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Novel Energy Savings Method Considering Extra Sensor Battery Discharge Time for Fish Farming Applications
... This room must maintain a constant temperature required for incubation (18°C) using heaters, as well as energy sources for illumination when necessary. Control of parameters such as temperature, CO₂, pH, and humidity is required, so mushroom growth is monitored every three days [21]. During the primordia initiation phase, ventilation must be maintained with air injectors and extractors to ensure that fresh air lowers the temperature and removes CO₂. ...
Based on a bibliographic review of articles and scientific documents regarding the composition of cocoa pod husk (CPH), the benefits of its use as a substrate, and the parameters required for the production of edible mushrooms, an environmental assessment for the operation phase was conducted. A total of 85 environmental aspects were identified, along with 85 direct impacts and 117 indirect impacts related to various environmental components. However, after reviewing the entire project phase, the environmental impacts were reduced to 38. Additionally, the utilization of CPH generates employment and economic income for the community, although it requires high consumption of water, energy, and fuel. The environmental categorization of the project indicated that it is a moderate-impact project, where its effects do not spread beyond the direct area of influence.
The integration of Healthcare 4.0 and wireless body area networks (HC-WBAN 4.0) offers a unique opportunity to revolutionize healthcare by enabling real-time patient health monitoring. This integration can lead to various benefits, including early detection of health issues, timely intervention, and personalized treatment. However, this integration also poses several challenges, such as managing and analyzing large volumes of data generated by WBANs and ensuring reliable communication between WBAN devices and healthcare systems. To address these challenges, we propose a novel approach that leverages data-driven and context-aware packet scheduling algorithms based on machine/deep learning techniques. Our proposed approach can reduce redundant data generated by WBAN sensors and optimize the quality of service (QoS) by dynamically adapting the packet generation rate of each sensor. We use an auto-regression model and deep reinforcement learning (AR-DRL) to learn each sensor’s optimal packet generation rate, which results in improved energy efficiency, transmission reliability, network stability, and transmission cost savings. Our simulation results show that our proposed approach outperforms state-of-the-art methods, achieving up to 72.87% and 66.44% reduction in transmission cost for a higher and minimum redundancy rate, respectively. These results demonstrate the potential of our approach to improve the efficiency and reliability of WBANs for healthcare applications and pave the way for more effective integration of Healthcare 4.0 and WBANs. Our proposed approach can help healthcare providers make more informed decisions, improve patient outcomes, and reduce healthcare costs.
In this work, a methodology is presented for city-wide road traffic noise indicator mapping. The need for direct access to traffic data is bypassed by relying on street categorization and a city microphone network. The starting point for the deterministic modeling is a previously developed but simplified dynamic traffic model, the latter necessary to predict statistical and dynamic noise indicators and to estimate the number of noise events. The sound propagation module combines aspects of the CNOSSOS and QSIDE models. In the next step, a machine learning technique—an artificial neural network in this work—is used to weigh the outcomes of the deterministic predictions of various traffic parameter scenarios (linked to street categories) to approach the measured indicators from the microphone network. Application to the city of Barcelona showed that the differences between predictions and measurements typically lie within 2–3 dB, which should be positioned relative to the 3 dB variation in street-side measurements when microphone positioning relative to the façade is not fixed. The number of events is predicted with 30% accuracy. Indicators can be predicted as averages over day, evening and night periods, but also at an hourly scale; shorter time periods do not seem to negatively affect modeling accuracy. The current methodology opens the way to include a broad set of noise indicators in city-wide environmental noise impact assessment.
Citation: Arumugam, M.; Jayaraman, S.; Sridhar, A.; Venkatasamy, V.; Brown, P.B.; Abdul Kari, Z.; Tellez-Isaias, G.; Ramasamy, T. Recent Abstract: Tilapia is a widely cultured species native to Africa; these fish are prolific breeders and constitute an economically important fish species supplying higher-quality protein. To meet the global food demand and achieve the UN's Sustainable Developmental Goals (SDG), the aquaculture industry has conceived of productive solutions with the potential for adaptability, palatability, and profitability. Tilapia may play a vital role with respect to the possibility for sustainability in the nutrition and aquaculture sectors. India contributes to the promotion of aquacultural practices through a structural framework focused on agricultural, environmental, geographical, and socioeconomic factors that provide opportunities for tilapia farming. Globally, the Indian aquaculture sector is currently the second largest in terms of aquacultural production but is moving toward different species that meet SDG and facilitate international marketing opportunities. The farming of aquacultural species with innovative technology constitutes an efficient use of resources. Productive research on feeding, disease management, construction, and layout helps overcome the challenges faced in aquaculture. These focused and sustained factors of the aquaculture industry offer a latent contribution to global food security. This review reports on the state of the art, the challenges regarding tilapia aquaculture in India, and the Indian government's schemes, missions, subsidies, projects and funding related to tilapia production. Key Contribution: The present review deals with the important farming strategies of tilapia aquaculture in India. Also, the policies framed by the Indian government through various programs and subsidies to expand the blue economy relating tilapia farming and their direct benefits to the aquaculture farmers were highlighted.
Wireless Sensor Networks (WSNs) are the main data collection tools used by Internet of Things (IoT) devices. The WSN-based IoT is a collection of several small, geographically dispersed, battery-powered sensors that are devoted to carrying out a certain activity in a collaborative manner. In a dense WSN-based IoT network, numerous sensors that are near to one another simultaneously collect the same data about the occurrence. Even though WSN-based IoT has opened up previously unimaginable possibilities in a variety of application areas, they are still susceptible to resource limitations. The energy of nodes, which is needed to run well for extended periods of time in many activities, is the most crucial resource in a given WSN-based IoT. Increasing the lifetime of the network is a major focus of research in the field of WSN-based IoT because it is impossible to replace or recharge batteries in remote, harsh or dangerous environments. In this article, an energy-efficient fuzzy-based unequal clustering with a sleep scheduling (EFUCSS) protocol for IoT that uses WSN is proposed. This protocol makes the network last longer and uses less energy. It does this by using clustering, scheduling, and data transmission. Unequal clusters based on Fuzzy C-Means are formed using this protocol to balance the energy used via reducing the distance that data travels. The selection of the cluster head is carried out using fuzzy logic system. The gateway’s (GW) distance, remaining energy, and centrality are input variables. The output fuzzy variable is chance. Fuzzy inference is performed using the Mamdani technique. The sleep scheduling strategy is used between the coupled nodes to reduce the number of transmitted nodes. Extensive Python-based simulation experiments are run in order to evaluate the performance of the proposed EFUCSS protocol. While taking into account different WSN-based IoT scenarios and several criteria, such as network stability, network lifetime, and energy efficiency, a comparison is made between the proposed EFUCSS protocol and other well-known conventional protocols. The results show that the proposed EFUCSS improves remaining energy by 26.92%–213.4% and network lifespan by 39.58%–408.13%. The suggested EFUCSS also results in a greater improvement in network lifespan compared to other comparable algorithms.
As the cornerstone of IoT-based systems, WSN connecting a wide range of intelligent sensor nodes is expected to bring significant changesin the near future. Due to the limited battery capacity of the sensor node, WSN considers maximizing network lifetime by minimizing thepower consumption to be the most important challenge. To this end, we propose a Distributed Adaptive Communication with On/Off switchingand Dual queuing for Energy efficiency (DACODE) as a novel asynchronous duty cycling mechanism. The performance evaluation showsthat the proposed mechanism significantly reduces power consumption while maintaining network throughput and guaranteeing data urgencyand queue stability.
Fish farming is still controlled and managed in the traditional way where water quality and fish feeding are manually controlled. There is a need to use computer and communication technology in fish farms for remote monitoring and control. This paper deals with the design and implementation of an internet of things (IoT) based system for real-time monitoring, control and management of fish farming. The design of such a system is based on measuring different types of variables and using the information to control fish growth and increase productivity. Each fish pond is a node in a wireless sensor network. The node contains an embedded microcontroller connected to a set of sensors and actuators and a wireless communication module. Two fuzzy controllers are designed to control the water quality in the ponds as well as the environment using five sensors in each pond plus three environmental sensors. Practical results indicate the accuracy of the measurement system compared to the results obtained from commercial devices used on the farm. These results also showed that the proposed approach achieves the best performance of the real-time monitoring and control system in fish ponds. Keywords: Embedded systems Fish farms Internet of things Real-time monitoring and control This is an open access article under the CC BY-SA license.
In wireless sensor networks, the energy efficiency is a big challenge and this can be defeated using various techniques such as clustering and routing. In the clustering approach, clusters are made up of the sensor nodes. In most of the applications, the sensor nodes are battery-powered. The sensor nodes may carry an uneven load of data and consume an unequal amount of energy. This leads to the formation of an energy hole in the network. Therefore, load balancing in the network with respect to the distance of data transmission is important in WSN. Hence, this paper proposes an energy-efficient as well as Load Balanced Clustering algorithm using Fuzzy logic (LBCF). The values of load and distance between the sensor node and BS are associated using rule-based fuzzy logic. The output values of the fuzzy system decides the cluster heads (CHs) and the size of the cluster. The sensor nodes get assigned to the CHs with respect to the capacity of CH. The simulations are conducted under different scenarios and network parameters and the proposed LBCF shows out performance to the state-of-the-art algorithms.
Wireless sensor networks (WSNs) are now frequently used to collect all necessary sensory data for decision-making. As a result, it is common for the sensor and sink nodes to synchronize their messages quickly. Due to this consequence, the sensor node consumes much energy because of the enhanced network traffic. Conventional sensor node batteries often have a small number of charge cycles, and recharging the battery at an inaccessible location is impracticable. Some practical concerns are feasible, such as network life, coverage/connectivity, which drains the nodes’ batteries without frequent maintenance, and network performance. To overcome the problem of an energy shortage, energy harvesting (EH) can offer a limitless supply of energy resources for networks. Solar, wind, mechanical, and thermal are all possibilities. Related to the intra-networks-based solution, we have designed an effective solution to solve the above issues called the constrained relay harvesting node placement (CRHNP) approach in EH-WSNs. This algorithm worked based on efficient coverage awareness and showed effective geometric-based coordination among nodes. Finding relay harvesting nodes and employing multiple maximum covering sets scheduling approaches in EH-WSNs are some of the functionalities supported by the CRHNP method. The proposed work’s results are classified into five categories: network lifetime, coverage degree, optimum RH node placement, node performance, and network sustainability performance assessment. The performance of the proposed method outperforms all other algorithms in each sector.
The Wireless-Sensor-Network (WSN) has been employed in all digital applications for several purposes like sensing, storing, and sharing information. However, managing energy consumption is more critical because of the movable environment. Several existing models have addressed these energy management issues. Still, those models lack in optimizing the energy usage of the WSN during the collision environment. This has motivated to find the best solution for energy optimization with an intelligent model. So, the present research article aims to develop the novel Buffalo-based Deep Belief Energy Management Framework (BDBEMF) for the WSN application. Initially, the required number of sensor nodes was created then the BDBEMF was designed to monitor the high consumption nodes. In addition, the Low-energy adaptive-clustering-hierarchy protocol has been considered for the communication process. Consequently, the Cluster Head has been selected based on less energy utilization and high-density hubs. The data rate of each node has been measured, and the high leaded data has been shared to work fewer nodes to balance the energy. Finally, the amount of alive and dead nodes was validated with few communication metrics. The presented model has gained maximum throughput and less energy consumption.
The openness nature of the Ad-hoc sensor networks emerged as a security threat in this network environment that leads to packets drop, network overhead, high range energy consumption, and transmission delay. Previous methods such as Trust-Based Malicious Nodes Detection in AD-hoc (TBMND) and Security based Data-Aware Routing Protocol (SDARP) found that this happens due to malicious nodes in the network. A malicious node in a network degrades its efficiency that also affects the routing decision making and also makes error decisions in route selection. Trust vector model and Trust-based Secure Routing (TBSR) have made attempts to evaluate trust nodes via various techniques, but unfortunately, the accuracy level has not been reached to the required range. In this paper, a trust-based energy-aware routing using GEOSR protocol for Ad-hoc sensor networks is presented for providing an energy-efficient and secured routing. In this trust-based energy-aware routing, initially the sensor nodes are deployed in the ad-hoc network. The clustering is performed based on the estimation of distance among the each nodes and the cluster head (CH) is selected based on the threshold value. After CH selection, the trust evaluation is performed for identify the trust and untrusted nodes present in the network. The untrusted node is considered as malicious node which is detected and blocked. The trusted nodes are forwarded to select the optimal routing path for secured transmission. Golden Eagle Optimized Secure Routing (GEOSR) is introduced for selecting the optimal routing path based on the parameters such as distance delay and energy objective function. Thus, energy-efficient and secured routing were done using the GEOSR protocol. GEOSR protocol was then implemented in the NS-2 simulation tool and then compared with existing techniques. GEOSR protocol shows 95% of residual energy for the prediction of two malicious nodes. Thus GEOSR was suitable for real-time applications.
We analysed literature data and our experimental results to determine why the readings of different temperature sensors might be notably different in air despite being placed in close proximity. We attributed these differences to two factors—unrestricted air movements and differences in the sensors’ response times. After elimination of these factors, the temperature readings of Pt100 and DS18B20 sensors exhibited an excellent agreement which, together with the convenient networking features provided by the DS18B20 sensors, confirmed their suitability for our use case.
Wireless Sensor Networks (WSN) always need energy due to the areas they are used. The use of sensors is quite wide, and in some of the places, it is very difficult or impossible to restore the energy of the sensors such as in war areas or in wildlife. Therefore, they need to use their existing energy most efficiently. For WSN, the role of clustering is crucial in terms of using less energy. Selecting the most appropriate sensor node as cluster head (CH) according to the criteria determined within the clustered sensors reduces the energy consumption. In this study, a new clustering algorithm is proposed for WSNs to reduce energy consumption and thus prolong the life of the WSNs. The Cluster Centered Cluster Head Selection Algorithm (C3HA), which is developed in line with this objective, gives a new perspective to the selection of CH while creating a more efficient WSN than the popular clustering algorithms LEACH, and PEGASIS. This developed algorithm is compared with popular algorithms and proved to be more efficient in terms of fast and accurate CH selection.
A survey of 101 shrimp farms in Ecuador revealed that the average annual shrimp yield based on production pond water surface area was 7.03 ± 0.93 (SE) t/ha/year. The range in annual yield among the five provinces with shrimp farms was 3.67 ± 0.71 to 11.95 ± 5.56 t/ha year, respectively. Averages for total land, water, and energy use were 0.54 ± 0.01 ha/t shrimp, 76,817 ± 6,330 m3/t shrimp, and 61.2 ± 4.2 GJ/t shrimp, respectively. Average wild fish use for fish meal and fish oil included in feed was 0.65 ± 0.02 t/t shrimp. One‐fourth of land use was land embodied in feed. Nearly 80% of total water use was incurred for exchanging water. Eighty‐seven of the farms practiced daily water exchange at an average rate of 8.5% pond volume per day (range 0.7–30%). Pumping water at all farms and mechanical aeration at 47 of the farms were the major direct energy uses. About half of the total energy use was embodied, and roughly half of the embodied energy was in feed.
Electrical resistivity property is widely employed recently for assessing the geotechnical parameters of soil, including the swelling index. However, under circumstances of leaking hydrocarbon products into expansive grounds, the perspective of using such technique in identifying the degree of contamination is still obscure and needs more investigation. In this research, six identical resistivity boxes are self-instrumented in the laboratory to test samples of different clay-bentonite mixtures upon oil contamination. The samples are tested under various control conditions of densification and water content. Each soil mixture is artificially contaminated with crude oil at a certain percentage ranging from 0% to 25% of its dry mass. The outcomes show that electrical resistivity increases with the addition of oil, and that is highly noticed for the samples characterized by high swelling potential. Also, increasing the dry unit weight of expansive polluted samples contributes to raising the resistivity measurements. However, adding more water to the contaminated samples causes a noticeable reduction of the resistivity measurements. The obtained results could be used as a guide for use in soil remediation concerning polluted ground. Thus, the study contributes to producing a reliable alternative to using costly, time and effort consuming conventional tests.
This article describes a technique for the manufacturing of the composites MWCNTs-modified elastomers. The effect of the different MWCNTs mass contents in the elastomer on specific volume and surface electrical resistivity values was investigated. The results on the heaters based on the elastomers and altered with multi-walled carbon nanotubes (MWCNTs) over various catalysts are discussed. It was found that with an increase in the MWCNTs mass content, the maximum power is achieved at a lower voltage value. Besides, temperature fields were measured on the surface of heater samples, and it was established that the temperature variation on the sample surface did not exceed 5 °C, and the heaters had a heating rate of 0.2–0.5 °C/s. Furthermore, a comparison of these heaters with the analogs previously reported by the other researchers is presented, indicating good importance of the reported work. The industrial (commercial) production of such devices is mainly associated with using in fan heaters for domestic purposes, in heaters built into clothes in underfloor heating systems and motor vehicles. The present work will find a good industrial application in the future.
Periodic Sensor Networks (PSNs) represent one of the important networks which are used in the Internet of Things (IoTs) future. It has gained a rapidly increased interest due to its wide use in various fields of applications. However, it suffers from the drawback of a limited lifetime as its power supply cannot be replaced or renew due to the nature of its deployment. Much of the research in this area has been focusing on conserving energy in whatever way possible. In this study, we tackled the issue of reducing the data volume transmitted to the final base station or sink. We proposed an Energy-efficient Two-layer data transmission reduction scheme that exploits both temporal and spatial redundancy in sensed data. The first layer is based on a modified version of the kNN algorithm and is applied at the sensor node level. bottom-up hierarchical clustering is implemented at the Cluster Head or aggregator to reduce data received by aggregator before sending it to the sink node. The proposed approach in layer two exploits the spatial correlation between neighboring nodes to remove the redundancy of data and thus to improve the lifetime of the PSN. The proposed approach is implemented using the OMNeT++ network simulator using real sensed data from Intel Berkeley Lab sensors. A number of experiments have been conducted to evaluate the proposed approach. The results are compared with some recently published methods that show the proposed approach outperforms the other methods in terms of many performance metrics such as data reduction rate, energy consumption, lost data.
Wireless sensor networks proliferate more and more in all social scopes and sectors. Such networks are implemented in smart homes, smart cities, security systems, medical resources, agriculture, automotive industry, etc. Communication devices and sensors of such networks are powered with batteries: the enlarging of battery life is a hot research topic. We focus on wireless sensor networks based on ZigBee technology. While sleep standard operation mode is defined for end devices, it is not the case for the rest of devices (routers and Coordinator), which usually always remain in active mode. We designed a formal optimization model for maximizing the enlarging of the battery life of routers and Coordinator, allowing us to delimit practical successful conditions. It was successfully tested with a standard ZigBee datasheet comprising technical data for sensors, routers, and coordinators. It was tested in a practical wireless sensor network assembly with XBee S2C devices. We derived, from the previous model, a novel but simple protocol of communication among routers and coordinators. It was tested in different use cases. We showed that when end devices generate traffic at regular intervals, the enlarging of the battery life of routers and Coordinator was possible only under certain use cases.
Reducing the consumption of energy and the network lifetime are the main challenges that affect wireless sensor networks (WSNs). High-quality clustering is one of the most important approaches for reducing the energy consumption in WSNs. Various criteria can be used to assess the quality of the clusters and considering all of these criteria can lead to high-quality clustering. In this study, we propose a method called the high-quality clustering algorithm (HQCA) for generating high-quality clusters. The HQCA method uses a criterion for measuring the cluster quality, which can improve the inter-cluster and intra-cluster distances as well as reducing the error rate during clustering. The optimal cluster head (CH) is selected based on fuzzy logic and according to various criteria such as the residual energy, the minimum and maximum energy in each cluster, and the minimum and maximum distances between the nodes in each cluster and the base station. The main advantages of this method are its high reliability, low error rate during the clustering process, the independence of key CHs, better scalability, and good performance in large-scale networks with a high number of nodes. The validity of the clustering quality is also measured based on external and internal criteria. Simulation results demonstrated that the HQCA-WSN method can significantly improve the energy consumption and network lifetime. The proposed method also significantly enhances the first node dies and last node dies metrics compared with similar methods.
The prototype is the first, step in building an Internet of Things(IoT) product. An IoT prototype consists of user interface, hardware devices including sensors, actuators and processors, backend software and connectivity. IoT microcontroller unit (MCU) or development board is used for prototyping. IoT microcontroller unit (MCU) or development board contain low-power processors which support various programming environments and may collect data from the sensor by using the firmware and transfer raw or processed data to an local or cloud-based server. NodeMCU is an open source and LUA programming language based firmware developed for ESP8266 wifi chip. Espruino , Mongoose OS, software development kit (SDK) provided by Espressif, ESP8266 add-on for Arduino are a few of development platforms that may program the ESP8266. ESP8266 may be used to either host the application or to offload all Wi-Fi networking functions from another application processor through its self contained Wi-Fi networking solution. ESP8266 has powerful on-board processing capabilities and sufficient storage that allow it to be integrated with minimal development up-front and minimal loading during runtime through its GPIOs(General Purpose input/output) with the sensors specific devices. ESP8266 has very low cost and high features which makes it an ideal module for Internet Of Things (IoT). It can be used in any application that require it to connect a device to local network or internet.
In the era of internet-of-things (IoT), many applications utilise wireless sensor networks (WSNs) including smart grids (SGs). Designing WSNs to fulfill the SGs requirements imposes some challenges such as limited power and signal propagation impairments, especially, in harsh environments. Consequently, saving power consumption in WSNs-based SGs is among the most significant challenges. The total power required at a certain sensor depends on two main parameters: the packet length and inter-node distance. This paper investigates the optimal packet length and inter-node distance to be utilised in a SG over six different environments aiming at maximising the network lifetime. The investigation is based on a link-layer model using Tmote Sky nodes taking into consideration the six environments impact. A mixed-integer programming (MIP) model is utilised to determine the best packet length and number of nodes for maximising the network lifetime. This model analyses the performance of maximum SG network lifetime over those environments and addresses the inter-node distance effect on the network lifetime maximisation. Simulation results show that decreasing the number of nodes covering a certain area is preferable to prolonging the network lifetime. Furthermore, for the considered models, the longer the packet length is, the longer the network lifetime will be.
Currently environment protection is a big challenge. One way of protection is focusing on power saving of electrical devices. Although computers and networks are not the main air polluting sources, they are contributing in a considerable way. Multiple proposals to reduce the power consumption have been presented due to the massive usage of the networking equipment. It is also important the economic profit (and not only the decreased pollution) derived by the reduction, at home and enterprises, of costs generated by the high power consumption of devices always on and, often, not utilized for a long time. In WiFi networks the main efforts to reduce consumption are focused on the user terminals, excluding the access points as main target. In this paper, we present a method to power saving focused on reducing consumption in the devices of access to the network, concretely of the access points. With this proposal, the consumption in the access points decreases and the costs would be reduced but keeping the utility for which they were designed.
Data gathering through wireless sensor networks (WSNs) has been used for the monitoring of endangered species. However, when it comes to animals that live in difficult access environments with large areas, where human access is extremely difficult, such as in polar regions, remote monitoring by traditional methods becomes complicated and even inefficient. This paper proposes the characterization of the animal random trajectories by means of the random walk model in order to select the appropriate detection range and number of nodes to guarantee a target detection probability. The animals are detected by static gatherer sensor nodes placed on land either by mobile sensor nodes attached to the animal or by land sensor nodes that detect them through movement, sound, or temperature among other methods. Due to their natural movement, the animal may be outside or inside the sensor nodes coverage radius. In order to reduce energy consumption, it is proposed that nodes be active and inactive, effectively increasing the system lifetime. As such, an inherent compromise between energy consumption and reporting efficiency is present in the design of the network. Building on this, careful network design is required in order to calculate the probability of successful detection and system lifetime. To this end, a mathematical model based on a Markov chain is proposed and developed. The model suitability is assessed via numerical simulations. The obtained results allow concluding that their trajectories can be modeled using specific phase type distributions. Finally, instead of considering that in polar regions, it is not feasible to have a conventional WSNs, where many nodes are placed together, single nodes are placed in strategic locations isolated among them, and communication between nodes is not possible. As such, it proposes a novel and simplified model, where a single sensor is used to analyze the performance of the complete multi-sensor network.
In the paper, the intelligent fish tank using STC89C52 as the control core embedded HC-SR04 ultrasonic distance measurement module and DS18B20 temperature sensor is introduced. This system can be used to remotely control and collect the data of the temperature and the level of water in the fish tank through WiFi module (ESP8266-01). When the water level is less than the default value, the system will be adjusted by adding water into the tank. At the same time, people could also get the data and control the tank whenever they want. The micro-controller is connected to the Internet through the WiFi module. With the help of MicroPython firmware, python programs are compiled within this WiFi module in order to connect to the WiFi at home, providing data transfer function. Android smart phones could connect to this system through WiFi and send commands. In this way, the fish tank could be controlled remotely to ensure the stability of the water temperature and level in the tank.
Smart homes have attracted much attention due to the expanding of Internet-of-Things (IoT) and smart devices. In this paper, we propose a smart gateway platform for data collection and awareness in smart home networks. A smart gateway will replace the traditional network gateway to connect the home network and the Internet. A smart home network supports different types of smart devices, such as in home IoT devices, smart phones, smart electric appliances, etc. A traditional network gateway is not capable of providing quality-of-service measurement, user behavioral analytics, or network optimization. Such tasks are traditionally performed with measurement agents such as optical splitters or network probes deployed in the core network. Our proposed platform is a lightweight plug-in for the smart gateway to accomplish data collection, awareness and reporting. While the smart gateway is able to adjust the control policy for data collection and awareness locally, a cloud-based controller is also included for more refined control policy updates. Furthermore, we propose a multi-dimensional awareness framework to achieve accurate data awareness at the smart gateway. The efficiency of data collection and accuracy of data awareness of the proposed platform is demonstrated based on the tests using actual data traffic from a large number of smart home users.
The need to deploy wireless sensor networks (WSNs) for real world applications, such as mobile multimedia for healthcare organizations, is increasing spectacularly. However, the energy problem remains one of the core barriers preventing an increase in investment in this technology. In this paper we propose a new technique to resolve the problems due to limited energy sources. Using a quaternary transceiver (in the architecture on a sensor node), instead of a binary one, which will use the amplitude/phase, modulator/demodulator units to increase the number of bits transmitted per symbol. The system will reduce the consumption of energy in the transmission phase due to the increased bits transmitted per symbol. Moreover, a neural network static random access memory (NN-SRAM) implementation in a clustering-based system for energy-constrained wireless sensor networks is proposed. The scheme reduces the total amount of energy consumption in storage and transmissions during the data dissemination process. Through simulation results based on Matlab and Spice software tools, it is shown that the neural network static random access memory implementation in a clustering based system reduces the energy consumption of the entire system by about to 76.99%.
Ubiquitous sensing allows smart cities to take control of many parameters (e.g., road traffic, air or noise pollution levels, etc.). An inexpensive Wireless Mesh Network can be used as an efficient way to transport sensed data. When that mesh is autonomously powered (e.g., solar powered), it constitutes an ideal portable network system which can be deployed when needed. Nevertheless, its power consumption must be restrained to extend its operational cycle and for preserving the environment. To this end, our strategy fosters wireless interface deactivation among nodes which do not participate in any route. As we show, this contributes to a significant power saving for the mesh. Furthermore, our strategy is wireless-friendly, meaning that it gives priority to deactivation of nodes receiving (and also causing) interferences from (to) the rest of the smart city. We also show that a routing protocol can adapt to this strategy in which certain nodes deactivate their own wireless interfaces.
The design of monitoring systems for marine areas has increased in the last years. One of the many advantages of wireless sensor networks is the quick process in data acquisition. The information from sensors can be processed, stored, and transmitted using protocols efficiently designed to energy saving and establishing the fastest routes. The processing and storing of data can be very useful for taking intelligent decisions for improving the water quality. The monitoring of water exchange in aquaculture tanks is very important to monitor the fish welfare. Thus, this paper presents the design, deployment, and test of a smart data gathering system for monitoring several parameters in aquaculture tanks using a wireless sensor network. The system based on a server is able to request and collect data from several nodes and store them in a database. This information can be postprocessed to take efficient decisions. The paper also presents the design of a conductivity sensor and a level sensor. These sensors are installed in several aquaculture tanks. The system was implemented using Flyport modules. Finally, the data gathering system was tested in terms of consumed bandwidth and the delay Transmission Control Protocol (TCP) packets delivering data from the sensors.
This paper concerns the implementation of an efficient underwater acoustic network suitable for long lasting environmental monitoring in fish farming. Several hardware and software solutions have been designed and implemented to extend the network lifetime and to make the system autonomous and suitable for such an application scenario. The proposed system is composed of different components. The SUNSET Software Defined Communication Stack (SDCS) is used to provide networking capabilities to underwater nodes communicating acoustically through AppliCon SeaModem modems. The Hydrolab Series 5 probes are used to monitor the water quality. Lifetime of underwater nodes is extended through the use of a novel
device that allows to harvest energy from underwater water currents via suitable propellers. In addition, novel sleep and wake up mechanisms have been designed and implemented into the underwater nodes to minimize the energy consumption of the system during the idle periods. The performance of the proposed system has been extensively evaluated in field by monitoring the water quality in three fish farming cages located in the Mediterranean Sea, Italy. The system has been connected to the Internet infrastructure allowing the users to easy interact with the underwater system in real-time. Our results confirm that the proposed system is suitable for long term monitoring providing a reliable and robust data collection scheme with an extended network life time.
Wireless Sensor Network (WSN) is known to be a highly resource constrained class of network where energy consumption is one of the prime concerns. In this research, a cross layer design methodology was adopted to design an energy efficient routing protocol entitled “Position Responsive Routing Protocol” (PRRP). PRRP is designed to minimize energy consumed in each node by (1) reducing the amount of time in which a sensor node is in an idle listening state and (2) reducing the average communication distance over the network. The performance of the proposed PRRP was critically evaluated in the context of network lifetime, throughput, and energy consumption of the network per individual basis and per data packet basis. The research results were analyzed and benchmarked against the well-known LEACH and CELRP protocols. The outcomes show a significant improvement in the WSN in terms of energy efficiency and the overall performance of WSN.
This project addresses the need for an expansion in the monitoring of marine environments by providing a detailed description of a low cost, robust, user friendly sonde, built on Arduino Mega 2560 (Mega) and Arduino Uno (Uno) platforms. The sonde can be made without specialized tools or training and can be easily modified to meet individual application requirements. The platform allows for internal logging of multiple parameters of which conductivity, temperature, and GPS position are demonstrated. Two design configurations for different coastal hydrographic applications are highlighted to show the robust and versatile nature of this sensor platform. The initial sonde design was intended for use on a Lagrangian style surface drifter that recorded measurements of temperature; salinity; and position for a deployment duration of less than 24 h. Functional testing of the sensor consisted of a 55 h comparison with a regularly maintained water quality sensor (i.e., YSI 6600 sonde) in Mobile Bay, AL. The temperature and salinity data were highly correlated and had acceptable RMS errors of 0.154 °C and 1.35 psu for the environmental conditions. A second application using the sonde platform was designed for longer duration (~3-4 weeks); subsurface (1.5-4.0 m depths) deployment, moored to permanent structures. Design alterations reflected an emphasis on minimizing power consumption, which included the elimination of the GPS capabilities, increased battery capacity, and power-saving software modifications. The sonde designs presented serve as templates that will expand the hydrographic measurement capabilities of ocean scientists, students, and teachers.
Batteryless sensors promise a sustainable future for sensing, but they face significant challenges when storing and using environmental energy. Incoming energy can fluctuate unpredictably between periods of scarcity and abundance, and device performance depends on both incoming energy and how much a device can store. Existing batteryless devices have used fixed or run-time selectable front-end capacitor banks to meet the energy needs of different tasks. Neither approach adapts well to rapidly changing energy harvesting conditions, nor does it allow devices to store excess energy during times of abundance without sacrificing performance.
This paper presents Stash, a hardware back-end energy storage technique that allows batteryless devices to charge quickly and store excess energy when it is abundant, extending their operating time and carrying out additional tasks without compromising the main ones. Stash performs like a small capacitor device when small capacitors excel and like a large capacitor device when large capacitors excel, with no additional software complexity and negligible power overhead. We evaluate Stash using two applications—temperature sensing and wearable activity monitoring—under both synthetic solar energy and recorded solar and thermal traces from various human activities. Our results show that Stash increased sensor coverage by up to 15% under variable energy-harvesting conditions when compared to competitor configurations that used fixed small, large, and reconfigurable front-end energy storage.
Nowadays, hospitals and government departments are struggling to reduce the health costs and improve the service quality. Hospitals rely mainly on nurses who have many duties including caring of patients, communicating with doctors , administering medicine and checking vital signs. Wireless body sensor network (WBSN) is one of the main applications that helps the medical staff in electronic health surveillance with early detection of critical physiological symptoms. However, the rapid growth in the patient number poses a high pressure on hospitals and increases the nurse duties. In addition, WBSN suffers from big data collected by the medical sensors. Therefore, collecting data is one of the major reason behind the exhaustion of limited energy. In this paper, we propose a novel and efficient framework for nurse-patient intelligent task organization that treats the scheduling problem in hospitals. Typically, our framework consists of three phases: data collection and transmission, patient classification, and nurse scheduling. The first phase uses the early warning system (EWS) to update the medical staff when the patient situation is changed. This will help to minimize the energy consumption in sensor and control its data transmission rate. Once the patient data are collected, the second level introduces an efficient classification model that grouping patients according to the severity level of their vital signs. The last phase proposes a nurse-scheduling algorithm based on the classification results and some priority metrics to balance the workload of the stressed medical personnel. We performed our simulation on real health sensor data collected from 18 patients, including various vital signs, while assuming a certain number of nurses. The obtained results show the effectiveness of our framework in conserving the sensor energy up to 95%, and ensuring a balanced nurse workloads where each nurse serves, during each period of one hour, an average of 5 patients with corresponding cumulative criticality of 62%.
Routing plays an essential role in ensuring normal and lasting operation of wireless body area networks (WBANs). However, existing routing schemes cause inefficient and unbalanced energy dissipation, which contributes to premature death of some nodes and high temperature within a small area of the body. In this article, we propose an adaptive time-varying routing (ATVR) protocol to address these issues. Unlike in conventional routing solutions, in our protocol a node may act as different roles (source node or relay node) and select different paths in disparate periods. This dynamic routing pattern helps to achieve a globally optimal routing solution. In ATVR, a node evaluation function and a path evaluation function are designed to reflect the node state and the path state respectively. Then, the path selection problem is transformed into a Hitchcock transportation problem, in which the nodes with worse node state act as source nodes (i.e., producers) and the nodes with better node state act as relay nodes (i.e., consumers). Then, this Hitchcock transportation problem is addressed by the AlphaBeta algorithm, in which the paths with less energy consumption and less path loss are selected to forward data. The experimental results show that our protocol has better performance in terms of energy consumption, network lifetime, and node temperature.
Underwater communication is a diverse and complex environment and has many problems. There are many problems in modeling and operating underwater networks. Physical and data link layer specifications for underwater networking should be taken into account in terms of quality of service metrics. In this study, we construct an underwater simulation environment as a cross-layer design consisting of physical and data link layers for the Internet of Underwater Things (IoUT) Network. The IoUT network has sensors with various priorities, a central node as a hub and a gateway as a buoy. The underwater sensors communicate with CSMA/CA-based IEEE 802.15.6 protocol on low frequencies. For a more realistic communication channel, all the features of the acoustic channel are designed and performance analyzes are discussed. In addition, the proposed IoUT is compared with terrestrial network results in terms of bit error rate, signal-to-noise ratio, packet loss ratio, end-to-end delay, radio receiver utilization, and throughput. We claim that we have made a realistic IoUT network, especially for divers, autonomous underwater vehicles with specific sensors, or for similar purposes.
The aquaculture industry has grown significantly in recent decades in order to meet global food demands, generating large economic gains in the countries that practice this activity. It is projected that by 2028, aquaculture will have a higher fish production than the wild catch sector. However, despite the high impact this industry has had on food production, there are a number of environmental and health factors that can affect and trigger fish diseases, causing significant economic losses. Among these diseases are those generated by the attack of pathogenic bacteria; more specifically, by the genera of gram-negative bacteria and, to a lesser extent, gram-positive bacteria. This chapter summarizes the information obtained by scientific sources on economic losses due to different types of contamination in the aquaculture industry, mainly the losses generated by the attacks of pathogenic bacteria. In addition, numerical data on production losses worldwide are presented according to the type of pathogenic bacteria. At the end of this chapter, the stages for the identification of bacterial diseases and the analysis of a fish farming system in the region of Huasteca Potosina, Mexico are detailed.
The primary goal of drip irrigation is to provide sufficient quantity of water and fertilizer to improve crop yield. An Efficient Scalable Data Collection Scheme (ESDCS) using Wireless Sensor Networks (WSNs) is designed to collect the sensor data from the agricultural field for predicting the amount of water required by the crop. In ESDCS, the waiting time based Cluster Head (CH) selection is proposed depending on the residual energy. Also, the utilization of Unmanned Aerial Vehicle (UAV) effectively collects the field sensor data over a wide area. The performance of the proposed ESDCS is tested in the sugarcane field for the automation of water irrigation and fertigation. The duration and amount of irrigation is calculated based on the crop evapotranspiration using crop co-efficient kc and daily weather condition. It is observed that the proposed system utilizes only 25.08 % of the water as compared with the existing irrigation system.
In wireless sensor networks (WSNs), the densely deployment and the dynamic phenomenon provide strong correlation between sensor nodes. This correlation is typically spatiotemporal. This paper proposes an efficient data collection technique, based on spatio-temporal correlation between sensor data, aiming to extend the network lifetime in periodic WSN applications. In the first step, our technique proposes a new model based on an adapted version of Euclidean distance which searches, in addition to the spatial correlation, the temporal correlation between neighbouring nodes. Based on this correlation and in a second step, a subset of sensors are selected for collecting and transmitting data based on a sleep/active algorithm. Our proposed technique is validated via experiments on real sensor data readings. Compared to other existing techniques, the results show the effectiveness of our technique in terms of reducing energy consumption and extending network lifetime while maintaining the coverage of the monitored area.
Due to the limit of computation and storage capability of devices, traditional security techniques face many challenges in the process of data transmission. Networks need to consume more energy and larger transmission for transmitting data in insecure network status. However, it is of great importance to deliver packets to the base station (or sink node) in a timely manner to support the delay-sensitive applications. For Wireless Sensor Networks (WSNs), it is challenging to satisfy end-to-end delay requirements due to the duty cycle adopted by the nodes, which can cause considerable delay because nodes can only transmit or receive information in their active periods (i.e., resulting in sleep delay). To address this issue, a dynamic duty cycle (DDC) scheme is proposed for minimizing the delay in WSNs. Specifically, we firstly investigate how the duty cycle affects the network delay. Then, the DDC scheme is devised to prolong the active period of nodes in non-hotspots areas. With a greater duty cycle, the forwarding node set remains awake with a larger chance. Consequently, the sleep delay of a node decreases, and the transmission delay is reduced. In addition, only the remaining energy of nodes is used to improve the performance. Thus, the DDC scheme does not damage network lifetime. Through analysis and experimental results, it is demonstrated that the DDC scheme can outperform other schemes. Compared with the existing fixed duty cycle (SDC) scheme, the transmission delay in the DDC scheme is reduced by 20–50%, the lifetime is increased by more than 16.7%, and the energy efficiency is improved by 15.3%–16.3%.
The Internet of Things (IoT) is experiencing rapid growth and being adopted across multiple domains. For example, in industry it supports the connectivity needed to integrate smart machines, components and products in the ongoing Industry 4.0 trend. However, there is a myriad of communication technologies that complicate the needed integration, requiring gateways to connect to the Internet. Conversely, using IEEE 802.11 (WiFi) devices can connect to existing WiFi infrastructures directly and access the Internet with shorter communication delays and lower system cost. However, WiFi is energy consuming, impacting autonomy of the end devices. In this work we characterize a recent WiFi-enabled device, namely the ESP8266 module, that is low cost and branded as ultra-low-power, but whose performance for IoT applications is still undocumented. We explore the built-in sleep modes and we measure the impact of infrastructure parameters beacon interval and DTIM period on energy consumption, as well as packet delivery ratio and received signal strength as a function of distance and module antenna orientation to assert area coverage. The ESP8266 module showed suitability for battery powered IoT applications that allow 2–4 days recharge cycles on a 1000mAh battery with seconds-scale transmission intervals.
Cabled intelligent systems bring with them the complexities of structures, the complications of data measurements and transmission, and a limited scale of application. A wireless sensor network is used to eliminate these disadvantages, however reliability of data transmission and energy saving in a wireless sensor network are two challenges that still need to be addressed. The design information on three types of nodes in a wireless sensor network is described in detail. Tree topology for WSN is adopted to decrease the packet loss rate and improve reliability of data transmission. Allowing sensor nodes to sleep and reorganising the data frames are the two approaches used to achieve energy-saving. The experimental results demonstrate the usefulness of these approaches in solving the challenges.
Abstract: In wireless sensor networks (WSNs), the densely deployment and the dynamic phenomenon provide strong correlation between sensor nodes. This correlation is typically spatio-temporal. This paper proposes an efficient data collection technique, based on spatio-temporal correlation between sensor data, aiming to extend the network lifetime in periodic WSN applications. In the first step, our technique propose a new model based on an adapted version of Euclidean distance which searches, in addition to the spatial correlation, the temporal correlation between neighboring sensor data. Based on this correlation, a subset of sensors are selected for collecting and transmitting data to the sink, in the second step, based on a sleep/active algorithm. Our proposed technique is validated via experiments on real sensor data readings. Compared to other existing techniques, the results show the effectiveness of our technique in terms of reducing energy consumption and extending network lifetime while maintaining the coverage of the monitored area.
The cubic spline interpolation method is proba- bly the most widely-used polynomial interpolation method for functions of one variable. However, the cubic spline method requires solving a tridiagonal matrix-vector equation with an O(n) computational time complexity where n is the number of data measurements. Even an O(n) time complexity may be too much in some time-ciritical applications, such as continuously estimating and updating the flight paths of moving objects. This paper shows that under certain boundary conditions the tridiagonal matrix solving step of the cubic spline method could be entirely eliminated and instead the coefficients of the unknown cubic polynomials can be found by solving a single recurrence equation in much faster time.
In 1-dimensional queue wireless sensor networks, how to balance end-to-end latency and energy consumption is a challenging problem. However, traditional best path routing and existing opportunistic routing protocols do not address them well because relay hop counts are usually much more, and the link appears more unreliable compared with general mesh topology. In this work, we formulate these 2 problems as a multiobjective optimization problem. Specifically, we first classify network packets into types of time tolerant and time critical and introduce a residual energy collection mechanism of neighboring nodes for forwarder set selection. We then propose a time-aware and energy-efficient opportunistic routing protocol (TE-OR) to optimize energy consumption and to reduce latency for time-critical packets. We evaluate TE-OR by different parameters and compare it with existing protocols. The performance results show that TE-OR achieves a trade-off between energy consumption and time delay and balances energy consumption among nodes while guaranteeing the latency of time-critical packets is minimized.
Balancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes overloaded, the likelihood of higher latency, energy loss, and congestion becomes high. In this paper, we propose an optimal load balanced clustering for hierarchical cluster-based wireless sensor networks. We formulate the network design problem as mixed-integer linear programming. Our contribution is 3-fold: First, we propose an energy aware cluster head selection model for optimal cluster head selection. Then we propose a delay and energy-aware routing model for optimal inter-cluster communication. Finally, we propose an equal traffic for energy efficient clustering for optimal load balanced clustering. We consider the worst case scenario, where all nodes have the same capability and where there are no ways to use mobile sinks or add some powerful nodes as gateways. Thus, our models perform load balancing and maximize network lifetime with no need for special node capabilities such as mobility or heterogeneity or pre-deployment, which would greatly simplify the problem. We show that the proposed models not only increase network lifetime but also minimize latency between sensor nodes. Numerical results show that energy consumption can be effectively balanced among sensor nodes, and stability period can be greatly extended using our models.