Sang-Seon Byun

Dynamic Spectrum Allocation in Wireless Cognitive Sensor Networks: Improving Fairness and Energy Efficiency

This paper considers the centralized spectrum allocations in resource-constrained wireless sensor networks with the following goals: (1) allocate spectrum as fairly as possible, (2) utilize spectrum resource maximally, (3) reflect the priority among sensor data, and (4) reduce spectrum handoff. The problem is formulated into a multi-objective problem, where we propose a new approach to solve it using modified game theory (MGT). In addition, cooperative game theory is adopted to obtain approximated solutions for MGT in reasonable time. The results obtained from numerical experiments show that the proposed algorithm allocates spectrum bands fairly with well observing each sensors priority and nearly minimal spectrum handoffs.

A reinforcement learning based routing protocol with QoS support for biomedical sensor networks

Biomedical sensor networks have been widely used in medical applications, where data packets usually contain vital sign information and the network used for communications should guarantee that these packets can be delivered to the medical center reliably and efficiently. In other words, a set of requirements for quality of services (QoS) must be satisfied. In this paper, RL-QRP, a reinforcement learning based routing protocol with QoS-support is proposed for biomedical sensor networks. In RL-QRP, optimal routing policies can be found through experiences and rewards without the need of maintaining precise network state information. Simulation results show that RL-QRP performs well in terms of a number of QoS metrics and energy efficiency in various medical scenarios. By investigating the impacts of network traffic load and sensor node mobility on the network performance, RL-QRP has been proved to fit well in dynamic environments.

A multi-agent reinforcement learning based routing protocol for wireless sensor networks

In this paper, we present MRL-QRP, a multi-agent reinforcement learning based routing protocol with QoS support for wireless sensor networks. In MRL-QRP, sensor node cooperatively computes QoS routes using a distributed value function - distributed reinforcement learning algorithm (DVFDRL). Global optimization can be achieved by using locally observed network information and limited exchanging of state values with immediate neighboring nodes. We compare the network performance of MRL-QRP with QoS-AODV, an on demand QoS support routing protocol. The impact of network traffic load and sensor node¿s mobility on the network performance are investigated, simulation results show that MRL-QRP performs well in respects of a number of QoS metrics and fits well in highly dynamic environments.

A market-clearing model for spectrum trade in cognitive radio networks

We model cognitive radio networks (CRNs) as a spectrum market where every primary user (PU) offer her subchannels with certain interference bound indicating the interference limit the PU can tolerate, and secondary users (SUs) purchase the right to access the subchannels while observing their budget constraints as well as the inference bound. In this spectrum market model, the utility of SU is defined as the achievable transmission rate in free space, and the utility of PU is given by the net profit the PU can make. Then we develop a market equilibrium in the context of Fisher model, and show that the equilibrium is obtained by solving an optimization problem called Eisenberg-Gale convex program. Furthermore, we develop a distributed algorithm with best response dynamics and price dynamics, and prove that its asymptotic solutions are equivalent to the solutions given by the convex program. Besides, we introduce adaptive step size to the price dynamics for faster convergence. With some numerical examples, we show that it helps to achieve faster convergence.

Approximations of multiobjective optimization for dynamic spectrum allocation in wireless sensor networks

This paper considers the problem of the centralized spectrum allocation in wireless sensor networks towards the following goals: (i) maximizing fairness, (ii) maximizing spectrum utilization, (iii) reflecting the priority among sensor data, and (iv) avoiding unnecessary spectrum handoff. We cast this problem into a biobjective mixed integer nonconvex nonlinear programming that is absolutely intractable to solve at least globally without any aid of conversion and approximation. We tackle this intractability with convexification, scalarization, and rounding method that yield good approximate integer solutions.

Power control for mission critical wireless sensor networks using repeated coalitional games

In this paper, we propose a coalitional game theoretic approach to the power control problem in resourceconstrained wireless sensor networks, where the objective is to enhance power efficiency of individual sensors while providing the QoS requirements. We model this problem as two-sided one-to-one matching game and deploy deferred acceptance procedure that produces a single matching in the core, which is the set of actions aN of all sensors such that no coalition of sensors has an action that all its members prefer to aN. Furthermore, we show that, by applying the procedure repeatedly, a certain stable state is achieved where no sensor can anticipate improvements in their power efficiency as far as all of them are subject to their own QoS constraints. We evaluate our proposal by comparing them with cluster-based coalescing and the local optimal solution obtained by maximizing the total system energy efficiency, where the objective function is non-convex.

Increasing sum rate in multiband cognitive radio networks by centralized power allocation schemes

In this paper, we develop two different approaches for the centralized joint power and bandwidth allocation problem. We aim to maximize the uplink sum rate with multiple base stations. First, we exploit the convex approximation with series of Geometric Programming (GP) formulations. Then, we propose a novel, iterative method that is enhanced with scheduling to improve both convergence speed and sum rate. The problem is handled in an interference-limited multiband system using power and interference constraints. Our numerical experiments illustrate that the new iterative methods sum rate performance is comparable to the GP, within small number of iterations and lower complexity.

Fair allocation of sensor measurements using Shapley value

In this paper, we consider the problem of measurement allocation in a spatially correlated sensor field. Our objective is to determine the probability of each sensors being measured for improved observability; the sensor located at less correlated area should be assigned more probability. To this end, we quantify the level of correlation of each sensor through the mutual information criterion reflecting the level of uncertainty about unattended locations. Then we deploy the Shapley value, a representative single-valued solution concept in cooperative game theory. The Shapley value expresses the average marginal contribution of each sensor to the observation of a spatially correlated sensor field, and can be used to allocate the probability of each sensors being measured in proportion to its contribution. Against the intractability in computing the true Shapley value, we deploy a randomized methods based on sampling, which can compute the approximate Shapley value with linear time complexity. Through numerical experiments, we evaluate the approximate Shapley value achieved by the randomized method by comparing it to the exact Shapley value, and estimate how the measurement allocation based on the Shapley value contributes to the overall observability and coverage.

On the steady state in multiuser multiband IR-UWB without NBI detection

Impulse radio ultra-wideband (IR-UWB) communication is a strong candidate for short range wireless sensor networks (BWSN). One of the biggest challenges in IR-UWB is to suppress the performance degradation due to concurrent narrowband interferences (NBIs). There are two main possible ways of NBI suppression, namely, cancellation and avoidance. The former is difficult to be implemented since it makes receivers largely complicate (e.g., high speed sampling and Rake receivers), and the latter requires accurate detection of NBI center frequency. In this paper we consider a multiuser multiband IR-UWB where no NBI detection mechanism is available due to complexity constraints of implanted biomedical sensors. In this situation, we are interested in finding a transmission scheduling policy that provides a stable state where no single sensor deviates from the scheduling. To this end, we model multiuser multiband IR-UWB as n-transmitter jammer game, and computes its Nash equilibrium that corresponds to the definition of the stable state. We observe that the steady state based on the Nash equilibrium provides a scheduling with the policy of letting all sensors access to all available subbands uniformly. We evaluate the scheduling scheme based on the steady state by comparing it with non-uniform random access scheduling.

A measurement allocation scheme for reliable data gathering in spatially correlated sensor networks

In this paper, we consider the measurement allocation problem in a spatially correlated sensor field. Our goal is to determine the probability of each sensors being measured based on its contribution to the estimation reliability; it is desirable that a sensor improving the estimation reliability is measured more frequently. We consider a correlation model reflecting transmission power limit, noise in measurement process and channel, and channel attenuation. Then the estimation reliability is defined as the distortion error between the event source in the sensor field and its estimation at the sink. Motivated by the correlation nature, we model the measurement allocation problem into a cooperative game, and then express each sensors contribution using Shapley value — a formal quantification of individual players average marginal contribution. Against the intractability in the computation of exact Shapley value, we deploy randomized method that enables to compute approximate Shapley value within reasonable time. In numerical experiments, we evaluate approximate Shapley value by comparing it to the exact one, and illustrate that measurement allocation according to Shapley value turns to the balance between the estimation reliability and network lifetime.