Use of Modern Technology in The Automated Farming of Agriculture

In order to improve efficiency, productivity, global market, and to reduce human intervention, time, and cost, there is a requirement for the introduction of new technology called the Internet of Things. The internet of things (IoT) is the network of interconnected devices that facilitates information transfer without human involvement. Agriculture and the Internet of Things work together to accomplish smart farming.   The current study is a systematic review on the use of IOT and other smart methods in agriculture

Efficient Actor Recovery Paradigm For Wireless Sensor And Actor Networks

Wireless sensor networks (WSNs) are becoming widely used worldwide. Wireless Sensor and Actor Networks (WSANs) represent a special category of WSNs wherein actors and sensors collaborate to perform specific tasks. WSANs have become one of the most preeminent emerging type of WSNs. Sensors with nodes having limited power resources are responsible for sensing and transmitting events to actor nodes. Actors are high-performance nodes equipped with rich resources that have the ability to collect, process, transmit data and perform various actions. WSANs have a unique architecture that distinguishes them from WSNs. Due to the characteristics of WSANs, numerous challenges arise. Determining the importance of factors usually depends on the application requirements. The actor nodes are the spine of WSANs that collaborate to perform the specific tasks in an unsubstantiated and uneven environment. Thus, there is a possibility of high failure rate in such unfriendly scenarios due to several factors such as power fatigue of devices, electronic circuit failure, software errors in nodes or physical impairment of the actor nodes and inter-actor connectivity problem. It is essential to keep inter-actor connectivity in order to insure network connectivity. Thus, it is extremely important to discover the failure of a cut-vertex actor and network-disjoint in order to improve the Quality-of-Service (QoS). For network recovery process from actor node failure, optimal re-localization and coordination techniques should take place. In this work, we propose an efficient actor recovery (EAR) paradigm to guarantee the contention-free traffic-forwarding capacity. The EAR paradigm consists of Node Monitoring and Critical Node Detection (NMCND) algorithm that monitors the activities of the nodes to determine the critical node. In addition, it replaces the critical node with backup node prior to complete node-failure which helps balances the network performance. The packet is handled using Network Integration and Message Forwarding (NIMF) algorithm that determines the source of forwarding the packets (Either from actor or sensor). This decision-making capability of the algorithm controls the packet forwarding rate to maintain the network for longer time. Furthermore, for handling the proper routing strategy, Priority-Based Routing for Node Failure Avoidance (PRNFA) algorithm is deployed to decide the priority of the packets to be forwarded based on the significance of information available in the packet. To validate the effectiveness of the proposed EAR paradigm, we compare the performance of our proposed work with state-of the art localization algorithms. Our experimental results show superior performance in regards to network life, residual energy, reliability, sensor/ actor recovery time and data recovery

Energy-Efficient and Fresh Data Collection in IoT Networks by Machine Learning

The Internet-of-Things (IoT) is rapidly changing our lives in almost every field, such as smart agriculture, environmental monitoring, intelligent manufacturing system, etc. How to improve the efficiency of data collection in IoT networks has attracted increasing attention. Clustering-based algorithms are the most common methods used to improve the efficiency of data collection. They group devices into distinct clusters, where each device belongs to one cluster only. All member devices sense their surrounding environment and transmit the results to the cluster heads (CHs). The CHs then send the received data to a control center via single-hop or multi-hops transmission. Using unmanned aerial vehicles (UAVs) to collect data in IoT networks is another effective method for improving the efficiency of data collection. This is because UAVs can be flexibly deployed to communicate with ground devices via reliable air-to-ground communication links. Given that energy-efficient data collection and freshness of the collected data are two important factors in IoT networks, this thesis is concerned with designing algorithms to improve the energy efficiency of data collection and guarantee the freshness of the collected data. Our first contribution is an improved soft-k-means (IS-k-means) clustering algorithm that balances the energy consumption of nodes in wireless sensor networks (WSNs). The techniques of “clustering by fast search and find of density peaks” (CFSFDP) and kernel density estimation (KDE) are used to improve the selection of the initial cluster centers of the soft k-means clustering algorithm. Then, we utilize the flexibility of the soft-k-means and reassign member nodes by considering their membership probabilities at the boundary of clusters to balance the number of nodes per cluster. Furthermore, we use multi-CHs to balance the energy consumption within clusters. Extensive simulation results show that, on average, the proposed algorithm can postpone the first node death, the half of nodes death, and the last node death when compared to various clustering algorithms from the literature. The second contribution tackles the problem of minimizing the total energy consumption of the UAV-IoT network. Specifically, we formulate and solve the optimization problem that jointly finds the UAV’s trajectory and selects CHs in the IoT network. The formulated problem is a constrained combinatorial optimization and we develop a novel deep reinforcement learning (DRL) with a sequential model strategy to solve it. The proposed method can effectively learn the policy represented by a sequence-to-sequence neural network for designing the UAV’s trajectory in an unsupervised manner. Extensive simulation results show that the proposed DRL method can find the UAV’s trajectory with much less energy consumption when compared to other baseline algorithms and achieves close-to-optimal performance. In addition, simulation results show that the model trained by our proposed DRL algorithm has an excellent generalization ability, i.e., it can be used for larger-size problems without the need to retrain the model. The third contribution is also concerned with minimizing the total energy consumption of the UAV-aided IoT networks. A novel DRL technique, namely the pointer network-A* (Ptr-A*), is proposed, which can efficiently learn the UAV trajectory policy for minimizing the energy consumption. The UAV’s start point and the ground network with a set of pre-determined clusters are fed to the Ptr-A*, and the Ptr-A* outputs a group of CHs and the visiting order of CHs, i.e., the UAV’s trajectory. The parameters of the Ptr-A* are trained on problem instances having small-scale clusters by using the actor-critic algorithm in an unsupervised manner. Simulation results show that the models trained based on 20- clusters and 40-clusters have a good generalization ability to solve the UAV’s trajectory planning problem with different numbers of clusters, without the need to retrain the models. Furthermore, the results show that our proposed DRL algorithm outperforms two baseline techniques. In the last contribution, the new concept, age-of-information (AoI), is used to quantify the freshness of collected data in IoT networks. An optimization problem is formulated to minimize the total AoI of the collected data by the UAV from the ground IoT network. Since the total AoI of the IoT network depends on the flight time of the UAV and the data collection time at hovering points, we jointly optimize the selection of the hovering points and the visiting order to these points. We exploit the state-of-the-art transformer and the weighted A* to design a machine learning algorithm to solve the formulated problem. The whole UAV-IoT system, including all ground clusters and potential hovering points of the UAV, is fed to the encoder network of the proposed algorithm, and the algorithm’s decoder network outputs the visiting order to ground clusters. Then, the weighted A* is used to find the hovering point for each cluster in the ground IoT network. Simulation results show that the model trained by the proposed algorithm has a good generalization ability to generate solutions for IoT networks with different numbers of ground clusters, without the need to retrain the model. Furthermore, results show that our proposed algorithm can find better UAV trajectories with the minimum total AoI when compared to other algorithms

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Routing and Medium Access Control (MAC) in wireless sensor network for monitoring emergency applications

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIn recent years, Wireless Sensor Networks (WSNs) have been implemented in many applications including emergency applications. Emergency applications require different characteristics than others, such as robust communication, low energy consumption and minimum end-to-end delay. Routing and Medium Access Control (MAC) are two protocols that have been used by many researchers to achieve those requirements. This thesis mainly focuses on studying distributive clustering routing and MAC protocol for emergency applications. To design robust communication in emergency applications, this thesis has proposed a modified LEACH protocol considering the health status of sensor nodes. LEACH is a benchmark protocol employing distributive clustering-based routing with low energy consumption, however this protocol is not suitable for emergency applications. The health status refers to the condition of nodes, safe or in danger, with the danger status shows the high probability to be destroyed sooner because of external factors such as fire. The proposed approach avoids selecting the nodes in danger as cluster heads. Furthermore, efficient multi-hop communication is employed to minimise energy consumption. The simulation result shows that total data received, energy consumption , packet delivery ratio, and energy efficiency of the proposed approach are stable with an increasing number of destroyed nodes. Furthermore, a grid-based clustering approach with health status is proposed to further enhance energy constraint and robust communication. The proposed approach includes distributive clustering and incorporate constant number of CHs in every round. The remaining energy, the health status of node, and the distance to the centre of the grid are consided when choosing the cluster head. Simulation results have revealed that the proposed protocol has a significant effect on the time for first node to destroy due to energy consumption, an increase of 45% compared to LEACH. Furthermore, packet delivery ratio of the proposed approach is enhanced by 16% compared to LEACH. In order to reduce end to end delay, a priority-based grid Time Division Multiple Access (TDMA) has been proposed. In this approach, traffic is classified into two categories: emergency traffic from danger nodes, and monitoring traffic from safe nodes. This scheme was implemented using three steps: formation of a new TDMA frame, the arrangement of slots and priority allocation. Simulations results showed an improvement of around 65% and 70% in end to end delay compared to Grid and LEACH approaches.Directorate General of Resources for Science, Technology, and Higher Education of Indonesia; the University of Ria

Smart Urban Water Networks

This book presents the paper form of the Special Issue (SI) on Smart Urban Water Networks. The number and topics of the papers in the SI confirm the growing interest of operators and researchers for the new paradigm of smart networks, as part of the more general smart city. The SI showed that digital information and communication technology (ICT), with the implementation of smart meters and other digital devices, can significantly improve the modelling and the management of urban water networks, contributing to a radical transformation of the traditional paradigm of water utilities. The paper collection in this SI includes different crucial topics such as the reliability, resilience, and performance of water networks, innovative demand management, and the novel challenge of real-time control and operation, along with their implications for cyber-security. The SI collected fourteen papers that provide a wide perspective of solutions, trends, and challenges in the contest of smart urban water networks. Some solutions have already been implemented in pilot sites (i.e., for water network partitioning, cyber-security, and water demand disaggregation and forecasting), while further investigations are required for other methods, e.g., the data-driven approaches for real time control. In all cases, a new deal between academia, industry, and governments must be embraced to start the new era of smart urban water systems

The Acceptance of Using Information Technology for Disaster Risk Management: A Systematic Review

The numbers of natural disaster events are continuously affecting human and the world economics. For coping with disaster, several sectors try to develop the frameworks, systems, technologies and so on. However, there are little researches focusing on the usage behavior of Information Technology (IT) for disaster risk management (DRM). Therefore, this study investigates the affecting factors on the intention to use IT for mitigating disaster’s impacts. This study conducted a systematic review with the academic researches during 2011-2018. Two important factors from the Technology Acceptance Model (TAM) and others are used in describing individual behavior. In order to investigate the potential factors, the technology platforms are divided into nine types. According to the findings, computer software such as GIS applications are frequently used for simulation and spatial data analysis. Social media is preferred among the first choices during disaster events in order to communicate about situations and damages. Finally, we found five major potential factors which are Perceived Usefulness (PU), Perceived Ease of Use (PEOU), information accessibility, social influence, and disaster knowledge. Among them, the most essential one of using IT for disaster management is PU, while PEOU and information accessibility are more important in the web platforms