Kae Hsiang Kwong

Dynamic bandwidth allocation algorithm for differentiated sservices over WDM EPONs

A variety of passive optical network (PON) systems has been proposed to target the bottleneck problem in the local access. The medium access controls (MACs) of the PON systems are either based on cyclic rotation or polling mechanism. However, these MACs have inherited a potential problem; the cycle time (or polling cycle in the polling MACs) increases linearly as the number of attached ONUs scale up, which can be caused by increasing customer numbers. The longer cycle time means that the ONUs have to wait longer before the next transmission window arrives thus contributing to longer packet delays and poorer QoS. In this paper, a new WDM PON system is proposed where multiple wavelength channels are established in both upstream and downstream directions. The MAC applied is based on an adaptive polling mechanism, and can be used as an upgrade solution for PON systems

Implementation of herd management systems with wireless sensor networks

The work summarises a study of the data communications requirements for agricultural livestock monitoring applications using wireless sensor networks (WSNs). Several design challenges are identified and analysed in depth based on actual global positioning system positioning data gathered from an actual herd of cattle. A wireless system including antennae diversity together with data downloads optimisation schemes utilising data collector and routers are developed and tested in a working farm environment. Two analysis metrics, connection availability and connection duration, are used to quantify the impact of cattle movement on network connectivity. The major contributions of this study stem from a definition of the communication issues in deploying animal monitoring platforms in free-ranging farm environments and the analysis and optimisation of the wireless data download performance using as the foundation knowledge gained from a series of working farm trials. Additionally, the data download protocols are designed particularly to treat animal movement. The results prove the viability of WSN-based solutions for livestock monitoring applications.

CogSeNet: A Concept of Cognitive Wireless Sensor Network

Cognitive Sensor Network (CogSeNet) is an intelligent based wireless sensor network system which relies on cognitive processes to provide a dynamic capability in configuring wireless sensor network. CogSeNet is a network structure formed by sensor nodes equipped with cognitive modules allowing them to observe their operating environment and allowing a decision can be derived. A series of actions result so that the nodes can adapt and achieve certain goals by an overall policy. These goals can be as simple as to provide robust connectivity or as complex as negotiate additional resources from neighbouring network groups to forward mission-critical data. In this paper, the concept of cognitive sensor network is reviewed and a preliminary case study is illustrated.

Adaptation of wireless sensor network for farming industries

In recent years, wireless sensor networks (WSN) have received considerable attention within agriculture and farming as a means to reduce operational costs and enhance animal health care. This paper examines the application of WSNs to livestock monitoring and the issues related to hardware realization. The core of this study is to overcome the aforementioned drawbacks by using alternative cheap, low power consumption sensor nodes capable of providing real-time communication at a reasonable hardware cost. In this paper, various factors i.e. radio frequency selection, channel bandwidth, etc. have been evaluated to provide a solution which can obtain real-time data from diary cattle whilst conforming to the limitations associated with WSNs implementations.

Dynamic backhaul sensitive Network Selection Scheme in LTE-WiFi wireless HetNet

Small-cell deployment within a wireless Heterogeneous Network (HetNet) presents backhauling challenges that differ from those of conventional macro-cells. Due to the lack of availability of fixed-lined backhaul at desired locations and due to cost saving reasons, operators may deploy a variety of backhaul technologies in a given network, combining available technologies such as fiber, xDSL, wireless backhaul and multi-hop mesh networks to backhaul small-cells. As a consequence, small-cells capacity may be non-uniform in the HetNet. Furthermore, some small-cells backhaul capacity may fluctuate if wireless backhaul is chosen. With such concerns in mind, a new network selection strategy considering small-cell backhaul capacity is proposed to ensure that users enjoy the best possible user experience especially in terms of connection throughput and fairness. The study compares performance of several common Network Selection Schemes (NSSs) such as WiFi First (WF) and Physical Data Rate (PDR) with the proposed Dynamic Backhaul Capacity Sensitive (DyBaCS) NSS in LTE-WiFi HetNets. The downlink performance of HetNet is evaluated in terms of average throughput per user and fairness among users. The effects of varying WiFi backhaul capacity form the focus for the evaluation. Results show that the DyBaCS scheme generally provides superior performance in terms of fairness and average throughput per user across the range of backhaul capacities considered.

Wireless Sensor Networks for Cattle Health Monitoring

This paper investigates an adaptation of Wireless Sensor Networks (WSNs) to cattle health monitoring. The proposed solution facilitates the requirement for continuously assessing the condition of individual animals, aggregating and reporting this data to the farm manager. There are several existing approaches to achieving animal monitoring, ranging from using a store and forward mechanism to employing GSM-based techniques; these approaches only provide sporadic information and introduce a considerable cost in staffing and physical hardware. The core of this solution overcomes the aforementioned drawbacks by using alternative cheap, low power consumption sensor nodes capable of providing real-time communication at a reasonable hardware cost. In this paper, both the hardware and software have been designed to provide real-time data from dairy cattle whilst conforming to the limitations associated with WSNs implementations.

Wireless sensor network for animal monitoring using both antenna and base-station diversity

Wireless sensor networks are widely used for condition monitoring applications. Much effort has been invested in improving the performance of such networks. Diversity is a well-proven technique in this context. Here, we present the practical application of base station and antenna diversity. Empirical measurements of performance in a realistic environment are reported and a statistical analysis of the resulting data is presented.

Wireless Sensor Networks for Beef and Dairy Herd Management

Abstract This paper reports on the application of wireless sensor technology to cattle monitoring. By monitoring and understanding cattle’s individual and herd behaviour, farmers can potentially identify the onset of illness, lameness or other conditions which might benefit from early intervention. Low cost sensor network platforms show considerable potential in this context but are faced with a number of significant technical challenges before they are widely and routinely adopted. This paper focuses on challenges that relate specifically to the backhaul of data from cattle mounted sensory devices including data protocols, power consumption, mobility, operational range, data transmission volumes and herd size. The optimization of a wireless communications platform based around the IEEE 802.15.4 standard protocol from the perspective of operational battery lifetime has been analysed as a function of daily download volume and herd size. Boundary conditions are presented according to battery life expectancy. Operational issues such as the length of time grazing animals spend within range of base stations are also reported.

Antenna and Base-Station Diversity for WSN Livestock Monitoring

Antenna and base-station diversity have been applied to a wireless sensor network for the monitoring of live-stock. A field trial has been described and the advantage to be gained in a practical environment has been assessed.

Application of reinforcement learning to wireless sensor networks: models and algorithms

Wireless sensor network (WSN) consists of a large number of sensors and sink nodes which are used to monitor events or environmental parameters, such as movement, temperature, humidity, etc. Reinforcement learning (RL) has been applied in a wide range of schemes in WSNs, such as cooperative communication, routing and rate control, so that the sensors and sink nodes are able to observe and carry out optimal actions on their respective operating environment for network and application performance enhancements. This article provides an extensive review on the application of RL to WSNs. This covers many components and features of RL, such as state, action and reward. This article presents how most schemes in WSNs have been approached using the traditional and enhanced RL models and algorithms. It also presents performance enhancements brought about by the RL algorithms, and open issues associated with the application of RL in WSNs. This article aims to establish a foundation in order to spark new research interests in this area. Our discussion has been presented in a tutorial manner so that it is comprehensive and applicable to readers outside the specialty of both RL and WSNs.