperative game with individual pricing for the general multiple access channel (MAC) system without successive interference cancellation (SIC). Each user allocates its own power by optimizing the individual utility function with clever price adaptation. We show that by the proposed prices, the best response (BR) power allocation of each user converges rapidly. The individual prices are proposed such that the Shannon rate-based quality-of-service (QoS) requirement of each user is achieved at the unique Nash equilibrium (NE) point. We analyse different behavior types of the users, especially the malicious behavior and the resulting NE power allocation and achievable rates of all the users with malicious users. We illustrate the convergence of the BR dynamic and the Price of Malice (PoM) by numerical simulations.

Network mechanism design aims to achieve system level goals such as efficiency or social welfare maximization through resource allocation mechanisms on networks, where individual users are selfish and independent decision-makers. This paper focuses on mechanisms for energy efficiency in uplink of multi-carrier wireless systems and rate control in wireless networks. The problem is modeled as strategic (nonco-operative) game with a resource pricing scheme, where the prices are imposed by a mechanism designer. The users decide on their actions according to both own preferences and given prices. The rules and incentives of the mechanism are designed in such a way that the system objective which is a combination of social welfare and energy efficiency is maximized. A weighting parameter in the objective function allows to smoothly vary the emphasis from social welfare to energy efficiency according to the preference of the designer. The users are assumed initially to be concerned only about their throughput. However, the designer modifies their incentives using pricing so that they become more energy-aware. A distributed optimization framework is developed accordingly in which users have general concave utilities (in terms of throughput) that are unknown to the designer and the energy-efficiency objective is expressed as a convex function of user power levels. An iterative pricing mechanism is obtained as a result, and illustrated through simulations.

In mobile commerce, a company provides location based services to a set of mobile users. The users report to the company their location with a level of granularity to maintain a degree of anonymity, depending on their perceived risk, and receive in return monetary benefits or better services from the company. This paper formulates a quantitative model in which information theoretic metrics such as entropy, quantify the anonymity level of the users. The individual perceived risks of users and the benefits they obtain are considered to be linear functions of their chosen location information granularity. The interaction between the mobile commerce company and its users are investigated using mechanism design techniques as a privacy game. The user best responses and optimal strategies for the company are derived under budgetary constraints on incentives, which are provided to users in order to convince them to share their private information at the desired level of granularity.

We address the problem of allocating a single divisible good to a number of agents. The agents have concave valuation functions parameterized by a scalar type. The agents report only the type. The goal is to find allocatively efficient, strategy proof, nearly budget balanced mechanisms within the Groves class. Near budget balance is attained by returning as much of the received payments as rebates to agents. Two performance criteria are of interest: the maximum ratio of budget surplus to efficient surplus, and the expected budget surplus, within the class of linear rebate functions. The goal is to minimize them. Assuming that the valuation functions are known, we show that both problems reduce to convex optimization problems, where the convex constraint sets are characterized by a continuum of half-plane constraints parameterized by the vector of reported types. We then propose a randomized relaxation of these problems by sampling constraints. The relaxed problem is a linear programming problem (LP). We then identify the number of samples needed for “near-feasibility” of the relaxed constraint set. Under some conditions on the valuation function, we show that value of the approximate LP is close to the optimal value. Simulation results show significant improvements of our proposed method over the Vickrey-Clarke-Groves (VCG) mechanism without rebates. In the special case of indivisible goods, the mechanisms in this paper fall back to those proposed by Moulin, by Guo and Conitzer, and by Gujar and Narahari, without any need for randomization. Extension of the proposed mechanisms to situations when the valuation functions are not known to the central planner are also discussed.