Man Hon Cheung

An optimal energy allocation algorithm for energy harvesting wireless sensor networks

With the use of energy harvesting technologies, the lifetime of a wireless sensor network (WSN) can be prolonged significantly. Unlike a traditional WSN powered by non-rechargeable batteries, the energy management policy of an energy harvesting WSN needs to take into account the energy replenishment process. In this paper, we study the energy allocation for sensing and transmission in an energy harvesting sensor node with a rechargeable battery and a finite data buffer. The sensor node aims to maximize the total throughput in a finite horizon subject to time-varying energy harvesting rate, energy availability in the battery, and channel fading. We formulate the energy allocation problem as a sequential decision problem and propose an optimal energy allocation (OEA) algorithm using dynamic programming. We conduct simulations to compare the performance between our proposed OEA algorithm and the channel-aware energy allocation (CAEA) algorithm from [1]. Simulation results show that the OEA algorithm achieves a higher throughput than the CAEA algorithm under different settings.

Joint Energy Allocation for Sensing and Transmission in Rechargeable Wireless Sensor Networks

Different from a traditional wireless sensor network (WSN) powered by nonrechargeable batteries, the energy management policy of a rechargeable WSN needs to take into account the process of energy harvesting. In this paper, we study the energy allocation for sensing and transmission in an energy harvesting sensor node with a rechargeable battery and a finite data buffer. The sensor aims to maximize the expected total amount of data transmitted until the sensor stops functioning subject to time-varying energy harvesting rate, energy availability in the battery, data availability in the data buffer, and channel fading. Since the lifetime of the sensor is a random variable, we formulate the energy allocation problem as an infinite-horizon Markov decision process (MDP), and propose an optimal energy allocation (OEA) algorithm using the value iteration. We then consider a special case with infinite data backlog and prove that the optimal transmission energy allocation (OTEA) policy is monotonic with respect to the amount of battery energy available. Finally, we conduct extensive simulations to compare the performance of our OEA algorithm, OTEA algorithm, the finite-horizon transmission energy allocation (FHTEA) algorithm extended from [2], and the finite-horizon OEA (FHOEA) algorithm from [1]. Simulation results show that the OEA algorithm transmits the largest amount of data, and the OTEA algorithm can achieve a near-optimal performance with low computational complexity.

Hedonic Coalition Formation Game for Cooperative Spectrum Sensing and Channel Access in Cognitive Radio Networks

Cooperative spectrum sensing is an effective technique to improve the sensing performance and increase the spectrum efficiency in cognitive radio networks (CRNs). In this paper, we consider a CRN with multiple primary users (PUs) and multiple secondary users (SUs). We first propose a cooperative spectrum sensing and access (CSSA) scheme for all the SUs, where the SUs cooperatively sense the licensed channels of the PUs in the sensing subframe. If a channel is determined to be idle, the SUs which have sensed that channel will have a chance to transmit packets in the data transmission subframe. We then formulate this multi-channel spectrum sensing and channel access problem as a hedonic coalition formation game, where a coalition corresponds to the SUs that have chosen to sense and access a particular channel. The value function of each coalition and the utility function of each SU take into account both the sensing accuracy and the energy consumption. We propose an algorithm for decision node selection in a coalition. Moreover, we propose an algorithm based on the switch rule to allow the SUs to make decisions on whether to join or leave a coalition. We prove analytically that the set with all the SUs converges to a final network partition, which is both Nash-stable and individually stable. Besides, the proposed algorithms are adaptive to changes in network conditions. Simulation results show that our proposed CSSA scheme achieves a better performance than the closest PU (CPU) scheme and the noncooperative spectrum sensing and access (NSSA) scheme in terms of the average utility of the SUs.

A Coalition Formation Game for Energy-Efficient Cooperative Spectrum Sensing in Cognitive Radio Networks with Multiple Channels

Spectrum sensing is one of the key technologies to realize spectrum reuse and increase the spectrum efficiency in cognitive radio networks (CRNs). In this paper, we study energy-efficient cooperative multi-channel spectrum sensing in CRNs. We first propose a cooperative spectrum sensing and accessing (CSSA) scheme for all the secondary users (SUs). The SUs cooperatively sense the licensed channels of the primary users (PUs) in the sensing slot. If a channel is determined to be idle, the SUs which have sensed that channel will have a chance to transmit packets in the data transmission slot. We then formulate this multi- channel spectrum sensing problem as a coalition formation game, where a coalition corresponds to the SUs that have chosen to sense and access a particular channel. The utility function of each coalition takes into account both the sensing accuracy and energy efficiency. We propose distributed algorithms to find the optimal partition that maximizes the aggregate utility of all the coalitions in the system. We prove analytically that the proposed algorithms terminate at a stable partition that achieves the optimal aggregate utility. Simulation results show that the proposed algorithms result in the self-organization of the SUs that achieves a higher aggregate utility after each iteration. Also, the convergence and optimality of the proposed algorithms are proved by simulation results.

DAWN: Delay-Aware Wi-Fi Offloading and Network Selection

To accommodate the explosive growth in mobile data traffic, both mobile cellular operators and mobile users are increasingly interested in offloading the traffic from cellular networks to Wi-Fi networks. However, previously proposed offloading schemes mainly focus on reducing the cellular data usage, without paying too much attention on the quality of service (QoS) requirements of the applications. In this paper, we study the Wi-Fi offloading problem with delay-tolerant applications under usage-based pricing. We aim to achieve a good tradeoff between the users payment and its QoS characterized by the file transfer deadline. We first propose a general Delay- Aware Wi-Fi Offloading and Network Selection (DAWN) algorithm for a general single-user decision scenario. We then analytically establish the sufficient conditions, under which the optimal policy exhibits a threshold structure in terms of both the time and file size. As a result, we propose a monotone DAWN algorithm that approximately solves the general offloading problem, and has a much lower computational complexity comparing to the optimal algorithm. Simulation results show that both the general and monotone DAWN schemes achieve a high probability of completing file transfer under a stringent deadline, and require the lowest payment under a non-stringent deadline as compared with three heuristic schemes.

Hybrid Overlay/Underlay Cognitive Femtocell Networks: A Game Theoretic Approach

Femtocell networks have the potential to satisfy the increasing demand of mobile data usage. The recently proposed concept of cognitive femtocell network provides an effective way to further improve the spectrum spatial and frequency reuse. In this paper, we study the subchannel allocation problem for orthogonal frequency division multiple access (OFDMA)-based hybrid overlay/underlay cognitive femtocell networks. While most of the previous related studies did not fully exploit the potential of spatial and frequency reuse of the network, we propose a hybrid overlay and underlay spectrum access mechanism to further improve the performance of cognitive femtocell networks. We formulate the subchannel allocation problem as a coalition formation game among femtocell users under the hybrid access scheme, and analyze the stability of the coalition structure. We propose an efficient algorithm based on the solution concept of recursive core, and achieve a stable and efficient allocation. Simulation results show that the proposed algorithm achieves an improvement in aggregate network throughput up to 72% comparing to the overlay only scheme, 35% comparing to the underlay only scheme, and 18% comparing to a recently proposed coalition formation algorithm in the literature.

Random access for elastic and inelastic traffic in WLANs

In this paper, we consider the problem of random access in wireless local area networks (WLANs) with each station generating either elastic or inelastic traffic. Elastic traffic is usually non-real-time, while inelastic traffic is usually coming from real-time applications. We formulate a network utility maximization (NUM) problem, where the optimization variables are the persistent probabilities of the stations and the utilities are either concave or sigmoidal functions. Sigmoidal utility functions can better represent inelastic traffic sources compared to concave utility functions commonly used in the existing random access literature. However, they lead to non-convex NUM problems which are not easy to solve in general. By applying the dual decomposition method, we propose a subgradient algorithm to solve the formulated NUM problem. We also develop closed-form solutions for the dual subproblems involving sigmoidal functions that have to be solved in each iteration of the proposed algorithm. Furthermore, we obtain a sufficient condition on the link capacities which guarantees achieving the global optimal solution when our proposed algorithm is being used. If this condition is not satisfied, then we can still guarantee that the optimal value of the objective function is within some lower and upper bounds. We perform various simulations to validate our analytical models when the available link capacities meet or do not meet the sufficient optimality condition.

A Stackelberg game for cooperative transmission and random access in cognitive radio networks

In cognitive radio networks, the secondary users (SUs) can be selected as the cooperative relays to assist the transmission of the primary user (PU). In order to increase the utility, the PU needs to consider whether it is beneficial to use cooperative transmission and which SU should be chosen as the cooperative relay. In addition, if the PU selects a secondary relay, it needs to allocate time resources for cooperative transmission. Then, the SUs need to determine their strategies of random access when the licensed spectrum of the PU is available. In this paper, we first establish a model for cooperative cognitive radio networks with one PU and multiple SUs. We then propose a cooperative transmission and random access (CTRA) scheme. Based on the sequential structure of the decision-making, we study the cooperative cognitive radio network and determine the equilibrium strategies for both the PU and the SUs using the Stackelberg game. Simulation results show that both the PU and the SUs obtain higher utilities when compared with the noncooperative transmission and random access (NTRA) scheme.

SINR-Based Random Access for Cognitive Radio: Distributed Algorithm and Coalitional Game

In this paper, we study the problem of multi-channel medium access control (MAC) in cognitive radio (CR) networks. While most of the previously proposed MAC protocols for CR networks are heuristic and are based on the simplistic protocol model, we design a distributed MAC protocol using the more accurate signal-to-interference-plus-noise-ratio (SINR) model. First, we assume that the secondary users are cooperative and formulate the problem of assigning transmission and listening probabilities for random access as a non-convex network utility maximization problem. We propose a three-phase algorithm that converges to a near-optimal solution after solving a number of convex optimization problems distributively. Simulation results show that our proposed algorithm based on the SINR model achieves a higher aggregate throughput than other schemes which are based on the protocol model. Then, we consider the case that the secondary users are rational. We use coalitional game theory to study the incentive issues of user cooperation in a given channel for the SINR model. In particular, we use the solution concept of the core to analyze the stability of the grand coalition, and the solution concept of the Shapley value to fairly divide the payoff among the users. We show that the Shapley value lies in the core when all the users are one-hop neighbours of each other. We illustrate the Shapley value and the core with a numerical example.

Power-Delay Tradeoff With Predictive Scheduling in Integrated Cellular and Wi-Fi Networks

The explosive growth of global mobile traffic has led to rapid growth in the energy consumption in communication networks. In this paper, we focus on the energy-aware design of the network selection, subchannel, and power allocation in cellular and Wi-Fi networks, while taking into account the traffic delay of mobile users. Based on the two-timescale Lyapunov optimization technique, we first design an online Energy-Aware Network Selection and Resource Allocation (ENSRA) algorithm, which yields a power consumption within O(1/V)bound of the optimal value, and guarantees an O(V) traffic delay for any positive control parameter V. Motivated by the recent advancement in the accurate estimation and prediction of user mobility, channel conditions, and traffic demands, we further develop a novel predictive Lyapunov optimization technique to utilize the predictive information, and propose a Predictive Energy-Aware Network Selection and Resource Allocation (P-ENSRA) algorithm. We characterize the performance bounds of P-ENSRA in terms of the power-delay tradeoff theoretically. To reduce the computational complexity, we finally propose a Greedy Predictive Energy-Aware Network Selection and Resource Allocation (GP-ENSRA) algorithm, where the operator solves the problem in P-ENSRA approximately and iteratively. Numerical results show that GP-ENSRA significantly improves the power-delay performance over ENSRA in the large delay regime. For a wide range of system parameters, GP-ENSRA reduces the traffic delay over ENSRA by 20-30% under the same power consumption.