ALBERT

Description: To maximize the limited spectrum among primary users and cognitive Internet of Things (IoT) users as we save the limited power and energy resources available, there is a need to optimize network resources. Whereas it is quite complex to study the impact of transmission rate, transmission power or transmission delay alone, the complexity is aggravated by the simultaneous consideration of all these three variables jointly in addition to a channel selection variable, since it creates a non-convex problem. Our objective is to jointly optimize the three major variables; transmission power, rate and delay under constraints of Bit Error Rate (BER), interference and other channel limitations. We analyze how total power, rate and delay vary with packet size, network size, BER and interference. The resulting problem is solved using a branch-and-cut polyhedral approach. For simulation of results, we use MATLAB together with the state-of-the-art BARON software. It is observed that an increase in packet size generally leads to an increase in total rate, total power and total transmission delay. It is also observed that increasing the number of secondary users on the channel generally leads to an increased power, delay and rate.

Description: 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.

Description: The changing climate has negatively impacted food systems by affecting rainfall patterns and leading to drought, flooding, and higher temperatures which reduce food production. This study examined the ability of communities to cope with food insecurity due to the changing climate in the Serere and Buyende districts, which are two different agro-ecological zones of Uganda. We administered 806 questionnaires to households, a sample size which was determined using Yamane’s formula, with the snowball sampling method used to select the households. The questionnaire sought information, including that regarding the respondents’ resources, the effects of climate change on households, and the coping mechanisms employed to reduce the impact of climate change on food security. The data collected was coded and analyzed using the statistical package for the social sciences (SPSS). Agriculture was found to be the main source of income for 42.4% of male adults and 41.2% of female adults in Serere. In Buyende, 39.9% of males and 33.7% of females rely on selling animal, poultry, and food crops. Aggregate results further showed that 58.3% of females and 42.2% of the males from both districts had suffered from the impacts of climate change, and that the effects were more evident between March and May, when communities experienced crop failure. The study further found that the percentage of households who had three meals a day was reduced from 59.7% to 43.6%, while the number of households with no major meals a day increased from 1.3% to 1.6%. We also found that 34.3% of households reported buying food during periods of crop failure or food scarcity. Moreover, despite reporting an understanding of several coping mechanisms, many households were limited in their ability to implement the coping mechanisms by their low incomes. This reinforced their reliance on affordable mechanisms, such as growing drought-resistant crops (32.7%), rearing drought-resistant livestock breeds (26.1%), and reducing the number of meals a day (14.5%), which are mechanisms that are insufficient for solving all the climate-related food insecurity challenges. We recommend that the government intervenes by revising policies which help farmers cope with the negative effects of climate change, promoting the sensitization of farmers to employing the coping mechanisms, and subsidizing agricultural inputs, such as resistant varieties of crops, for all to afford.