Browsing by Author "Sinde, Ramadhani S."

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Development and Testing of Adaptive Vehicle Speed Monitoring System integrated with Alcoholic Detector for Public Buses: A case of Tanzania
(International Journal of Computer Applications, 2015-10-07) Ramju, Farhan; Sinde, Ramadhani S.; Kaijage, Shubi
21 Development and Testing of Adaptive Vehicle Speed Monitoring System integrated with Alcoholic Detector for Public Buses: A case of Tanzania Farhan Ramju Department of Communication Science and Engineering Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania. Ramadhani S. Sinde Department of Communication Science and Engineering Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania. Shubi Kaijage Department of Communication Science and Engineering Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania. ABSTRACT Road accidents are the serious humanity and public health issue in Tanzania. The problem is increasing day by day. Apart from the loss of many lives, the effect of the road crashes on the country’s economy is massive. In Tanzania Human factors is the main contribution of major road crashes while over-speeding and drinking driving is one of the accelerating factors to the increase of road casualties. Existing measures to limit these problems have been unsuccessful to diminish the road accidents thus only use of handheld devices such as the speed radar gun and breath analyzer is applicable during inspection on the road or check points. Since these devices are not automatic in the sense that they would need to be operated manually by the Traffic Police, they lack the continuous monitoring of speed and therefore their efficiency in speed detection is low. To address these challenges, an adaptive vehicle speed monitoring system integrated with alcohol detector is utmost important. This chapter attempts to develop an effective solution for vehicle speed monitoring and alcohol detection on a real time basis. The main objective of this paper is to develop an adaptive vehicle speed monitoring integrated with an alcoholic detection system able to monitor the vehicle speed into defined speed limits and driver’s alcoholic content (Blood Alcoholic Content) during the journey on the road. The system consists of GPS module that measures the distance and calculates the accurate speed of moving objects and also provides a location in term of latitude and Longitude, sensor nodes to measure the level of alcoholic content through breath, Arduino controller also used to drive the operation of the system. The system is integrated with LCD display for the driver and GSM network to send the message to the database to be stored for future uses and constantly updating the law enforcers (traffic policies) on what is going on in the roads and take prompt action in case of misbehaving. The system will help most of traffic police in finding out driver’s behavior on the road and also public buses that are daily victims of road accidents results due to the Human factors.
Energy efficient wireless sensor network for monitoring temperature and relative humidity in forest
(NM-AIST, 2020-04) Sinde, Ramadhani S.
Monitoring the forest‟s weather has been essential to living things over the years. Currently, there is a shortage of information on real-time temporal and spatial environmental conditions of the forest that drive forest health condition. This work focuses on the sensing of humidity and temperature as weather data from the forest. Unlike the traditional systems used to collect weather information, the use of wireless sensor network (WSN) gives real-time data capture from every point of the forest. However, the WSN faces the number of challenges including low bandwidth, low power, and short battery lifespan. Reducing the network delay and improving the network lifetime are always big issues in the domain of WSN. To resolve these downsides, this work proposes an Energy Efficient Scheduling using Deep Reinforcement Learning (DRL) ( S-DRL) algorithm in WSN. S-DRL contributes three phases to prolong network lifetime and to reduce network delay that is: Clustering phase, Duty-Cycling phase and Routing phase. S-DRL starts with the clustering phase where it reduces the energy consumption incurred during data aggregation. It is achieved through Zone based Clustering (ZbC) scheme. In ZbC scheme, hybrid Particle Swarm Optimization (PSO) and Affinity Propagation (AP) algorithms are utilized. Duty cycling is adopted in the second phase by executing DRL algorithm. Here, the sensor node autonomously decides its sleep or wakeup time to transmit sensed data to the head node. From which, S-DRL reduces the energy consumption of individual sensor nodes effectually. The transmission delay is mitigated in third (routing) phase using Ant Colony Optimization (ACO) and FireFly Algorithm (FFA). S-DRL is modeled in the Network Simulator 3.26 (NS3) simulator. The results conquered are valuable in provisions of upcoming metrics including network lifetime, energy consumption, throughput and delay. From this evaluation, it is proved that E2S-DRL reduces energy consumption, delay up to 40% and enhances throughput and network lifetime up to 35% compared to the existing Time Division Multiple Access (cTDMA), Distributed Random Allocation (DRA) and improved Artificial bee colony (iABC) methods. The weather data will be stored in the database for further action. This study was conducted in the biodiversity-rich Usambara forest reserve in Tanzania. Timely collected environmental data will help other researchers to predict wildfire since the temperature rise can cause fire outbreak. Finally, to evaluate the performance of the proposed system using the following metrics namely network lifetime, energy consumption, throughput and delay.