Kobi Cohen

A Time-Varying Opportunistic Approach to Lifetime Maximization of Wireless Sensor Networks

In this paper, we examine the advantages of transmission scheduling by medium access control (MAC) protocols for energy-limited wireless sensor networks (WSN) as a means of maximizing network lifetime. We consider transmission scheduling for sensor networks with a mobile access point, where each sensor transmits its measurement directly to an access point through a fading channel. WSN lifetime maximization depends almost exclusively on the channel-state information (CSI) and the residual-energy information (REI) of each sensor in the network. We discuss distributed protocols which exploit local CSI and REI. We present a novel protocol for distributed transmission scheduling, dubbed the time-varying opportunistic protocol (TOP), for maximizing the network lifetime. TOP prioritizes sensors with better channels when the network is young, by exploiting local CSI to reduce transmission energy. However, TOP prefers sensors with higher residual energy when the network is old by exploiting local REI to reduce the wasted energy. We show that the relative performance loss of TOP compared to the optimal centralized protocol in terms of network lifetime decreases as the initial energy stored in the sensors increases. Furthermore, TOP significantly simplifies the implementation of carrier sensing compared to other distributed MAC protocols. We also explore the case of large-scale wireless sensor networks, where the activated sensors are picked randomly and modify the implementation of TOP for such networks. Simulation results show that TOP outperforms other distributed MAC protocols that have been proposed recently.

Game Theoretic Aspects of the Multi-Channel ALOHA Protocol in Cognitive Radio Networks

In this paper we consider the problem of distributed throughput maximization of cognitive radio networks with the multi-channel ALOHA medium access protocol. First, we characterize the Nash Equilibrium Points (NEPs) of the network when users solve an unconstrained rate maximization (i.e., the total transmission probability equals one). Then, we focus on constrained rate maximization, where user rates are subject to a total transmission probability constraint. We propose a simple best-response algorithm that solves the constrained rate maximization, where each user updates its strategy using its local channel state information (CSI) and by monitoring the channel utilization. We prove the convergence of the proposed algorithm using the theory of potential games. Furthermore, we show that the network approaches a unique equilibrium as the number of users increases. Then, we formulate the problem of choosing the access probability as a leader-followers Stackelberg game, where a single user is chosen to be the leader to manage the network. We show that a fully distributed setup can be applied to approximately optimize the network throughput for a large number of users. Finally, we extend the model to the case where primary and secondary users co-exist in the same frequency band.

Energy-Efficient Detection in Wireless Sensor Networks Using Likelihood Ratio and Channel State Information

In this paper we investigate transmission scheduling by Medium Access Control (MAC) for energy-efficient detection using Wireless Sensor Networks (WSN). We consider the binary hypothesis testing problem. The decision is made by an access point and is based on received data from sensors that transmit through a fading channel. We study the significance of exploiting both Channel-State Information (CSI) and Likelihood-Ratio Information (LRI) to design an adequate MAC protocol that minimizes the total transmission energy required for optimal detection. We formulate the access problem as a history-dependent decision process. The optimal solution is mathematically intractable and suffers from exponential complexity as a function of model size. Hence, we propose an approximate solution using the Markov property to reduce complexity and make the problem mathematically tractable. We designed the LRI and CSI Based Access (LCBA) protocol based on this solution. The LCBA protocol trades off between LRI and CSI to reduce the total transmission energy. Simulation results show a significant performance gain of LCBA over existing approaches.

Optimal Index Policies for Anomaly Localization in Resource-Constrained Cyber Systems

The problem of anomaly localization in a resource-constrained cyber system is considered. Each anomalous component of the system incurs a cost per unit time until its anomaly is identified and fixed. Different anomalous components may incur different costs depending on their criticality to the system. Due to resource constraints, only one component can be probed at each given time. The observations from a probed component are realizations drawn from two different distributions depending on whether the component is normal or anomalous. The objective is a probing strategy that minimizes the total expected cost, incurred by all the components during the detection process, under reliability constraints. We consider both independent and exclusive models. In the former, each component can be abnormal with a certain probability independent of other components. In the latter, one and only one component is abnormal. We develop optimal index policies under both models. The proposed index policies apply to a more general case where a subset (more than one) of the components can be probed simultaneously. The problem under study also finds applications in spectrum scanning in cognitive radio networks and event detection in sensor networks.

Active Hypothesis Testing for Anomaly Detection

The problem of detecting a single anomalous process among a finite number M of processes is considered. At each time, a subset of the processes can be observed, and the observations from each chosen process follow two different distributions, depending on whether the process is normal or abnormal. The objective is a sequential search strategy that minimizes the expected detection time subject to an error probability constraint. This problem can be considered as a special case of active hypothesis testing first considered by Chernoff where a randomized strategy, referred to as the Chernoff test, was proposed and shown to be asymptotically (as the error probability approaches zero) optimal. For the special case considered in this paper, we show that a simple deterministic test achieves asymptotic optimality and offers better performance in the finite regime. We further extend the problem to the case where multiple anomalous processes are present. In particular, we examine the case where only an upper bound on the number of anomalous processes is known.

Asymptotically Optimal Anomaly Detection via Sequential Testing

Sequential detection of independent anomalous processes among K processes is considered. At each time, only M (1 ≤ M ≤ K) processes can be observed, and the observations from each chosen process follow two different distributions, depending on whether the process is normal or abnormal. Each anomalous process incurs a cost per unit time until its anomaly is identified and fixed. Switching across processes and state declarations are allowed at all times, while decisions are based on all past observations and actions. The objective is a sequential search strategy that minimizes the total expected cost incurred by all the processes during the detection process under reliability constraints. We develop index-type algorithms for the case with both known observation distributions and the case when the observation distributions have unknown parameters. We show that the proposed algorithms are asymptotically optimal in terms of minimizing the total expected cost as the error constraints approach zero. Simulation results demonstrate strong performance in the finite regime.

Performance Analysis of Likelihood-Based Multiple Access for Detection Over Fading Channels

In this paper, we consider the binary hypothesis testing problem using wireless sensor networks. We analyze the case where sensors transmit their local log-likelihood ratio (LLR) directly to a fusion center (FC) using an analog transmission scheme over multiple-access fading channels. Due to the nature of the wireless medium, the FC receives a superposition of sensor transmissions. The decision is made by the FC and is based on received data from the sensors. In contrast to the case of identical channels and i.i.d observations, the analog transmission of the LLR over multiple-access fading channels does not achieve the centralized error exponent. Large deviation tools are used in this paper to characterize the error exponent in the asymptotic regime (when the number of sensors approaches infinity) in the case of non-i.i.d observations and non-i.i.d fading channels. Chernoff bounding techniques are used to provide bounds on the error probability for a finite number of sensors when the observations and the fading channels are independent across sensors. Specific performance analysis is provided for detection over both i.i.d and spatially correlated Markovian fading channels. Simulation results then illustrate the detectors performance.

Quickest anomaly detection: A case of active hypothesis testing

The problem of quickest detection of an anomalous process among M processes is considered. At each time, a subset of the processes can be observed, and the observations follow two different distributions, depending on whether the process is normal or abnormal. The objective is a sequential search strategy that minimizes the expected detection time subject to an error probability constraint. This problem can be considered as a special case of active hypothesis testing first considered by Chernoff in 1959, where a randomized test was proposed and shown to be asymptotically optimal. For the special case considered in this paper, we show that a simple deterministic test achieves asymptotic optimality and offers better performance in the finite regime.

Distributed game-theoretic optimization and management of multichannel ALOHA networks

The problem of distributed rate maximization in multichannel ALOHA networks is considered. First, we study the problem of constrained distributed rate maximization, where user rates are subject to total transmission probability constraints. We propose a best-response algorithm, where each user updates its strategy to increase its rate according to the channel state information and the current channel utilization. We prove the convergence of the algorithm to a Nash equilibrium in both homogeneous and heterogeneous networks using the theory of potential games. The performance of the best-response dynamic is analyzed and compared to a simple transmission scheme, where users transmit over the channel with the highest collision-free utility. Then, we consider the case where users are not restricted by transmission probability constraints. Distributed rate maximization under uncertainty is considered to achieve both efficiency and fairness among users. We propose a distributed scheme where users adjust their transmission probability to maximize their rates according to the current network state, while maintaining the desired load on the channels. We show that our approach plays an important role in achieving the Nash bargaining solution among users. Sequential and parallel algorithms are proposed to achieve the target solution in a distributed manner. The efficiencies of the algorithms are demonstrated through both theoretical and simulation results.

Distributed learning algorithms for spectrum sharing in spatial random access networks

We consider distributed optimization over orthogonal collision channels in spatial multi-channel ALOHA networks. Users are spatially distributed and each user is in the interference range of a few other users. Each user is allowed to transmit over a subset of the shared channels with a certain attempt probability. We study both the non-cooperative and cooperative settings. In the former, the goal of each user is to maximize its own rate irrespective of the utilities of other users. In the latter, the goal is to achieve proportionally fair rates among users. We develop simple distributed learning algorithms to solve these problems. The efficiencies of the proposed algorithms are demonstrated via both theoretical analysis and simulation results.