Recent developments and technological advancements in wireless communication, MicroElectroMechanical Systems (MEMS) technology and integrated circuits has enabled low-power, intelligent, miniaturized, invasive/non-invasive micro and nano-technology sensor nodes strategically placed in or around the human body to be used in various applications, such as personal health monitoring. This exciting new area of research is called Wireless Body Area Networks (WBANs) and leverages the emerging IEEE 802.15.6 and IEEE 802.15.4j standards, specifically standardized for medical WBANs. The aim of WBANs is to simplify and improve speed, accuracy, and reliability of communication of sensors/actuators within, on, and in the immediate proximity of a human body. The vast scope of challenges associated with WBANs has led to numerous publications. In this paper, we survey the current state-of-art of WBANs based on the latest standards and publications. Open issues and challenges within each area are also explored as a source of inspiration towards future developments in WBANs.

Wireless Body Area Networks: A Survey

Recently, Wireless Sensor Networks (WSNs) have attracted lot of attention due to their pervasive nature and their wide deployment in Internet of Things, Cyber Physical Systems, and other emerging areas. The limited energy associated with WSNs is a major bottleneck of WSN technologies. To overcome this major limitation, the design and development of efficient and high performance energy harvesting systems for WSN environments are being explored. We present a comprehensive taxonomy of the various energy harvesting sources that can be used by WSNs. We also discuss various recently proposed energy prediction models that have the potential to maximize the energy harvested in WSNs. Finally, we identify some of the challenges that still need to be addressed to develop cost-effective, efficient, and reliable energy harvesting systems for the WSN environment.

Energy harvesting in wireless sensor networks: A comprehensive review

https://hal.archives-ouvertes.fr/hal-01009386/document

Energy efficiency in wireless sensor networks: A top-down survey

Wireless sensor networks (WSNs) monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002–2013 of machine learning methods that were used to address common issues in WSNs. The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3963&context=sis_research

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Interest in Wireless Body Area Networks (WBANs) has increased significantly in recent years thanks to the advances in microelectronics and wireless communications. Owing to the very stringent application requirements in terms of reliability, energy efficiency, and low device complexity, the design of these networks requires the definition of new protocols with respect to those used in general purpose wireless sensor networks. This motivates the effort in research activities and in standardisation process of the last years. This survey paper aims at reporting an overview of WBAN main applications, technologies and standards, issues in WBANs design, and evolutions. Some case studies are reported, based on both real implementation and experimentation on the field, and on simulations. These results have the aim of providing useful insights for WBAN designers and of highlighting the main issues affecting the performance of these kind of networks.

A Survey on Wireless Body Area Networks: Technologies and Design Challenges

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 sensor's priority and nearly minimal spectrum handoffs.

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

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

As a new multimedia information acquisition and processing method, wireless multimedia sensor network (WMSN) has great application potential and attracts more and more attentions. Compared to traditional wireless sensor networks, the routing design of WMSN has higher requirements on the quality of service (QoS). This paper systemically analyzes the similarity between social network and WMSN, then designs a QoS trust estimation model based on social network analysis, which enables each sensor node measuring the service quality by monitoring the behaviors of neighbor nodes. An energy efficient QoS assurance routing based on cluster hierarchy (EEQAR) is proposed to achieve energy efficiency and meet the requirement of QoS. To obtain a better performance, EEQAR routing adopts cellular topology to form the cluster structure and balance the energy consumption by structure movement. The simulation results show the high performance of EEQAR routing in lifetime and quality of service.

Energy Efficiency QoS Assurance Routing in Wireless Multimedia Sensor Networks

Context-awareness assists ubiquitous media applications in discovering the changeable contextual information and adapting their behaviors accordingly. A wide spectrum of context-aware schemes have been proposed over the last decade. However, most of them provide partial functionalities of context-awareness in ubiquitous media applications. They are specified to a certain task and lack of systematic research on context-awareness. To this end, this survey aims at answering how close we are to developing context-aware applications in ubiquitous media in a systematic manner. This survey proposes a reference framework to identify key functionalities of context-awareness. Then, it investigates the state-of-the-art advances in every functionality of context-awareness. Finally, it points out potential directions in context-awareness research and tools for building and measuring context-aware ubiquitous media systems.

Survey on context-awareness in ubiquitous media

Biomedical sensor networks have been widely used in medical scenarios. Examples include patient monitoring, elderly assistance and disaster response. 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 within a given time and a certain packet delivery ratio. In other words, a set of quality of services (QoS) must be satisfied. In this paper, a cross-layer designed QoS-aware routing service framework is proposed. The main goal of the framework is to provide prioritized routing service and user specific QoS support. Routes are determined by user specific QoS metrics, wireless channel status, packet priority level, and sensor node's willingness to be a router. Furthermore, the routing service can send feedback on network conditions to the user application, so the medical application service level can be adjusted to obtain the highest adaptability and robustness. Simulation results have shown that the routing service framework performs well in respects of QoS metrics and energy efficiency in various medical scenarios. The routing service can provide guaranteed QoS for users of high priority level and acceptable network performances for 'best effort' required users.

/pdf/a-qos-aware-routing-service-framework-for-biomedical-sensor-58rl9ntlx6.pdf

Xuedong Liang

Papers

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

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

Energy Efficiency QoS Assurance Routing in Wireless Multimedia Sensor Networks

Survey on context-awareness in ubiquitous media

A QoS-aware Routing Service Framework for Biomedical Sensor Networks