Browsing by Author "Dogo, E.M."

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A Secure Electronic Voting System Using Multifactor Authentication and Blockchain Technologies.
(2022) Olaniyi, O.M.; Dogo, E.M.; Nuhu, B.K.; Treiblmaier, H.; Abdulsalam, Y.S.; Folawiyo, Z.
This chapter presents a distributed e-voting system that solves the problems of vote-rigging, voter impersonation, and vote falsification, all of which are prevalent in traditional paper ballot systems. In general, the digitization of democratic decision-making is convenient, fast, and cost-saving but can become a gateway for electoral fraud if not properly secured. Authentication and the simultaneous achievement of confidentiality, integrity, and availability represent major challenges toward establishing e-voting as a reliable means of democratic decision-making. In this chapter, a combination of multifactor authentication (MFA) and blockchain techniques is used to secure electronic voting. MFA hampers the compromising of voters’ identities and allows for easy verification, while blockchain technology protects the integrity of the votes and ensures the verifiability of the cast votes. Combining a facial recognition algorithm and RFID authenticates and authorizes voters to participate in the election process. A smart contract implemented on an Ethereum network provides the required measures of integrity and verifiability for secure e-voting. Performance evaluations of the proposed approach show that the MFA yielded a 0.1% false acceptance rate and a 0.8% false rejection rate for 100 voters, respectively. This illustrates that the proposed technique can solve issues of authentication and integrity, thereby paving the way for free, fair, and credible e-democratic decision-making in digitally enabled voting scenarios.
A survey of machine learning methods applied to anomaly detection on drinking-water quality data
(2019) Dogo, E.M.; Nwulu, N.I.; Twala, B.; Aigbavboa, C.O.
Traditional machine learning (ML) techniques such as support vector machine, logistic regression, and artificial neural network have been applied most frequently in water quality anomaly detection tasks. This paper presents a review of progress and advances made in detecting anomalies in water quality data using ML techniques. The review encompasses both traditional ML and deep learning (DL) approaches. Our findings indicate that: 1) Generally, DL approaches outperform traditional ML techniques in terms of feature learning accuracy and fewer false positive rates. However, it is difficult to make a fair comparison between studies because of different datasets, models and parameters employed. 2) We notice that despite advances made and the advantages of the extreme learning machine (ELM), its application is sparsely exploited in this domain. This study also proposes a hybrid DL-ELM framework as a possible solution that could be investigated further and used to detect anomalies in water quality data.
Combating Road Traffic Congestion with Big Data: A Bibliometric Review and Analysis of Scientific Research
(Springer, 2021) Dogo, E.M.; Makaba, T.; Afolabi, O.J.; Ajibo, A.C.
Road traffic congestion is one of the challenging problems confronting city dwellers globally. It is majorly caused by either one or a combination of recurrent congestion, nonrecurrent congestion, and precongestion conditions in urban road networks. This chapter performs a bibliometric analysis and reviews the volume of literature linking big data with combating road traffic congestion between 2011 and 2020. The review employs a quantitative analysis of bibliometric science mapping tool to highlight features that affect knowledge accumulation. The chapter also reviews the intellectual structure of knowledge based on total publications and citations. The key scholars, documents, affiliations, regions, data, and algorithms that influenced the development of this research area are analyzed. The results of documents co-citation evaluation show that the key research clusters are salient elements linked with the development and deployment of connected and autonomous vehicles (CAVs) technology. These research clusters are traffic flow prediction, congestion and accidents alert systems, security and privacy mitigation, vehicle emission profiles, travel time estimation, optimization of vehicular routing, journey planning and congestion prediction, and travel and parking guidance. Finally, the chapter presents the way forward and future research direction for sustainable road traffic management in the context of smart city initiatives leveraging on big data.
Combating Road Traffic Congestion with Big Data: A Bibliometric Review and Analysis of Scientific Research
(Springer, 2021) Dogo, E.M.; Makaba, T.; Afolabi, O.J.; Ajibo, A.C.
Road traffic congestion is one of the challenging problems confronting city dwellers globally. It is majorly caused by either one or a combination of recurrent congestion, nonrecurrent congestion, and pre congestion conditions in urban road networks. This chapter performs a bibliometric analysis and reviews the volume of literature linking big data with combating road traffic congestion between 2011 and 2020. The review employs a quantitative analysis of bibliometric science mapping tool to highlight features that affect knowledge accumulation. The chapter also reviews the intellectual structure of knowledge based on total publications and citations. The key scholars, documents, affiliations, regions, data, and algorithms that influenced the development of this research area are analyzed. The results of documents co-citation evaluation show that the key research clusters are salient elements linked with the development and deployment of connected and autonomous vehicles (CAVs) technology. These research clusters are traffic flow prediction, congestion and accidents alert systems, security and privacy mitigation, vehicle emission profiles, travel time estimation, optimization of vehicular routing, journey planning and congestion prediction, and travel and parking guidance. Finally, the chapter presents the way forward and future research direction for sustainable road traffic management in the context of smart city initiatives leveraging on big data.
Evaluative analysis of next generation mobile networks in future smart grid in developing countries
(ACM, 2019) Dogo, E.M.; Salami, A. F.; Nwulu, N.I.
Smart grid (SG) promises an efficient system that provides effective monitoring, timely statuses and vital automation capabilities across generation, transmission and distribution spectrum of the power grid. SG applications have stringent and unique latency and bandwidth requirements. 4G LTE and the evolving 5G promises to support a wide range of applications in the SG. This research analyses the performance of 4G LTE and 5G networks in supporting big data analytics for SG in developing countries. In order to evaluate the technical practicality of using wireless cellular networks provided by local mobile operators, a comparative analysis of three 4G LTE networks operators on wide area network (WAN) application and an emulated future 5G networks is carried out in Nigeria. The latency (λ), throughput (τ) and packet loss rate (ψ) for the three local networks providers (denoted as OP1, OP2, OP3), and the emulated 5G networks (denoted as EFN1, EFN2, EFN3) were used for the experimentation using OMNET++ simulation tool. The obtained results indicate that OP2 is a better choice for WAN SG applications when the communication radius is below 1600m and OP3 is a preferable choice when communication radius grows up to 2400m and beyond. While on the other hand, the results obtained for the 5G networks show that, on average, EFN2 is a better choice for WAN SG applications when the communication radius is lesser than 1600m and EFN3 is a preferable choice when the communication radius extend beyond 2400m.
Paillier Cryptosystem Based ChainNode for Secure Electronic Voting
(Frontiers in Blockchain, 2022) Umar, B,U.; Olaniyi, O.M.; Olajide, D.O.; Dogo, E.M.
Blockchain is a distributed and decentralized ledger of transactions that are linked together cryptographically leading to immutability and tamper-resistance, thereby ensuring the integrity of data. Due to the ability of blockchain to guarantee the integrity of data, it has found wide-range adoption in electronic voting (e-voting) systems in recent years, this is in a bid to prevent manipulation of votes. However, due to the distributed nature of the blockchain, opportunities arise for privacy intrusion of the data being secured. The translation of this privacy flaw in blockchain to e-voting systems is the possibility of violation of the privacy of the electorates. Consequently, in a bid to achieve integrity and privacy of votes in e-voting, this study presents the use of an open-source blockchain system, coupled with a privacy-oriented cryptosystem known as the Paillier cryptosystem, towards addressing the privacy concerns of the blockchain. The performance of the system was evaluated and a transaction throughput of 1424 tps was obtained for ten thousand simulated ballot transactions. Further evaluation was carried out on the system, by increasing the number of system transactions. This showed that the mining time of the blockchain increased by an average factor of 0.18 s for every thousand increases in the number of transactions. Also, the response time of the system to a range of user actions was evaluated over an increasing number of voters. Results obtained showed that the response time of the system for vote casting operations increased by an average of 0.33 min per thousand voters while for vote tallying there was an increase in response time by an average of 0.848 min per thousand voters. The scientific value of this study is the development of an integrity and privacy-preserving e-voting system consisting of an open-source nodechain coupled with a privacy-oriented cryptosystem known as the Paillier cryptosystem following the security requirements of e-voting systems. The proposed system addresses the issue of integrity in e-voting while still maintaining the privacy of the electorates.
The Design and Performance Evaluation of a Wireless Sensor Network Based Irrigation System on Different Soil Types
(2021) Umar, B. U.; Dogo, E.M.; Nuhu, B. K.; Haq, A. K.; Olaleye, P. T.
In the Nigerian economy, agriculture plays a very important role, and most of its people depend on it for their livelihood. Agricultural practices in the country are still mainly based on conventional, traditional methods of farming which usually results in wastage of water resources and low production of crops to meet the country's demand. There is a need to transform farming from the traditional way to a more efficient method with optimum water utilization. Irrigation is an assistive measure to salvage the problem of inadequate water for dry season farming. Irrigation consumes a lot of water, time and must be done on a timely basis. The automated irrigation system helps to curb the problem of overwatering and under watering of the land. This research proposed an Arduino-based smart irrigation system using a wireless sensor network to overcome the problem of overwatering, underwatering, and efficient time utilization in farming. The system is implemented using Arduino IDE, Proteus Simulation Tools, and Blynk Platform. The effect of the four-mobile network: MTN, GLO, Airtel and 9mobile on response time for Gidan- Kwano area was evaluated. Testing carried out on the system resulted in a response time of 0.75 seconds for Glo 2G network and 0.45 seconds for Glo 4G network. Less than 1sec in the worst-case scenario. This makes the system effective in terms of time response, thereby eradicate the waste of time that manual system operation poised to irrigation scheduling. Also, the appropriate soil moisture content is maintained, whether it rains or not. This reduces excesses and ensures healthy plant growth, increasing agricultural productivity, and cultivating crops are made possible throughout the year. The system also will help in driving agricultural innovation through the use of IoT.
Toward Sustainable Domestication of Smart IoT Mobility Solutions for the Visually Impaired Persons in Africa
(Springer, 2020) Salami, A.F.; Dogo, E.M.; Nwulu, N.I.; Paul, B.S.
According to World Health Organization (WHO) estimates, Africa accounts for 10% of the global visually impaired persons (VIPs). This visual impairment burden is exacerbated by the shortage of specialist medical human resources, orientation and mobility specialists, and high cost of assessing primary eye care services. These render the majority of VIPs to rely heavily on human-assisted guides and ineffective navigation aids for their daily routines and movements. A viable technological solution that can fill this void and meet these mobility needs is the Internet of Things (IoT). This chapter provides an assessment of smart IoT mobility solutions pertinent to the African context. Furthermore, the barriers to the realization of technology domestication as well as growth catalysts are examined. Lastly, this chapter proffers technical recommendations for sustainable domestication of smart IoT mobility solutions for VIPs in Africa.