Over the recent era, Wireless Sensor Network (WSN) has attracted much attention among industrialists and researchers owing to its contribution to numerous applications including military, environmental monitoring and so on. However, reducing the network delay and improving the network lifetime are always big issues in the domain of WSN. To resolve these downsides, we propose an Energy-Efficient Scheduling using the Deep Reinforcement Learning (DRL) (E²S-DRL) algorithm in WSN. E²S-DRL contributes three phases to prolong network lifetime and to reduce network delay that is: the clustering phase, duty-cycling phase and routing phase. E²S-DRL starts with the clustering phase where we reduce the energy consumption incurred during data aggregation. It is achieved through the Zone-based Clustering (ZbC) scheme. In the ZbC scheme, hybrid Particle Swarm Optimization (PSO) and Affinity Propagation (AP) algorithms are utilized. Duty cycling is adopted in the second phase by executing the DRL algorithm, from which, E²S-DRL reduces the energy consumption of individual sensor nodes effectually. The transmission delay is mitigated in the third (routing) phase using Ant Colony Optimization (ACO) and the Firefly Algorithm (FFA). Our work is modeled in Network Simulator 3.26 (NS3). The results are valuable in provisions of upcoming metrics including network lifetime, energy consumption, throughput and delay. From this evaluation, it is proved that our E²S-DRL reduces energy consumption, reduces delays by up to 40% and enhances throughput and network lifetime up to 35% compared to the existing cTDMA, DRA, LDC and iABC methods.

Since the realization in the 1930s that elevated fluoride concentrations in drinking water can have detrimental effects on human health, new methods have been progressively developed in order to reduce fluoride to acceptable levels. In the developing world the necessity for filtration media that are both low-cost and sourced from locally available materials has resulted in the widespread use of bone char. Since the early 1990s metallic iron (Fe⁰) has received widespread use as both an adsorbent and a reducing agent for the removal of a wide range of contaminant species from water. The ion-selectivity of Fe⁰ is dictated by the positively charged surface of iron (hydr)oxides at circumneutral pH. This suggests that Fe⁰ could potentially be applied as suitable filter media for the negatively charged fluoride ion. This communication seeks to demonstrate from a theoretical basis and using empirical data from the literature the suitability of Fe⁰ filters for fluoride removal. The work concludes that Fe⁰-bearing materials, such as steel wool, hold good promise as low-cost, readily available and highly effective decentralized fluoride treatment materials.

Metallic iron (Fe⁰)-based filtration systems have the potential to significantly contribute to the achievement of the United Nations (UN) Sustainable Development Goals (SDGs) of substantially improving the human condition by 2030 through the provision of clean water. Recent knowledge on Fe⁰-based safe drinking water filters is addressed herein. They are categorized into two types: Household and community filters. Design criteria are recalled and operational details are given. Scientists are invited to co-develop knowledge enabling the exploitation of the great potential of Fe⁰ filters for sustainable safe drinking water provision (and sanitation).