Water is a critical and indispensable resource for the sustainability of life, economic development, and the environment. According to the United Nations (UN) estimates, 70% of the world’s population will live in cities by the year 2025, and the current centralized piped infrastructure relied upon by water utilities will be inadequate. Leveraging on the advancement in emerging blockchain, Internet of Things (IoT), and sensor technologies offers a means for efficient water management. In this era of Fourth Industrial Revolution (4IR), human creativity will be a critical requirement in this regard. This chapter explores the impact of blockchain and IoT on water management and examines the feasibility of its adoption in multiple case scenarios and instances such as stormwater management, water quality monitoring and reporting directly to consumers and other relevant stakeholders, and smart payment and contract, in order to sustainably deal with the challenges of global water crisis induced by climate change and rapid population growth. This chapter makes special and unique emphasis on the relevance of the research through an African perspective and view. Furthermore, the technical advantages, socioeconomic gains, and technological benefits of synergizing blockchain and IoT such as enhanced security and transparency, reduced operational cost, overall efficiency, and other merits are expatiated.

All control systems suffer from problems related to undesirable overshoot, longer settling times and vibrations while going form one state to another state. Most of relevant techniques had been in the form of suggesting modification and improvement in the instrumentation or interfacing part of the control system and the results reported, remain suffering from shortcomings related to hardware parameter dependence and maintenance and operational complexities. Present study was based on a software approach which was focusing on an algorithmic approach for programming a PIC16F877A microcontroller, for eliminating altogether the parametric dependence issues while adding the benefits of easier modification to suit a given control system to changing operational conditions. Said approach was first simulated using MATLAB/SIMULINK using the techniques of Proportional Derivative Fuzzy Logic Controller (PD-FLC) whose membership function, fuzzy logic rules and scaling gains were optimized by the genetic algorithm technique. Simulated results were verified by programming the PIC16F877A microcontroller with the algorithm and using it on a temperature control system where a fan was regulated in response to variations in the ambient system temperature. Resulting tabulated performance indices showed a considerable improvement in rising and settling time besides reducing overshoot and steady state error.

This paper addresses coverage loss and rapid energy depletion issues for wireless livestock sensor networks by proposing a UAV-based energy-efficient reconfigurable routing (UBER) scheme for smart wireless livestock sensor networking applications. This routing scheme relies on a dynamic residual energy thresholding strategy, robust cluster-to-UAV link formation, and UAV-assisted network coverage and recovery mechanism. The performance of UBER was evaluated using low, normal and high UAV altitude scenarios. Performance metrics employed for this analysis are network stability (NST), load balancing ratio (LBR), and topology fluctuation effect ratio (TFER). Obtained results demonstrated that operating with a UAV altitude of 230 m yields gains of 31.58%, 61.67%, and 75.57% for NST, LBR, and TFER, respectively. A comparative performance evaluation of UBER was carried out with respect to hybrid heterogeneous routing (HYBRID) and mobile sink using directional virtual coordinate routing (MS-DVCR). The performance indicators employed for this comparative analysis are energy consumption (ENC), network coverage (COV), received packets (RPK), SN failures detected (SNFD), route failures detected (RFD), routing overhead (ROH), and end-to-end delay (ETE). With regard to the best-obtained results, UBER recorded performance gains of 46.48%, 47.33%, 15.68%, 19.78%, 46.44%, 29.38%, and 58.56% over HYBRID and MS-DVCR in terms of ENC, COV, RPK, SNFD, RFD, ROH, and ETE, respectively. The results obtained demonstrated that the UBER scheme is highly efficient with competitive performance against the benchmarked CBR schemes.

Cloud computing is an evolving and new way of delivering computing services and resources over the internet which are managed by third parties at remote sites. Cloud computing is based on existing technologies like web services, Service Oriented Architecture (SOA), web3.0, grid computing and virtualization, etc. Computing services includes data storage, processing and software. Cloud computing is enjoying a lot of buzz in Nigeria due to its perceived economic and operational benefits and stakeholders believe that it will transform the IT industry in Nigeria. Despite all its promises there still exist so many challenges before Cloud computing see the light of the day in Nigeria. This paper delivers an overview of Cloud computing together with its advantages and disadvantages. Thereafter, the challenges and drivers affecting the adoption of Cloud computing in Nigeria are outlined. Finally, recommendations for the adoption of Cloud computing is discussed with Nigeria as a case study.

This paper presents the impact of utilizing a biased energy distribution (BED) scheme for clustering sensor networks. In clustering sensor networks, some of the nodes are elected as aggregators and they compress the data from their cluster members before sending the aggregated data to the sink. Existing clustering routing protocols assume that all the nodes are provided with equal amount of energy but this shortens the network lifetime and makes the network unstable. This paper proposes a solution prioritizing the network into higher and lower energy nodes. The aim of this approach is to ensure well balanced energy consumption in order to maximize network lifetime. It is shown by simulation that the proposed technique exhibits better performance when compared to existing clustering routing techniques in terms of throughput, network lifetime and energy consumption.