No abstract

There is rapid interest growing in the use of smart, connected devices. The developing world market for smart technology is evolving to adopt and adapt to the interconnected world of devices leading to the Internet of Things (IoT) everywhere. This research paper presents the design, development, and deployment of a prototype for the secure wireless home automation system with OpenHAB 2. We employed the use of two (2) high-performance microcontrollers, namely, the Arduino Mega 2560, interfaced with a 16-channel relay, and Raspberry Pi Model B, running the OpenHAB software. The Raspberry Pi functioned as the server to develop a prototype of an automated smart home that is remotely controllable from both a web application and an Android mobile app. In designing a wireless controlled switch for home appliances, two security procedures were implemented, namely, the token-based JSON Web Token (JWT) interface and Advanced Encryption Standard (AES) procedures for authentication and data encryption. Our system delivered a home automation system that leverages on the power of the latest version of OpenHAB to maximize productivity and overall home security while making it adaptable to the management of individual devices. When tested, both the developed hardware and software modules performed extremely well to meet the goal of a secured home automation system. Industry-standard penetration testing tools and frameworks, including Aircrack-ng, were utilized; wireless network audit began with a full sweep of the wireless frequencies with excellent results. It also ensures the efficient use of energy in the home as devices are intelligently controlled from both mobile and web applications. The results of the design and implementation of the additional layer for the security of the OpenHAB framework provide various theoretical and practical implications for home automation.

In 2011, the Namibian parliament presented and promulgated the Namibian Spatial Data Infrastructure (NamSDI) with the aim of promoting the sharing and improved access and use of geospatial data and services across Namibia. Notable SDI models, developed from the enterprise, information and computational viewpoints of the Reference Model for Open Distributed Processing (RM-ODP), comprise direct and indirect roles of stakeholders and special cases of each general role in an SDI. Hence, the International Cartographic Association (ICA) model was used to identify the stakeholders in and around NamSDI, which is still at the infancy stage of development. The application of a high-level ICA model proved to be relevant and useful in discriminating and categorizing Nam-SDI stakeholders according to their roles and vested interests. Some stakeholders, such as official government mapping agencies, assume multiple roles, while others, such as database administrators, are not yet active. In the absence of baseline data and given the infancy status of NamSDI, attributes such as skills, capacity of producers and service providers, were not considered. Modelling NamSDI stakeholders in the context of ICA's stakeholder model contributed significantly to a better understanding of NamSDI stakeholder types and subtypes and pointed out gaps that may hinder its successful and effective implementation.

No abstract

No abstract

Continuous use of agricultural land without periodic assessment of its suitability or performance for the cultivation of a specific crop could degrade soil fertility and compromise the long-term sustainability of the land to support production. Our aim is to use an integrated approach to assess agricultural land suitability in a small-scale farming system in the semi-arid region of northern Ghana, identify limiting factors for optimum crop production, and recommend intervention options towards sustainable farm management. We developed a data-driven model for land suitability analysis based on the Generalized Additive Models (GAM) approach. We validated the model with the actual yield data for six food crops (maize, pepper, yam, rice, peanut, and cowpea) under various biological, physical and chemical soil conditions across six communities. The result showed that the farmlands across the communities were highly suitable for maize and pepper but not suitable for cowpea. A qualitative validation method based on the contingency table showed the accuracy percentage of 84–100% for POD (Probability of Detection) and 6–21% for FAR (False alarm ratio) for all crops type. Hence, our model could be considered excellent to predict land suitability for different crop types. We recommend that stakeholders in the agricultural value chain should collaborate to develop low-cost and effective means of helping farmers determine the suitability of soils for specific crops to ensure that farmlands are not of depleted nutrients. In addition, periodic farmer training on appropriate farm management practices, including the right use of fertilizers, is needed.

Continuous use of agricultural land without periodic assessment of its suitability or performance for the cultivation of a specific crop could degrade soil fertility and compromise the long-term sustainability of the land to support production. Our aim is to use an integrated approach to assess agricultural land suitability in a small-scale farming system in the semi-arid region of northern Ghana, identify limiting factors for optimum crop production, and recommend intervention options towards sustainable farm management. We developed a data-driven model for land suitability analysis based on the Generalized Additive Models (GAM) approach. We validated the model with the actual yield data for six food crops (maize, pepper, yam, rice, peanut, and cowpea) under various biological, physical and chemical soil conditions across six communities. The result showed that the farmlands across the communities were highly suitable for maize and pepper but not suitable for cowpea. A qualitative validation method based on the contingency table showed the accuracy percentage of 84–100% for POD (Probability of Detection) and 6–21% for FAR (False alarm ratio) for all crops type. Hence, our model could be considered excellent to predict land suitability for different crop types. We recommend that stakeholders in the agricultural value chain should collaborate to develop low-cost and effective means of helping farmers determine the suitability of soils for specific crops to ensure that farmlands are not of depleted nutrients. In addition, periodic farmer training on appropriate farm management practices, including the right use of fertilizers, is needed.

No abstract