The enhanced distributed channel access (EDCA) protocol is a supplement to IEEE 802.11 medium access control (MAC), ratified by IEEE 802.11e task group to support quality of service (QoS) requirements of both data and real-time applications. Previous research show that it supports priority scheme for multimedia traffic but strict QoS is not guaranteed. This can be attributed to inappropriate tuning of the medium access parameters. Thus, an in-depth analysis of the EDCA protocol and ways of tuning medium access parameters to improve QoS requirements for multimedia traffic is presented in this work. An EDCA model was developed and simulated using MATLAB to assess the effect of differentiating contention window (CW) and arbitration inter-frame space (AIFS) of different traffic on QoS parameters. The optimal performance, delay, and maximum sustainable throughput for each traffic type were computed under saturation load. Insight shows that traffic with higher priority values acquired most of the available channels and starved traffic with lower priority values. The AIFS has more influence on the QoS of EDCA protocol. It was also observed that small CW values generate higher packet drops and collision rate probability. Thus, EDCA protocol provides mechanism for service differentiation which strongly depends on channel access parameters: CW sizes and AIFS.

Despite their infrequency, natural disasters like hurricanes, tornadoes, and floods pose significant threats to power systems, with profound economic impacts on nations and individuals. This paper delves into enhancing power system resilience against such disruptions through techniques such as network reconfiguration, defensive isolation, and targeted strengthening. A critical factor in power system failures is cyber-attacks, which exploit the integration of sensors, monitoring tools, and communication devices. To bolster system defenses against cyber threats, strategies like intrusion prevention systems, multiple layers of defense, and prompt security responses are implemented. Additionally, ageing infrastructure and human error contribute significantly to system failures, underscoring the importance of error prevention and maintenance of aging components. This paper conducts a comprehensive analysis of the grid's advanced automation systems, reviews several case studies, and proposes solutions to mitigate the challenges posed by these threats.

This work presents a new all-inclusive mathematical model that combines both processes and an energy analysis of a semi-automated biomass-powered multipurpose dryer. A mathematical model was developed and a wood sample was used to simulate the model. Energy interaction between the system and sample was established. Most importantly, the incorporation of a sensor control system ensures that, once there is an increase in thermal energy from the combustion of the biomass, a signal is passed to the temperature sensor module that controls the system’s temperature and hence shuts down the heat supply at a predetermined temperature; in this case, at 67 °C. The results of the system’s modification show that the peak temperature of the drying space and the sample was 67 °C and 56 °C, respectively, and that the maximum temperature lag witnessed by the two regimes was 10 °C. The peak temperature removal rate of the sample was 0.0066 kg/h, while the sample attained 0.4 (40%) moisture concentration of its initial value; 90% mass content removal (10% remaining mass content) of the initial mass of the sample was achieved at the end, with a simulation time of 240 s.

Improved channel quality indicator estimation using extended Kalman filter in LTE networks under diverse mobility models

The majority of today's farming challenges cannot be addressed by traditional farming methods, which is why it is critical to develop an affordable, energy‐efficient system that can increase agricultural productivity, operate in a monitored environment, and handle a variety of challenges related to climate, pests, and other issues. Using greenhouses is one possible way to achieve most of these. Previous studies on greenhouse have not carried out a smart climate conditions monitoring and prediction approach with a mathematical modeling technique for optimized and best system designs and operations. This study aims to address this by using sensors to control the climate in a way that is adaptable to various crops and numerical modeling of an optimized greenhouse system. To achieve this, humidity and optical sensors were integrated into the system to regulate the ambient conditions effectively. Results show a direct correlation between solar radiation and temperature. The imposed peak solar radiation and ambient temperature values of 1100 W/m ² and 37.3°C were used for the input variables. For over a 72‐h period, the temperature absorbed inside the glass (59°C) was observed to be higher than that of the room (38°C) with their respective peak accumulated energy values of 119 and 92 kJ for glazing and smart room, respectively. The peak thermal efficiencies recorded during mid‐day for the glazing and the smart rooms were 68% and 47%, respectively.