With the exponential growth of Internet of Things (IoT) devices, edge computing has emerged as a vital paradigm for localized data processing, low-latency communication, and efficient resource utilization. However, routing in IoT-based edge networks remains challenging due to dynamic topologies, constrained energy resources, and growing security threats. To tackle these challenges, this article proposes QLB-IoT, a novel framework that integrates Q-learning-based intelligent routing with blockchain-assisted trust management to optimize energy consumption while ensuring secure and adaptive data transmission. The proposed method enables IoT devices to autonomously learn energy-efficient routing paths based on real-time network parameters such as residual energy and link distance. The Q-learning mechanism minimizes route flapping and promotes adaptive decision-making. Simultaneously, blockchain and digital signatures establish a decentralized trust layer that authenticates devices and prevents attacks like blackhole and Sybil. Compared with open shortest path first (OSPF), Q-Routing, deep reinforcement learning-based control framework for traffic engineering (DRL-TE), and intelligent edge network routing (ENIR), exhibits an extended network lifetime to $\approx 1300$ rounds ( $2.4\times $ OSPF and 8% beyond ENIR), increases throughput by 18%–30%, and reduces average latency by half, while minimizing packet loss by $\approx 40$ % and increasing the likelihood of quality of service (QoS) compliance by up to 10%. These improvements stem from a real-time exploration–exploitation mechanism that dynamically reroutes traffic away from energy-depleted nodes, and from the immutable ledger that prevents malicious route manipulation without centralized oversight. By combining adaptive, energy-aware routing with verifiable trust, QLB-IoT delivers a scalable and resilient solution for mission-critical IoT-edge deployments.

Wireless sensor networks (WSNs) offer advantages in deployment flexibility and affordability due to their compact size and low cost. However, real-world WSN implementations face challenges, particularly regarding energy consumption. The limited battery capacity of sensor nodes restricts their operational lifespan, often resulting in; it is difficult to periodically replace the batteries in sensor nodes or recharge them, which reduces the system's overall operating time. To address the energy consumption challenge, WSNs are often divided into clusters. This clustering approach reduces communication costs and the energy required for routing data packets. Consequently, selecting the most efficient cluster head (CH) is crucial for maximizing the network's overall lifespan. This paper proposes a protocol that prioritizes energy efficiency, proximity to the base station, and even distribution within clusters when selecting CHs for intra-cluster communication. For inter-cluster communication, the protocol draws inspiration from the SMO protocol to identify the optimal next-hop CH based on remaining energy and distance to the base station. The performance of this proposed protocol is then compared against LEACH routing protocols. The simulation results indicate that the network becomes more optimized and energy efficient by using the proposed protocol.

Air quality has a significant impact on agricultural productivity, with pollutants such as ozone (O3), particulate matter (PM), and carbon monoxide (CO) posing serious threats to crop yields. Plant growth and yields can be disrupted by these pollutants, highlighting the need for effective solutions. Strategies, including improvements in air quality management to reduce pollutant levels and the development of advanced predictive models, have been proposed. The concentrations of air pollutants and their potential impacts on agriculture can be forecasted by these models, allowing proactive measures to be taken to mitigate adverse effects on crop yields. In this research, the challenges associated with predicting the impact of key air pollutants on rice yield in India are addressed. Satellite data from the Giovanni Data Centre was utilized to monitor concentrations of O?, PM, and CO, and to calculate the Air Quality Index (AQI). The Prophet model is employed to predict future pollutant levels and AQI. Soil temperature and air moisture data were incorporated to assess their combined impact on crop yield. Nineteen years of monthly rice yield data from FAOSTAT was used to train a feed-forward neural network with inputs including PM, O?, CO, AQI, soil temperature, and air moisture. A high accuracy of 94% was achieved by the model, effectively predicting crop yields based on these factors, and a clear inverse relationship between air quality and crop yield was demonstrated: significant decreases in yield were correlated with higher concentrations of O?, PM, and CO. Concrete evidence of the detrimental effects of air pollution on crop productivity is provided by these findings.

The rapid evolution of consumer electronics has transformed everyday life by integrating advanced technologies such as Artificial Intelligence (AI), the Internet of Things (IoT) and augmented reality (AR). This chapter examines the current landscape of consumer electronics, highlighting both technological advancements and the environmental challenges posed by increased device production and disposal. Special emphasis is placed on sustainability and the adoption of emerging technologies, including blockchain, 6G wireless networks and the Metaverse to develop smarter, more efficient and secure consumer products. The Metaverse introduces immersive digital experiences. Blockchain technology enhances device security, transparency and supply chain integrity by enabling decentralized authentication. Meanwhile, 6G networks promise ultra-high-speed, low-latency connectivity. This paper also identifies future research directions aimed at balancing innovation with ecological responsibility and enhanced user experiences.