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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.

Network lifetime remains as a significant requirement in Wireless Sensor Network (WSN) exploited to prolong network processing. Deployment of low power sensor nodes in WSN is essential to utilize the energy efficiently. Clustering and sleep scheduling are the two major processes involved in improving network lifetime. However, abrupt and energy unaware selection of cluster head (CH) is nonoptimal in WSN which reflects in the drop of energy among sensor nodes. This paper addresses the twofold as utilization of sensor nodes to prolong the node's energy and network lifetime by LEACH-based cluster formation and Time Division Multiple Access scheduling (TDMA). Clusters are constructed by the design of an Enhanced-Low-Energy adaptive Clustering Hierarchy protocol (E-LEACH) that uses parallel operating optimization (Grey Wolf Optimization (GWO) and Discrete Particle Swarm Optimization (D-PSO)) for selecting an optimal CH and helper CH. The fitness values estimation from GWO and D-PSO is concatenated to prefer the best optimal CH. E-LEACH also manages the cluster size which is one of the conventional disadvantages in LEACH. CHs are responsible to perform energy-aware TDMA scheduling which segregates the coverage area into 24 sectors. Alternate sectors are assigned Ramadhani Sinde ABOUT THE AUTHOR Ramadhani Sinde works as an assistant lecturer in the School of Computational and Communication Sciences and Engineering at Nelson Mandela African Institution of Science and Technology (NM-AIST). Currently, he under Doctoral Environmental Management Information System project undertaking PhD studies in the field of Information and Communication of Science and Engineering. His research interests include Internet of Things (IoT) network, embedded systems, mobile computing, modelling and performance evaluation of wireless sensor networks, wireless and mobile communication. Mr. Sinde has co-authored more than 10 papers in internationally refereed journals and conferences. He is a Queen Elizabeth Scholarship -Advanced Scholars Program alumni.

Many different wireless sensor nodes for gas pipeline leak detection and location has been proposed but still there are challenges particularly on environmental issues and signal accuracy. This paper discusses theories and environmental constraints for wireless sensor nodes, a case study of Dar es Salaam -Tanzania and finally presents a design and simulation results of the proposed wireless sensor node using Proteus Design Suite for detecting frequency of sound exited by jetting gas, leaking from higher pressurised gas pipeline. This kind of proposed system can be useful to gas companies or industries whereby gas transportation is done.

The importance of conserving forests has been a massive motivation for this research. Forests play an essential role in preventing global warming by absorbing greenhouse gases and building sustainable societies. Forests have a variety of functions, such as land conservation, securing of water sources, control of climate change, and creation of natural environs essential to human existence and regulating the temperature of the atmospheric environment. There have been many changes in the field of wireless technology in the past decade, one of which is undoubtedly the introduction of WSN.