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Wireless Sensor Network (WSN)-based Automatic Weather Stations (AWSs) perform automatic collection and transmission of weather data. These AWSs face challenges, which lower their performance. Hence, a need for regular monitoring to reduce down time. We propose condition monitoring, comprised of a data receiver, analyser, problem classifier and reporter and visualizer, to mine data relationships, identify possible causes of problems and perform reporting of AWS status. The data receiver uses an M/M/1/k queuing model. We use Successive Pairwise REcord Differences (SPREDs) algorithm to compare arrival rates and packet content so as to establish sensor, node and AWS level performance. We also perform a hybrid of Grubb outlier detection and correlations amongst related variables for data validation. Problems take on one of four states. One connection can receive data at a rate as low as 1ms, without loss while problem identification especially in high density network is improved

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The development of perpetually powered sensor networks for environment monitoring to avoid periodic battery replacement and to ensure the network never goes offline due to power is one of the primary goals in sensor network design. In many environment-monitoring applications, the sensor network is internet-connected, making the energy budget high because data must be transmitted regularly to a server through an uplink device. Determining the optimal solar panel size that will deliver sufficient energy to the sensor network in a given period is therefore of primary importance. The traditional technique of sizing solar photovoltaic (PV) panels is based on balancing the solar panel power rating and expected hours of radiation in a given area with the load wattage and hours of use. However, factors like the azimuth and tilt angles of alignment, operating temperature, dust accumulation, intermittent sunshine and seasonal effects influencing the duration of maximum radiation in a day all reduce the expected power output and cause this technique to greatly underestimate the required solar panel size. The majority of these factors are outside the scope of human control and must be therefore be budgeted for using an error factor. Determining of the magnitude of the error factor to use is crucial to prevent not only undersizing the panel, but also to prevent oversizing which will increase the cost of operationalizing the sensor network. But modeling error factors when there are many parameters to consider is not trivial. Equally importantly, the concept of microclimate may cause any two nodes of similar specifications to have very different power performance when located in the same climatological zone. There is then a need to change the solar panel sizing philosophy for these systems. This paper proposed the use of actual observed solar radiation and battery state of charge data in a realistic WSN-based automatic weather station in an outdoor uncontrolled environment. We then develop two mathematical models that can be used to determine the required minimum solar PV wattage that will ensure that the battery stays above a given threshold given the weather patterns of the area. The predicted and observed battery state of charge values have correlations of 0.844 and 0.935 and exhibit Root Mean Square Errors of 9.2% and 1.7% for the discrete calculus model and the transfer function estimation (TFE) model respectively. The results show that the models perform very well in state of charge prediction and subsequent determination of ideal solar panel rating for sensor networks used in environment monitoring applications.

The changing environment, climate, and the increasing manifestation of disasters, has generated an increased demand for accurate and timely weather information. This information is provided by the National meteorological authorities (NMAs) through different dissemination channels e.g., using radios, Televisions, emails among others. The use of ICTs to provide weather information is recently gaining popularity. A study was conducted in three countries, namely Nigeria, Uganda, and South Sudan to assess the efficiency of an ICT tool, known as “Weather Information Dissemination System”. The study involved 254 participants (Uganda: 71; South Sudan: 133; and Nigeria: 50). The collected primary data were first quality controlled and organized thematically for detailed analysis. Descriptive statistics was used to provide quantitative analysis as well as content scrutinized for qualitative analysis. The results showed that there is a need for timely weather information to plan farming activities such as planting and application of fertilizers and pesticides as well as to manage flood and drought by the water sector and disaster management. Results further showed that the majority of the respondents have access to the technology needed to access weather and climate information. The respondents who received weather information from NMAs noted that the forecast was good. However, they further noted that there is more room for improvement especially with making the forecasts location-specific, ensuring mobile access is adequate in all regions, provision of weather information by SMS (in countries where this service is currently unavailable) and improved timing of the weather information. Finally, uncertainty about the accuracy of weather information and the weather information not meeting specific needs are key barriers to people’s willingness to pay for it (Uganda: 33.3%; South Sudan: 46.1%; and Nigeria: 33.3%). Improved collaborations between the NMAs, ICT service providers, policymakers and farmers will facilitate an effective approach to weather information access and dissemination. Innovative sensitization approaches through the media houses will enable better understanding of weather products and utilization, and access to enabling ICTs would increase access to weather forecasts

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