A Differential Evolution-Based Routing Algorithm for Environmental Monitoring Wireless Sensor Networks

The traditional Low Energy Adaptive Cluster Hierarchy (LEACH) routing protocol is a clustering-based protocol. The uneven selection of cluster heads results in premature death of cluster heads and premature blind nodes inside the clusters, thus reducing the overall lifetime of the network. With a full consideration of information on energy and distance distribution of neighboring nodes inside the clusters, this paper proposes a new routing algorithm based on differential evolution (DE) to improve the LEACH routing protocol. To meet the requirements of monitoring applications in outdoor environments such as the meteorological, hydrological and wetland ecological environments, the proposed algorithm uses the simple and fast search features of DE to optimize the multi-objective selection of cluster heads and prevent blind nodes for improved energy efficiency and system stability. Simulation results show that the proposed new LEACH routing algorithm has better performance, effectively extends the working lifetime of the system, and improves the quality of the wireless sensor networks

Design of a Water Environment Monitoring System Based on Wireless Sensor Networks

A water environmental monitoring system based on a wireless sensor network is proposed. It consists of three parts: data monitoring nodes, data base station and remote monitoring center. This system is suitable for the complex and large-scale water environment monitoring, such as for reservoirs, lakes, rivers, swamps, and shallow or deep groundwaters. This paper is devoted to the explanation and illustration for our new water environment monitoring system design. The system had successfully accomplished the online auto-monitoring of the water temperature and pH value environment of an artificial lake. The system's measurement capacity ranges from 0 to 80 °C for water temperature, with an accuracy of ±0.5 °C; from 0 to 14 on pH value, with an accuracy of ±0.05 pH units. Sensors applicable to different water quality scenarios should be installed at the nodes to meet the monitoring demands for a variety of water environments and to obtain different parameters. The monitoring system thus promises broad applicability prospects

Peatland Forest Fire Prevention Using Wireless Sensor Network Based on Naïve Bayes Classifier

Recently, peatland forest fires happened massively and gave bad impact for environment. It is necessary to make efforts to reduce of peatland forest fires, Early Warning System (EWS) is one of the solutions. Here, we propose an EWS to prevent forest fire in peatland by using Wireless Sensor Network (WSN). It uses three significant parameters which are oxygen concentration, soil humidity, and environment temperature. Naïve bayes classifier processes the data parameters and then determines forest fire potential. Unusual measurement of the parameters will trigger the classifier decision. Forest fire potential will be displayed through web services. This EWS helps the authorities to monitor and detect forest fire potential in the peatland, so it can be prevented

A Study for Remote Monitoring of Water Points in Mauritania Based on IoT (LoRa) Technology

Wetlands in Mauritania contain the most important water sources necessary for the survival of rural communities in the country. In these areas, the main rural activities such as animal husbandry, agriculture, and fishing take place. Lack of water or flooding must be monitored to plan solutions in advance. After a comparative study of IoT wireless technologies, we proposed that LoRa technology is the most suitable for our field of application. However, in certain areas where access to the cellular network is difficult, we propose the addition of satellite communication in the LoRamonitoring system to achieve information collected at any point in the world via the cloud and the Internet. We carried out a practical case for the areas covered by the UMTS (3G) cellular network using devices integrating LoRaWAN to evaluate the performance of this technology. The results show the success of the communication over a distance of 14 km

The impact of agricultural activities on water quality: a case for collaborative catchment-scale management using integrated wireless sensor networks

The challenge of improving water quality is a growing global concern, typified by the European Commission Water Framework Directive and the United States Clean Water Act. The main drivers of poor water quality are economics, poor water management, agricultural practices and urban development. This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions. Water quality monitoring (WQM) is currently undertaken through a number of data acquisition methods from grab sampling to satellite based remote sensing of water bodies. Based on the surveyed sampling methods and their numerous limitations, it is proposed that wireless sensor networks (WSNs), despite their own limitations, are still very attractive and effective for real-time spatio-temporal data collection for WQM applications. WSNs have been employed for WQM of surface and ground water and catchments, and have been fundamental in advancing the knowledge of contaminants trends through their high resolution observations. However, these applications have yet to explore the implementation and impact of this technology for management and control decisions, to minimize and prevent individual stakeholder’s contributions, in an autonomous and dynamic manner. Here, the potential of WSN-controlled agricultural activities and different environmental compartments for integrated water quality management is presented and limitations of WSN in agriculture and WQM are identified. Finally, a case for collaborative networks at catchment scale is proposed for enabling cooperation among individually networked activities/stakeholders (farming activities, water bodies) for integrated water quality monitoring, control and management

Transport mechanism for wireless micro sensor network

Wireless sensor network (WSN) is a wireless ad hoc network that consists of very large number of tiny sensor nodes communicating with each other with limited power and memory constrain. WSN demands real-time routing which requires messages to be delivered within their end-to-end deadlines (packet lifetime). This report proposes a novel real-time with load distribution (RTLD) routing protocol that provides real time data transfer and efficient distributed energy usage in WSN. The RTLD routing protocol ensures high packet throughput with minimized packet overhead and prolongs the lifetime of WSN. The routing depends on optimal forwarding (OF) decision that takes into account of the link quality, packet delay time and the remaining power of next hop sensor nodes. RTLD routing protocol possesses built-in security measure. The random selection of next hop node using location aided routing and multi-path forwarding contributes to built-in security measure. RTLD routing protocol in WSN has been successfully studied and verified through simulation and real test bed implementation. The performance of RTLD routing in WSN has been compared with the baseline real-time routing protocol. The simulation results show that RTLD experiences less than 150 ms packet delay to forward a packet through 10 hops. It increases the delivery ratio up to 7 % and decreases power consumption down to 15% in unicast forwarding when compared to the baseline routing protocol. However, multi-path forwarding in RTLD increases the delivery ratio up to 20%. In addition, RTLD routing spreads out and balances the forwarding load among sensor nodes towards the destination and thus prolongs the lifetime of WSN by 16% compared to the baseline protocol. The real test bed experiences only slight differences of about 7.5% lower delivery ratio compared to the simulation. The test bed confirms that RTLD routing protocol can be used in many WSN applications including disasters fighting, forest fire detection and volcanic eruption detection

DESIGN OF RELIABLE AND SUSTAINABLE WIRELESS SENSOR NETWORKS: CHALLENGES, PROTOCOLS AND CASE STUDIES

Integrated with the function of sensing, processing, and wireless communication, wireless sensors are attracting strong interest for a variety of monitoring and control applications. Wireless sensor networks (WSNs) have been deployed for industrial and remote monitoring purposes. As energy shortage is a worldwide problem, more attention has been placed on incorporating energy harvesting devices in WSNs. The main objective of this research is to systematically study the design principles and technical approaches to address three key challenges in designing reliable and sustainable WSNs; namely, communication reliability, operation with extremely low and dynamic power sources, and multi-tier network architecture. Mathematical throughput models, sustainable WSN communication strategies, and multi-tier network architecture are studied in this research to address these challenges, leading to protocols for reliable communication, energy-efficient operation, and network planning for specific application requirements. To account for realistic operating conditions, the study has implemented three distinct WSN testbeds: a WSN attached to the high-speed rotating spindle of a turning lathe, a WSN powered by a microbial fuel cell based energy harvesting system, and a WSN with a multi-tier network architecture. With each testbed, models and protocols are extracted, verified and analyzed. Extensive research has studied low power WSNs and energy harvesting capabilities. Despite these efforts, some important questions have not been well understood. This dissertation addresses the following three dimensions of the challenge. First, for reliable communication protocol design, mathematical throughput or energy efficiency estimation models are essential, yet have not been investigated accounting for specific application environment characteristics and requirements. Second, for WSNs with energy harvesting power sources, most current networking protocols do not work efficiently with the systems considered in this dissertation, such as those powered by extremely low and dynamic energy sources. Third, for multi-tier wireless network system design, routing protocols that are adaptive to real-world network conditions have not been studied. This dissertation focuses on these questions and explores experimentally derived mathematical models for designing protocols to meet specific application requirements. The main contributions of this research are 1) for industrial wireless sensor systems with fast-changing but repetitive mobile conditions, understand the performance and optimal choice of reliable wireless sensor data transmission methods, 2) for ultra-low energy harvesting wireless sensor devices, design an energy neutral communication protocol, and 3) for distributed rural wireless sensor systems, understand the efficiency of realistic routing in a multi-tier wireless network. Altogether, knowledge derived from study of the systems, models, and protocols in this work fuels the establishment of a useful framework for designing future WSNs

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa