The Internet of Things (IoT) based Smart city applications are the latest technology-driven solutions designed to collect and analyze data to enhance the quality of life for urban residents by creating more sustainable, efficient, and connected communities. Communication nodes are networked independently to monitor the circumstances, where they require being more energy-efficient and securing, to improve the performance of sustainable cities. Because, the enormous number of networked devices makes the smart cities application vulnerable to many security breaches, all of which have serious ramifications for a city and its residents’ safety, well-being, and economic development. Low-powered sensors have limitations in terms of battery life, short transmission range, and security considerations, despite the fact that the combination of edge computing and Green IoT considerably enhances network performance in terms of processing and data storage. Consequently, it is necessary to implement an advanced approach to provide energy resources with secure data transmission for sustainable cities. Therefore, this research proposes an Intelligent Buffalo-based Secure Edge-enabled Computing (IB-SEC) framework for smart cities by IoT platform, which aims to enhance communication efficiency, and reliability, with minimizing latency in terms of energy consumption and data security for data transmission. The developed IB-SEC platform utilizes a combination of the African Buffalo Optimization (ABO) algorithm and a Distributed Hash function-based security algorithm to enhance the efficiency, security, and reliability of data transmission in IoT-based smart city networks. This platform leverages the capabilities of secure edge computing and MAC protocols to achieve its goals, by implementing encryption, authentication, and access control mechanisms that ensure the wireless communication is secure, and the data is protected against unauthorized access. Overall, the IB-SEC framework provides an advanced approach to secure IoT networks and enable smart city applications. Moreover, the developed platform enables the integration of heterogeneous edge devices, sensors, and systems which is effectively managed and adapted by Median Access Control (MAC) protocols. The IB-SEC is implemented in MATLAB and validated through cutting-edge security algorithms in terms of improved security, reliability, throughput, and reduced latency and energy consumption.

The charging loads of electric vehicles (EVs) at residential premises are controlled through a tariff system based on fixed timing. The conventional tariff system presents the herding issue, such as with many connected EVs, all of them are directed to charge during the same off-peak period, which results in overloading the power grid and high charging costs. Besides, the random nature of EV users restricts them from following fixed charging times. Consequently, the real-time pricing scenarios are natural and can support optimizing the charging load and cost for EV users. This paper aims to develop charging cost optimization algorithm (CCOA) for residential charging of EVs. The proposed CCOA coordinates the charging of EVs by heuristically learning the real-time price pattern and the EV’s information, such as the battery size, current state-of-charge, and arrival & departure times. In contrast to the holistic price, the CCOA determines a threshold price value for each arrival and departure sequence of EVs and accordingly coordinates the charging process with optimizing the cost at each scheduling period. The charging cost is captured at the end of each charging activity and the cumulative cost is calculated until the battery’s desired capacity. Various charging scenarios for individual and aggregated EVs with random arrival sequences of EVs against the real-time price pattern are simulated through MATLAB. The simulation results show that the proposed algorithm outperforms with a low charging cost while avoiding the overloading of the grid compared to the conventional uncoordinated, flat-rate, and time-of-use systems.

Information and telecommunication technologies are considered significant for the economic and social development of isolated and remote areas. Internet technologies create links between urban and rural areas that can overcome the barrier of distance that typically hinders development. In rural areas of Pakistan, Internet penetration is low due to high cost, lack of manageable infrastructure, public issues and many other factors that disturb sustainable Internet provision; however, the choice of access to technology in rural areas is a complex process. In this paper, we used the Analytic Hierarchy Process (AHP) method, which is a powerful multi-criteria decision-making (MCDM) approach and is a structured procedure for organizing and analyzing complex decisions. Furthermore, we proposed an AHP model for the selection of an ideal Internet access technology, which would facilitate access to Internet services to the 61% of the population who live in Pakistan’s rural areas. Data were collected from 38 respondents, from both academia and the telecom sector IT professionals. The relative weights of each factor and technology alternative were synthesized with Expert Choice 2000. This paper can provide comprehensive recommendations to telecommunication policymakers for Internet deployment and the selection of optimal Internet access technology in rural Pakistan.