Hock Guan Goh

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.

Energy efficient routing for wireless sensor networks with grid topology

Agricultural monitoring using wireless sensor networks has gained much popularity recently. In this paper, we review five existing flat-tree routing algorithms and proposed a new algorithm suitable for applications such as paddy field monitoring using wireless sensor network. One of the popular data collection methods is the data aggregation approach, where sensor readings of several nodes are gathered and combined into a single packet at intermediate relay nodes. This approach decreases the number of packets flowing and minimizes the overall energy consumption of the sensor network. However, most studies in the past do not consider the network delay in this context, which is an essential performance measure in real-time interactive agricultural monitoring through Internet and cellular network. We propose an algorithm called Information Selection Branch Grow Algorithm (ISBG), which aims to optimize the network in achieving higher network lifetime and shortening the end-to-end network delay. The performance of this algorithm is assessed by computer simulation and is compared with the existing algorithms used for data aggregation routing in wireless sensor networks.

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.

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 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.

Performance study of tree-based routing algorithm for 2D grid wireless sensor networks

In this paper, we focus our study on energy-efficient routing algorithms. We study an algorithm called energy-efficient tree-based routing, which is based on a tree-like topology rooted at a fixed base station (sink node) of the network with a fixed 2-dimensional (2D) grid topology. The performance of this algorithm is assessed by computer simulation and is compared with other existing algorithms. Tree-based routing algorithm is found to achieve longer network lifetime, smaller end-to-end network delay, and has better robustness to failures when compared with other algorithms in the case where the base station is placed at the perimeter of the grid.

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.

A fast, adaptive, and energy-efficient multi-path-multi-channel data collection protocol for wireless sensor networks

Energy consumption, traffic adaptability, fast data collection, etc are the major issues in wireless sensor networks (WSNs). Most existing WSN protocols are able to handle one or two of the above issues with the other(s) being compromised. In order to reduce the energy consumption of wireless sensor nodes while having fast data collection under different traffic generating rates, this paper proposes a fast, adaptive, and energy-efficient multi-path-multi-channel (FAEM) data collection protocol. FAEM makes use of the Basketball Net Topology proposed in the literature, in which a multi-parent-multi-child connection table is pre-established at each node; each node is also pre-assigned a receiving channel which is different from those of the neighboring nodes so as to eliminate the transmission interference. During data transmission, time is divided into duty cycles, and each consists of two phases, namely distributed iterative scheduling phase and slot-based packet forwarding phase. The former is to match parents and children of the entire WSN in a distributed manner in order to determine whether a node should be in upload (to which parent), download (from which child), or sleep mode in a particular slot; while the latter is for nodes to take action according to the schedule. Simulation shows that our protocol is able to achieve lower energy consumption, data reliability and low latency even during a high traffic load.