Karim Khalil

Optimal scheduling in cooperate-to-join Cognitive Radio Networks

Optimal transmission scheduling in wireless cognitive networks is considered under the spectrum leasing model.We propose a cooperative scheme in which secondary nodes share the time slot with primary nodes in return for cooperation. Cooperation is feasible only if the systems performance is improved over the non-cooperative case. First, we investigate a scenario where secondary users are interested in immediate rewards. Then, we formulate another problem where the secondary users are guaranteed a portion of the primary utility, on a long term basis, in return for cooperation. In both scenarios, our proposed schemes are shown to outperform non-cooperative scheduling schemes, in terms of both individual and total expected utility, for a given set of feasible constraints. Based on Lyapunov Optimization techniques, we show that our schemes are arbitrarily close to the optimal performance at the price of reduced convergence rate.

On the delay limited secrecy capacity of fading channels

In this paper, the delay limited secrecy capacity of the flat fading channel is investigated under two different assumptions on the available transmitter channel state information (CSI). The first scenario assumes perfect prior knowledge of both the main and eavesdropper channel gains. Here, upper and lower bounds on the secure delay limited capacity are derived and shown to be tight in the high signal-to-noise ratio (SNR) regime (for a wide class of channel distributions). In the second scenario, only the main channel CSI is assumed to be available at the transmitter. Remarkably, under this assumption, we establish the achievability of non-zero secure rate (for a wide class of channel distributions) under a strict delay constraint. In the two cases, our achievability arguments are based on a novel twostage approach that overcomes the secrecy outage phenomenon observed in earlier works.

Opportunistic Secrecy with a Strict Delay Constraint

We investigate the delay limited secrecy capacity of the flat fading channel under two different assumptions on the available transmitter channel state information (CSI). The first scenario assumes perfect prior knowledge of both the main and eavesdropper channel gains. Here, upper and lower bounds on the delay limited secrecy capacity are derived, and shown to be tight in the high signal-to-noise ratio (SNR) regime. In the second scenario, only the main channel CSI is assumed to be available at the transmitter where, remarkably, we establish the achievability of a non-zero delay-limited secure rate, for a wide class of channel distributions, with a high probability. In the two cases, our achievability arguments are based on a novel two-stage key-sharing approach that overcomes the secrecy outage phenomenon observed in earlier works.

Optimal scheduling and power allocation in cooperate-to-join cognitive radio networks

In this paper, optimal resource allocation policies are characterized for wireless cognitive networks under the spectrum leasing model. We propose cooperative schemes in which secondary users share the time-slot with primary users in return for cooperation. Cooperation is feasible only if the primary systems performance is improved over the non-cooperative case. First, we investigate a scheduling problem where secondary users are interested in immediate rewards. Here, we consider both infinite and finite backlog cases. Then, we formulate another problem where the secondary users are guaranteed a portion of the primary utility, on a long-term basis, in return for cooperation. Finally, we present a power allocation problem where the goal is to maximize the expected net benefit defined as utility minus cost of energy. Our proposed scheduling policies are shown to outperform non-cooperative scheduling policies, in terms of expected utility and net benefit, for a given set of feasible constraints. Based on Lyapunov optimization techniques, we show that our schemes are arbitrarily close to the optimal performance at the price of reduced convergence rate.

Multiple access game with a cognitive jammer

We consider a two-user multiple access game in which one player (primary user) is interested in maximizing its data rate at the minimum possible transmission power and the other player (secondary, cognitive user) can either jam the primary traffic or coordinate with the primary user and send its own message to the common destination. The cognitive user employs noise forwarding as a leverage to maximize its own data rate by forcing the primary user to decrease its power level. First, the unique Nash equilibrium of the non-cooperative static game is derived and shown to be inefficient for certain ranges of channel gains and cost parameters. Then, a Stackelberg game formulation is considered in which the primary user is the leader. Here, interestingly, it is shown that the secondary accepts to play as the follower where the Stackelberg equilibrium dominates the Nash equilibrium and hence lose-lose situations are eliminated.

Coexistence management for heterogeneous networks in white spaces

Certain spectrum bands originally occupied by legacy systems such as TV transmission have been opened up for free and unlicensed access in many countries provided no interference to the incumbents. It is envisioned that heterogeneous networks will coexist in these white space bands sharing the available unlicensed spectra. Therefore, an efficient coexistence management scheme is needed to coordinate access from different networks such that spectrum utilization is maximized while harmful interference is avoided. In this paper, we develop a two-stage coexistence management algorithm that efficiently coordinates spectrum sharing among heterogeneous networks operating over spectrum white spaces. The first stage allocates bandwidth to the networks such that fairness is achieved while maximizing spectrum reuse. Then, in the second stage, the available bandwidth is divided into channel slots and assigned to different networks according to the allocation from the first stage. The proposed scheme can be implemented at the white space database manager where the regulations require networks to contact in order to get the list of available spectrum for their location. Simulation results show that our proposed scheme provides significant performance gains owing to orthogonal channel assignment with coordinated spectrum re-use.

Optimal Monitor Placement for Detection of Persistent Threats

We study optimal monitor placement for intrusion detection in networks with persistent attackers. The problem is modeled as a stochastic game in which the attacker attempts to control targets by delivering malicious packets while the defender tries to detect such attempts. The state of the game is determined by the target end-systems in the network, each of which can be in either a healthy or a compromised state. Compromised targets are controlled by the attacker and may be used to inject malicious packets into the network to attack healthy targets. In addition, a random re-imaging process is deployed on all targets to regain control of compromised targets. We find the game value and the equilibrium strategies for both players under different assumptions on the knowledge of the state at the defender.

Iterative Fair Channel Assignment for Wireless Networks

We introduce an orthogonal channel assignment algorithm that increases spectrum utilization via systematic re-use of channels, leveraging carrier aggregation capability supported in the latest access technologies and maintaining fairness across networks. Unlike conventional graph coloring, the proposed algorithm iterates over available channels assigning one channel at a time starting with a network with a higher priority (e.g. based on assigned channels so far) and then re-using the same channel in as many other networks as possible. Numerical results show that the proposed algorithm not only improves spectrum utilization but also well approximates the optimal solution yet with polynomial-time complexity.

Energy Efficient Object Detection in Camera Sensor Networks

A wireless camera network can provide situation awareness information (e.g., humans in distress) in scenarios such as disaster recovery. If such camera sensors are battery operated, sending raw video feeds back to a central controller can be expensive in terms of energy consumption. Further, if all cameras were to use the optimal processing algorithm for object decision, they may also expend unnecessary energy. Stated otherwise, cameras that capture the same objects may not all have to use the optimal algorithm to achieve a desired accuracy, and this can save processing energy costs. In this paper, our objective is to design and implement a framework that can support coordination among cameras to deliver highly accurate detection of objects in an energy efficient way. The framework, which we call EECS (for energy efficient camera sensors), estimates the detection accuracy and energy costs incurred (both the processing and communication costs are taken into account) with each detection algorithm for each camera, and comes up with a choice of cameras for sending information pertaining to the object of interest. This set of cameras and the video processing algorithms that they must use, are chosen so as to minimize the energy expenditures, given a desired detection accuracy. We implement EECS on a camera network built with smartphones, and demonstrate that it reduces the energy consumption by up to 40% while ensuring a object detection accuracy of over 86%.

Jaal: Towards Network Intrusion Detection at ISP Scale

We have recently seen an increasing number of attacks that are distributed, and span an entire wide area network (WAN). Today, typically, intrusion detection systems (IDSs) are deployed at enterprise scale and cannot handle attacks that cover a WAN. Moreover, such IDSs are implemented at a single entity that expects to look at all packets to determine an intrusion. Transferring copies of raw packets to centralized engines for analysis in a WAN can significantly impact both network performance and detection accuracy. In this paper, we propose Jaal, a framework for achieving accurate network intrusion detection at scale. The key idea in Jaal is to monitor traffic and construct in-network packet summaries. The summaries are then processed centrally to detect attacks with high accuracy. The main challenges that we address are (a) creating summaries that are concise, but sufficient to draw highly accurate inferences and (b) transforming traditional IDS rules to handle summaries instead of raw packets. We implement Jaal on a large scale SDN testbed. We show that on average Jaal yields a detection accuracy of about 98%, which is the highest reported for ISP scale network intrusion detection. At the same time, the overhead associated with transferring summaries to the central inference engine is only about 35% of what is consumed if raw packets are transferred.