Marjan Zandi

On the Use of Biometrics to Secure Wireless Biosensor Networks

The fast improvements in a variety of technologies such as microprocessing, sensing material, and most importantly wireless technology resulted in development of the wireless sensor network technology. Wireless sensors that can be either implantable inside the human body or wearable by individuals are called the wireless biosensors. The wireless biosensors are used to gather real time and continuous medical data from different parts of the human beings. This medical data is typically sent to an external sensor and then to its associated destination where data processing and a final decision is carried out. Due to nature of medical data and their usage, ensuring the security of this data is extremely important. There are several limitations associated with biosensor networks such as limitation in power, memory, computation capability, and communication rate which makes the wireless biosensor security a real challenging problem. These security challenges form substantial barriers for the wide adoption of the technology. Biometrics approach is an efficient way to overcome the insecurity of the wireless biosensor networks. In this paper, we will look at how biometrics has helped securing data in wireless biosensor networks, and present the remaining challenges to have a workable biometric-based security framework for wireless biosensor networks.

Decentralized spectrum learning and access adaptive to channel availability distribution in primary network

We consider the effect of the mean availability distribution of primary channels on the performance of distributed learning and access policies, and develop a distributed learning and access policy that is effective in a wide range of primary channel conditions. We first extend the recently proposed BLA algorithm to distributed online learning of underlying primary channel availabilities, and modify the existing access policies to form BLA-ρRAND and BLA-DLF policies. By analyzing the distributed access collision mechanism offered by the ρRAND and DLF policies [1], [2], we identify how different mean channel availability distributions can impact the effectiveness of each policy. In light of this, we propose DSLA policy that adapts to different channel availability distribution conditions. Based on a closeness factor we propose, the DSLA policy automatically switches between the underlying learning policies, as well as the access policies, to determine which policy is most effective for a given primary channel condition. Simulation studies show that our proposed DSLA policy is effective in providing a good performance for a wide range of primary channel availability distributions.

Learning-stage based decentralized adaptive access policy for dynamic spectrum access

We consider the problem of decentralized online learning and channel access in a cognitive radio network. Based on an existing distributed access policy proposed in [1], named the ρRAND policy, we propose an adaptive decentralized access policy in which the distributed coordination among secondary users is adjusted at different stages of learning accuracy of the primary network. Specifically, we exploit a “perceived population” by each secondary user to reduce collision events at different learning stages. We design a metric that measures the level of learning accuracy and use that as an indicator to adjust the “perceived population” by each secondary user. Simulations show that our proposed adaptive policy improves the leading constant of the normalized regret and can provide substantial improvement over the ρRAND policy.

Distributed opportunistic spectrum access with unknown population

We consider a cognitive radio network where M secondary users compete with each other to access one of the N available channels. Channel availability statistics are assumed to evolve as i.i.d. Bernoulli random processes with means unknown to the secondary users. In addition, the number of secondary users M is unknown to each user. The main objective here is to design a distributed online learning and access policy which maximizes the total throughput of the secondary users. It has previously been shown that this problem can elegantly be modeled as a decentralized multi-armed bandit (DMAB) problem when M is known. We propose a truly decentralized online learning algorithm based on DMAB problem for unknown M. We show that using distributed access policies with wrong knowledge of M results in linear growth of regret, and underestimation incurs more significant loss than overestimation does. For distributed online learning of M, we propose a dynamic thresholding method, where the thresholds are dynamically determined using virtual systems built upon the current estimates of mean channel availabilities. Our algorithm allows both overestimation and underestimation in estimating M over time, and thus is capable of tracking the population change of secondary users.

Distributed Stochastic Learning and Adaptation to Primary Traffic for Dynamic Spectrum Access

We design distributed online learning and channel access for secondary users in a cognitive radio network. Our goal is to design channel selection and access that can effectively adapt to a wide range of traffic load patterns in the primary network. We propose a distributed adaptive learning and access policy by applying stochastic learning automata (SLA), where each secondary user (SU) probabilistically chooses one of the most available channels to access, with the channel selection probabilities being updated based on the collision events. Our design includes two underlying distributed learning algorithms: learning of primary channel availabilities from each SUs own sensing history, and SLA-based learning of channel selection from each SUs own collision history for collision avoidance. We show that some existing distributed access policies can be viewed as special cases of our proposed adaptive policy, with a set of fixed channel selection probabilities. Next, we formulate the distributed channel selection and access problem as a noncooperative game. We show that it is an exact potential game with at least one pure strategy Nash equilibrium (NE). We prove that, under our proposed adaptive policy, the channel selection probabilities converge toward a pure strategy NE of the game. Simulation demonstrates the effectiveness of our proposed adaptive policy in a wide range of distributions of mean channel availabilities, as compared with other existing policies.

Distributed stochastic learning for dynamic spectrum access adaptive to primary network conditions

We consider the problem of decentralized online learning and channel access among M secondary users (SUs) in a cognitive radio network. We aim at designing an adaptive policy that can effectively respond to different primary network conditions. By applying stochastic learning automata, we propose an adaptive decentralized access policy. Each SU probabilistically chooses one of the M-best channels to access. The channel selection probability is then updated based on collision events. Our proposed adaptive policy utilizes two underlying distributed learning algorithms: one is to learn from sensing history on the primary channel availability, and the other is to learn from collision history on channel selections among SUs to avoid further collision. Some previously proposed distributed access policies can be viewed as special cases of our proposed adaptive policy, with a set of pre-set channel selection probabilities. Simulation results demonstrate the effectiveness of our proposed adaptive policy in various distributions of mean channel availabilities across primary channels, as compared with other existing policies.

A New Class of Nyquist Pulses with a Monomial Roll-Off

A class of Nyquist pulses with a monomial roll-off is proposed. Using the measures of closeness to Nyquist-II criterion (eye-width) and robustness to sampling phase jitter, their performances are assessed. It is shown that these pulses generally outperform the pulses with cosine or linear roll-offs.