Environmental Issues in Internet of Things: Challenges and Solutions

The Internet of Things (IoT) is an emerging technology which extends the boundaries of Internet to include a wide variety of devices. However, the technologies that facilitate its implementation come with some challenges. Its effect on the environment is one of these. To reflect the interest in this field, the paradigm of green IoT is used in research and practice. In this paper, we survey state-of-the-art technologies and applications in this new area. According to previous research, the IoT is a suite of technologies that enables a connection between millions of devices and sensors. These technologies mean that more resources are used and that there is more e-waste; however, it also leads to new possibilities to help the environment and society through natural disaster prevention. Each IoT technology brings benefits by reducing the negative effects of the activities for which it is used, and by using it directly in environmental protection. We investigate the challenges of and the solutions brought about by the essential components of the IoT on the environment, in accordance with these two fields of interest

Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review

Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of \emph{animal welfare} in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of \emph{smart computing and sensing} is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare

A Comprehensive Survey on Networking over TV White Spaces

The 2008 Federal Communication Commission (FCC) ruling in the United States opened up new opportunities for unlicensed operation in the TV white space spectrum. Networking protocols over the TV white spaces promise to subdue the shortcomings of existing short-range multi-hop wireless architectures and protocols by offering more availability, wider bandwidth, and longer-range communication. The TV white space protocols are the enabling technologies for sensing and monitoring, Internet-of-Things (IoT), wireless broadband access, real-time, smart and connected community, and smart utility applications. In this paper, we perform a retrospective review of the protocols that have been built over the last decade and also the new challenges and the directions for future work. To the best of our knowledge, this is the first comprehensive survey to present and compare existing networking protocols over the TV white spaces.Comment: 19 page

RFID-Based model for remote chicken monitoring: case of free-range chicken farming in Machakos County

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Computer-Based Information Systems (MSIS) at Strathmore UniversityPoultry farming is an important economic activity among Kenyan farmers. In order to earn income, they have to raise chicken and sell them once fully matured. This Indigenous chicken farming not only requires time investment but also money. However, security of chicken is an inevitability every farmer has to face. This is because, this category of chicken needs a free-range environment where it can move around, scratching for food, worms and other edibles. The chicken farmer faces challenges majorly theft of chicken by workers or strangers who randomly visit the farm. In this study, we developed a model that tracks chicken using RFID tags. This model enables a farmer to remotely monitor chicken from anywhere using the internet. The model constantly records and stores chicken data captured by the reader and updates this information in a database. The farmer can thereafter retrieve the tagged chicken information using a user interface designed to simplify the process of monitoring the chicken to minimise theft. The RFID model was designed to identify the chicken, each with its specific tag, containing particular information about it. To demonstrate this, an Arduino Uno microcontroller is set up with an encoded RFID system for chicken tracking. The RFID system uploads data sketches to a remote server via an Ethernet shield

Forest Observatory: a resource of integrated wildlife data

We propose the Forest Observatory, a linked datastore, to represent knowledge from wildlife data. It is a resource that semantically integrates data silos and presents them in a unified manner. This research focuses on the forest of the Lower Kinabatangan Wildlife Sanctuary (LKWS) in Sabah, Malaysian Borneo. In this region, wildlife research activities generate a variety of Internet of Things (IoT) data. However, due to the heterogeneity and isolation of such data (i.e., data created in different formats and stored in separate locations), extracting meaningful information is deemed time-consuming and labour-intense. One possible solution would be to integrate these data using semantic web technologies. As a result, data entities are transformed into a machine-readable format and can be accessed on a single display. This study created a semantic data model to integrate heterogeneous wildlife data. Our approach developed the Forest Observatory Ontology (FOO) to lay the foundation for the Forest Observatory. FOO modelled the IoT and wildlife concepts, established their relationships, and used these features to link historical datasets. We evaluated FOO’s structure and the Forest Observatory using pitfalls scanners and task-based methods. For the latter, a use case was assigned to the Forest Observatory, querying it before and after reasoning. The results demonstrated that our Forest Observatory provides precise and prompt responses to complex questions about wildlife. We hope our research will aid bioscientists and wildlife researchers in maximising the value of their digital data. The Forest Observatory can be expanded to include new data sources, replicated in various wildlife sanctuaries, and adapted to other domains

An energy-efficient routing protocol for Hybrid-RFID Sensor Network

Radio Frequency Identification (RFID) systems facilitate detection and identification of objects that are not easily detectable or distinguishable. However, they do not provide information about the condition of the objects they detect. Wireless sensor networks (WSNs), on the other hand provide information about the condition of the objects as well as the environment. The integration of these two technologies results in a new type of smart network where RFID-based components are combined with sensors. This research proposes an integration technique that combines conventional wireless sensor nodes, sensor-tags, hybrid RFID-sensor nodes and a base station into a smart network named Hybrid RFID-Sensor Network (HRSN)

The Validation of Novel Ecological Survey Methods for Use in Describing Harvest Mouse Micromys minutus Autecology

According to much of the literature relating to Micromys minutus (harvest mouse) the species has historically presented many challenges to researchers, particularly when attempting to collect sufficient data to describe their ecology, life history and responses to the ever-increasing threat of habitat loss and fragmentation. Methodological improvements are needed which provide sufficient species-specific data to underpin conservation and which are of sufficient quality to allow their movement ecology to be quantified. Here two novel methods were developed and tested, which included remote scent surveys using a detection dog and Radio Frequency Identification (RFID) trapping. After validation, RFID trapping was then used to quantify M. minutus movement in fragmented habitats. A preliminary study was carried out which assessed the ability of a dog to be trained to indicate the scent of M. minutus. Here positive reinforcement training methods were used and the dog’s effectiveness was evaluated in a training environment using scent samples collected from controlled and uncontrolled situations. Secondly, RFID trap effectiveness was compared to the results of live trapping. Data were maximised by releasing individually tagged M. minutus into a suitable semi-natural enclosure on the Moulton College estate. After validation a further release was undertaken to investigate M. minutus movement ecology. Here gaps of differing widths were incorporated into the release enclosures and movements between the habitat patches were measured. Individuals included in each release cohort were exposed to an Open Field Test prior to release, and thus, their behaviour in relation to trapping and movement was also assessed. There is strong evidence that a dog can be trained to detect M. minutus and discriminate their scent from other sympatric nontarget species in a controlled training environment. When applied to uncontrolled field situations, the remote scent survey proved more effective than nest search surveys by volunteers during the autumn months, providing preliminary evidence that olfactory indicators could be more efficient than visual clues when establishing presence of M. minutus. Additional validation in uncontrolled settings is still required. Encouraging results were also seen during validation of the use of RFID trapping with better results in terms of raw trapping rates over live trapping being observed. Furthermore, findings indicate that M. minutus have sufficient navigational and motion capacity to successfully move over gaps ≤2m, but gaps greater than 2m could limit their movement with possible implications for population persistence. The findings also suggest that individuals that explore more slowly may have an advantage when inhabiting a fragmented habitat. Thus, movement propensity is likely to be an individual behavioural trait and may vary across situations; this provides a novel perspective on their conservation and may support conservation decisions being based on behaviour rather than density. The data collected for this thesis demonstrates that progress has been made in terms of monitoring M. minutus and the findings presented are entirely novel for this species. Nevertheless, they remain a challenging species and more questions have been asked than can be answered within the thesis. However, the sum of this work has provided a clear direction for future research on M. minutus

Hybrid intelligent decision support system for distributed detection based on ad hoc integrated WSN & RFID

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe real time monitoring of environment context aware activities, based on distributed detection, is becoming a standard in public safety and service delivery in a wide range of domains (child and elderly care and supervision, logistics, circulation, and other). The safety of people, goods and premises depends on the prompt immediate reaction to potential hazards identified in real time, at an early stage to engage appropriate control actions. Effective emergency response can be supported only by available and acquired expertise or elaborate collaborative knowledge in the domain of distributed detection that include indoor sensing, tracking and localizing. This research proposes a hybrid conceptual multi-agent framework for the acquisition of collaborative knowledge in dynamic complex context aware environments for distributed detection. This framework has been applied for the design and development of a hybrid intelligent multi-agent decision system (HIDSS) that supports a decentralized active sensing, tracking and localizing strategy, and the deployment and configuration of smart detection devices associated to active sensor nodes wirelessly connected in a network topology to configure, deploy and control ad hoc wireless sensor networks (WSNs). This system, which is based on the interactive use of data, models and knowledge base, has been implemented to support fire detection and control access fusion functions aimed at elaborating: An integrated data model, grouping the building information data and WSN-RFID database, composed of the network configuration and captured data, A virtual layout configuration of the controlled premises, based on using a building information model, A knowledge-based support for the design of generic detection devices, A multi-criteria decision making model for generic detection devices distribution, ad hoc WSNs configuration, clustering and deployment, and Predictive data models for evacuation planning, and fire and evacuation simulation. An evaluation of the system prototype has been carried out to enrich information and knowledge fusion requirements and show the scope of the concepts used in data and process modelling. It has shown the practicability of hybrid solutions grouping generic homogeneous smart detection devices enhanced by heterogeneous support devices in their deployment, forming ad hoc networks that integrate WSNs and radio frequency identification (RFID) technology. The novelty in this work is the web-based support system architecture proposed in this framework that is based on the use of intelligent agent modelling and multi-agent systems, and the decoupling of the processes supporting the multi-sensor data fusion from those supporting different context applications. Although this decoupling is essential to appropriately distribute the different fusion functions, the integration of several dimensions of policy settings for the modelling of knowledge processes, and intelligent and pro-active decision making activities, requires the organisation of interactive fusion functions deployed upstream to a safety and emergency response.Saudi government, represented by the Ministry of Interior and General Directorate of Civil Defenc

Wildlife and environmental monitoring using RFID and WSN technology

Wireless Sensor Networks enable scientists to collect information about the environment with a granularity unseen before, while providing numerous challenges to software designers. Since sensor devices are often powered by small batteries, which take considerable effort to replace, it is of major importance to use energy carefully. We present two efficient ways of extending the lifetime of such systems: 1. an adaptive duty cycling protocol and 2. an adaptive data management protocol. Further, we present some details of our deployed sensor network in Wytham Woods, Oxfordshire