Work place: School of Computing, University of South Africa, Johannesburg, P/Bag X6, Florida, 1710, South Africa
Research Interests: Medical Informatics, Autonomic Computing, Computational Learning Theory, Distributed Computing, Data Structures and Algorithms, Mathematics of Computing
Ademola P. Abidoye received the Master’s degree from University of Ibadan, Ibadan, Nigeria, in 2006 and the Ph.D. degree from the University of the Western Cape, Cape Town, South Africa in 2015, both in Computer Science.
He is currently a Senior Lecturer in the Department of Computer Science, School of Computing, University of South Africa, Johannesburg, South Africa. He has attended both local and international conferences, as well published many papers in reputable international journals.
Dr. Abidoye is a member of Institute of Electrical and Electronics Engineers (IEEE), South African Institute of Computer Scientists and Information Technologists (SAICSIT), and Computer Professionals Registration Council of Nigeria (CPN). His research interests are in the areas of Distributed Computing, Machine Learning, Internet of Things (IoT), Cloud Computing, Cyber Security, Health Informatics, and Data Science.
DOI: https://doi.org/10.5815/ijitcs.2018.06.03, Pub. Date: 8 Jun. 2018
A new form of wireless sensor networks is emerging as an important component of the Internet of Things (IoT) where camera devices are interconnected and endowed with an IP address to form visual sensor networks. The applications of these networks span from smart parking systems in smart cities, video surveillance for security systems to healthcare monitoring and many others which are emerging from niche areas. The management of such sensor networks will require meeting a higher quality of service (QoS) constraints than demanded from traditional sensor networks. While many works have focused only on energy efficiency as a way of providing QoS in sensor networks, we consider a novel modelling approach where local optimizations implemented on the sensor nodes are translated into pheromone distribution used in ant colony optimization for path computation. We propose a routing protocol called the multipath ant colony optimization (MACO) that finds QoS-aware routing paths for the sensor readings from source nodes to the sink by relying on four local parameters: the link cost, the remaining energy of neighboring nodes, sensor nodes location information and the amount of data a neighbor node is currently processing. Finally, we propose an architecture for integrating sensor data with the cloud computing. Simulation results reveal the relative efficiency of the newly proposed approach compared to selected related routing protocols in terms of several QoS metrics. These include the network energy efficiency, delay and throughput.[...] Read more.
DOI: https://doi.org/10.5815/ijitcs.2018.04.04, Pub. Date: 8 Apr. 2018
Wireless sensor networks (WSNs) have become a popular research area that is widely gaining the attraction from both the researchers and the practitioner communities due to their wide area of applications. These include real time sensing for audio delivery, imaging, video streaming, environmental monitoring, industrial applications and remote monitoring. WSNs are constrained with limited energy due to their physical size. In order to maximize network lifetime, efficient use of limited sensor nodes energy resources is important. Energy efficient routing protocol for maximum lifetime in wireless sensor networks (EERPM) is proposed. Sensor nodes lifetime optimization models are formulated subject to energy consumption constraint, data flow conservation constraint, maximum data rate constraint and link capacity constraint. The models are used to solve mathematical models for the maximum lifetime routing problems. Sensor nodes transmit their data packets based on the link capacity that is inference free among the sets of links. Moreover, algorithms are developed for coverage of sensor nodes and maximization of lifetime for sensor nodes. Simulation results show that EERPM performs better than MLCS, MLCAL and AEEC protocols. It can reduce data gathering latency and achieve load balancing. Finally, the proposed method extends network lifetime compared to the related selected protocols.[...] Read more.
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