Xuedong Liang

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.

Body Area Networks

Body area network (BAN) technology has emerged in recent years as a subcategory of wireless sensor network technology targeted at monitoring physiological and ambient conditions surrounding human beings and animals. However, BAN technology also introduces a number of challenges seldom seen before due to the scarcity of hardware and radio communication resources and the special properties of the radio environment under which they operate. In this chapter, we review the foundations of BANs along with the most relevant aspects relating to their design and deployment. We introduce current, state-of-the-art applications of BAN, as well as the most challenging aspects concerning their adoption and gradual deployment. We also discuss issues pertaining to sensor node communications, trade-offs, and interfacing with external infrastructure, in addition to important aspects relating to wearable sensor technology, enabling software and hardware, as well as future trends and open research issues in BANs.

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.

MRL-CC: a novel cooperative communication protocol for QoS provisioning in wireless sensor networks

Cooperative communications have been demonstrated to be effective in combating the multiple fading effects in wireless networks, and improving the network performance in terms of adaptivity, reliability, data throughput, and network lifetime. In this paper, we investigate the use of cooperative communications for Quality of Service (QoS) provisioning in resource-constrained wireless sensor networks, and propose MRL-CC, a Multi-agent Reinforcement Learning based multi-hop mesh Cooperative Communication mechanism. In MRL-CC, a multi-hop mesh cooperative structure is constructed for reliable data disseminations. The cooperative mechanism that defines cooperative partner assignments, and coding and transmission schemes, is implemented using a multi-agent reinforcement learning algorithm. We compare the network performance of MRL-CC with MMCC, a Multi-hop Mesh structure based Cooperative Communication scheme, and investigate the impacts of network traffic load, interference, and sensor nodes mobility on the network performance. Simulation results show that MRL-CC performs well in terms of a number of QoS metrics, and fits well in large-scale networks and highly dynamic environments.

Cooperative Communications with Relay Selection for QoS Provisioning in Wireless Sensor Networks

Cooperative communications have been demonstrated to be effective in combating the multiple fading effects in wireless networks, and improving the network performance in terms of adaptivity, reliability and network throughput. In this paper, we investigate the use of cooperative communications with adaptive relay selection for resource-constrained wireless sensor networks, and propose QoS-RSCC, a QoS-support multi-agent reinforcement learning based relay selection scheme for cooperative communications. In QoS-RSCC, optimal relays, in terms of outage probability and channel efficiency, are selected distributedly from multiple relaying candidates for the intermediate routers along the multi-hop route, without the needs of prior knowledge of the wireless network model and centralized control. We compare the network performance of QoS-RSCC with CRP [1], and investigate the impacts of network traffic load, channel bit error rate, and nodes mobility on the network performance. Simulation results show that QoS-RSCC can achieve a near-optimal performance on both diversity gains and channel efficiency, and fits well in dynamic environments.

Cooperative communications with relay selection for wireless networks: design issues and applications

Relay selection schemes for cooperative communications to achieve full cooperative diversity gains while maintaining spectral and energy efficiency have been extensively studied in a recent research. These schemes select only the best relay from multiple relaying candidates to cooperate with a communication link. In the present paper, we reviewed recently proposed cooperative communication protocols that integrate with relay selection mechanisms. The key design issues for relay selection mechanisms, for example, relaying candidate selection, optimal relay assignment, and cooperative transmission, were identified. We further discussed the challenges of optimal relay assignment in multi-hop wireless sensor networks and presented the potential applications of cooperative communications with a relay selection in such networks. Future research directions were outlined, for example, the issues of service differentiation and system fairness in cooperative communication systems and the joint use of game theory and adaptive learning techniques in relaying candidate selection and optimal-relay assignment mechanisms for efficient allocation of network resources. Copyright

A novel cooperative communication protocol for QoS provisioning in wireless sensor networks

Cooperative communications have been demonstrated to be effective in combating the multiple fading effects in wireless networks, and improving the network performance in terms of adaptivity, reliability, data throughput and network life time. In this paper, we investigate the use of cooperative communications for quality of service (QoS) provisioning in resource-constrained wireless sensor networks, and propose MRL-CC, a Multi-agent Reinforcement Learning based multi-hop mesh Cooperative Communication mechanism for wireless sensor networks. In order to disseminate data reliably in MRL-CC, a multi-hop mesh cooperative structure is first constructed. Then a cooperative mechanism with cooperative partner assignments, and coding and transmission schemes is implemented using a multi-agent reinforcement learning algorithm. We compare the network performance of MRL-CC with MMCC [1], a Multi-hop Mesh structure based Cooperative Communication scheme, and investigate the impacts of network traffic load, interference and sensor nodes mobility on the network performance. Simulation results show that MRL-CC performs well in terms of a number of QoS metrics, and fits well in large-scale networks and highly 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.

Performance Analysis of ZigBee Technology for Wireless Body Area Sensor Networks

A Wireless Body Area Sensor Network (WBASN) is a radio-frequency (RF) based wireless networking technology that interconnects tiny nodes with sensor or actuator capabilities in, on or around a human body. Complementing the coverage of Wireless Personal Area Networks (WPANs), WBASNs target diverse applications. This paper extensively evaluates the performance of ZigBee technology for WBASN-WPAN system by analysis and computer simulations. Specifically, we configure the system deployment, network structure, applicable data rates for WBASN sensor nodes, and underlying radio parameters. Average reception ratio, throughput and latency have been evaluated for the overall system performance; fairness across sensor nodes has been examined; preliminary results regarding mobility support of ZigBee for WBASN are also shown. After examining the performance of ZigBee network with our WBASN-WPAN configurations, we come to the conclusion that serveral optimizations should be done before applying ZigBee to such a system.

A game-theoretic approach for relay assignment over distributed wireless networks

To achieve full cooperative diversity gains while still maintaining spectral and energy efficiency, relay assignment schemes for cooperative communications have been extensively studied in recent research. These schemes select only the best relay from multiple relaying candidates to cooperate with a communication link. In this paper, we formulate the problem of relay assignment as a non-cooperative, mixed strategy, repeated game, where relaying candidates are modeled as rational players. We then propose a Game Theory based Relay Assignment scheme GTRA, in which each player plays against all the other players, and determines whether to cooperate with a communication link on a packet-by-packet basis in a distributed manner. To adapt to dynamic environments, an adaptive learning algorithm is utilized by players to learn optimal strategies of relay assignment, as well as orienting the game to converge to a set of correlated equilibriums. We compare GTRA with BR, a fictitious game based approach. The simulation results show that GTRA outperforms BR in terms of network throughput, especially in environments where the channel fading becomes severe. It is also shown that GTRA can converge to a correlated equilibrium in a short period that enables it to work well in dynamic environments.