Artificial Intelligence for Solar Energy Harvesting in Wireless Sensor Networks

Solar cells have been extensively investigated for wireless sensor networks (WSN). In comparison to other energy harvesting techniques, solar cells are capable of harnessing the highest amount of power density. Furthermore, the energy conversion process does not involve any moving parts and does not require any intermediate energy conversion steps. Their main drawback is the inconsistent amount of energy harvested due to the intermittency and variability of the incoming solar radiation [1]. Consequently, being able to predict the amount of solar radiation is important for making necessary decisions regarding the amount of energy that can be utilized at the sensor node. We demonstrate that artificial intelligence (AI) can be used as an effective technique for predicting the amount of incoming solar radiation at these sensor nodes. We show that a Support Vector Machine (SVM) regression technique can effectively predict the amount of solar radiation for the next 24 hours based on weather data from previous days. We reveal that this technique outperforms other state of the art prediction methods for WSNs. To assess the performance of our proposed solution, we use experimental measurements that were collected for a period of two years from a weather station installed by Beijing Sunda Solar Energy Technology Company [2]. We also demonstrate how the harvested energy can be regulated using an innovative Power Management Unit [3]

Artificial Intelligence for Solar Energy Harvesting in Wireless Sensor Networks

Solar cells have been extensively investigated for wireless sensor networks (WSN). In comparison to other energy harvesting techniques, solar cells are capable of harnessing the highest amount of power density. Furthermore, the energy conversion process does not involve any moving parts and does not require any intermediate energy conversion steps. Their main drawback is the inconsistent amount of energy harvested due to the intermittency and variability of the incoming solar radiation [1]. Consequently, being able to predict the amount of solar radiation is important for making necessary decisions regarding the amount of energy that can be utilized at the sensor node. We demonstrate that artificial intelligence (AI) can be used as an effective technique for predicting the amount of incoming solar radiation at these sensor nodes. We show that a Support Vector Machine (SVM) regression technique can effectively predict the amount of solar radiation for the next 24 hours based on weather data from previous days. We reveal that this technique outperforms other state of the art prediction methods for WSNs. To assess the performance of our proposed solution, we use experimental measurements that were collected for a period of two years from a weather station installed by Beijing Sunda Solar Energy Technology Company [2]. We also demonstrate how the harvested energy can be regulated using an innovative Power Management Unit [3]

Autonomous Energy Management system achieving piezoelectric energy harvesting in Wireless Sensors

International audienceWireless Sensor Networks (WSNs) are extensively used in monitoring applications such as humidity and temperature sensing in smart buildings, industrial automation, and predicting crop health. Sensor nodes are deployed in remote places to sense the data information from the environment and to transmit the sensing data to the Base Station (BS). When a sensor is drained of energy, it can no longer achieve its role without a substituted source of energy. However, limited energy in a sensor's battery prevents the long-term process in such applications. In addition, replacing the sensors' batteries and redeploying the sensors is very expensive in terms of time and budget. To overcome the energy limitation without changing the size of sensors, researchers have proposed the use of energy harvesting to reload the rechargeable battery by power. Therefore, efficient power management is required to increase the benefits of having additional environmental energy. This paper presents a new self-management of energy based on Proportional Integral Derivative controller (PID) to tune the energy harvesting and Microprocessor Controller Unit (MCU) to control the sensor modes

Game theory based Ad-hoc On Demand Distance Vector Routing Protocol to Extend the Wireless Sensor Networks Life Time

This paper proposes a solution to increase the energy life time of wireless sensor networks (WSNs) via a concept of game theory enabled ad-hoc on demand distance vector (AODV) routing algorithm. Game theory is an optimal promising candidate for decision making in a wireless networking scenario to find the optimal path for data packets transfer between source node and destination node, where combination with the AODV routing algorithm, a procedure of game theory enabled AODV (GTEAODV) is developed and proposed in this research paper. The developed and proposed methodology is validated through simulation in NS2 environment and the results show an improvement in energy life time of the order of 30-35% in comparison to the existing routing methodology which uses co-operative routing techniques among the nodes in WSN. Further, the throughput of game theory enabled adhoc on demand routing is also highly improved in comparison to existing traditional approaches though obtained results. Though, game theory approach is an existing approach concatenation of it with AODV can provide increased network performance which is significant as portrayed in research results shown in the paper. Hence, by virtue of providing enhanced energy life time and data security through the nature of the algorithm, the proposed GTEAODV algorithm can be employed in defence applications for secure data transmission and reception for forthcoming deployment of 5G systems which are blossoming in world wide scenario

Cost benefit analysis of photovoltaic technology adoption at rest and service area for Malaysia Highway

Photovoltaic technology is cleaner technology that’s served the needs in reducing energy demand consumption. However, the demand for this technology is still weak due to its high cost of installation and maintenance. Besides that, rest and service areas (RSAs) are facilities that operate 24/7 consuming high energy demand. However, the combination of this technology for RSAs seems beneficial since it carries the benefits not only as part of alternative energy, but it will keep the whole environment from its footprint clean. Hence, this research aims to identify the cost-benefit analysis on PV technology, which assists decision-makers, stakeholders and highway concessionaires in selecting the best PV technology for RSAs. To achieve this aim, issues and challenges, types of PV technology and cost analysis have been investigated. Microsoft Excel and RETScreen Expert software have been used to evaluate the economic and environmental aspects. Five semi-structured interviews were conducted. All costs incurred were collected from manufacturers and governmental agencies. The study revealed that high initial cost of photovoltaic system, lack of public awareness and lack of government incentives are the key lever issues that hindering the prosperity of this technology in the Malaysian market. Besides that, it reveals that the total initial cost of monocrystalline and poly-crystalline PV system estimated to be (MYR 715400 and MYR 518500) respectively. The financial indicators for the monocrystalline PV system were found to be (MYR 1513182, 17.6% and 3.1) for (net present value, internal rate of return and benefit-cost ratio) respectively. While the poly-crystalline PV system were found to be (MYR 1440253, 21.5% and 3.8) for (net present value, internal rate of return and benefit- cost ratio) respectively. For the environmental analysis, monocrystalline and poly-crystalline reduce the GHG emission at Machap RSA by (25.6% and 22.3%), respectively. From this, concludes that poly-crystalline is more economical however it can be improved for monocrystalline providing more space area is being added up

Fuzzy Election based Optimization Algorithm (FEBOA) And Energy Harvesting Possibilistic FUZZYC-Means (EHFPCM) Clustering for EH-WSN

Wireless Sensor Network (WSN) includes of many nodes by restricted energy resources. Energy efficiency and harvested energy are major important issues in the WSN. Studies recently conducted have demonstrated that clustering is an effective way to increase energy efficiency. Energy Harvesting- Wireless Sensor Network (EH-WSN) is a flexible strategy for even clustering and Cluster Head (CH) selection is helpful to maximize network constancy and energy efficiency. In this paper, Energy Harvesting Possibilistic Fuzzy C-Means (EHFPCM) clustering is introduced to improve harvested energy usage by maintaining the consistency, connectivity, and balancing of harvested energy consumption in EH-WSN. It is based on Data Transmission (DT) and Cluster Establishment (CE). During CE, PFCM clustering is introduced for cluster formation. PFCM clustering divides the network into clusters. Each area forms a group and chooses one or more CH based on the multi-criteria like energy, distance to neighbors, distance to the Base Station (BS), and network load. In a cluster, the Fuzzy Election Based Optimization Algorithm (FEBOA) selects the CH according to the multi-criteria. It desires to receive packets from Cluster Member (CM), aggregate the received packets, and subsequently forward it to DT. DT, every CM wakes up during its designated working time and transmits the data it has gathered to the CH in the cluster. Lastly, measures such as Residual Energy (RE), Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), energy consumption, and average delay for transmission are used to measure the results of routing protocols

Magnetic Field Energy Harvesting in Railway

Magnetic field energy harvesting (MFEH) is a method by which a system can harness an ambient, alternating magnetic field in order to scavenge energy. Presented in this article is a novel application of the concept aimed at the magnetic fields surrounding the rail current in electrified railway. Due to its noninvasive nature, the approach has the potential to be widely deployed as a part of low-cost trackside condition monitoring systems in order to increase lifetime and reduce maintenance requirements. In this article, the viability of MFEH in railway is substantiated experimentally—two different configurations are assessed both in a controlled laboratory environment as well as in situ along Norwegian railway. When placed near an emulated section of railway carrying 200 A in the laboratory, the power output of the system is up to 40.5 mW at 50 Hz and 4.15 mW at 16 23Hz . In the field, the prototype system harvests 109 mJ from a single freight train passing by, rendering an estimated daily energy output of 1.14 J in a moderately trafficked location. It is argued that the approach could indeed eliminate the need for battery replacements and potentially increase the lifetime of an energy-efficient, battery-powered condition monitoring system indefinitely.acceptedVersio

Application and Energy-Aware Data Aggregation using Vector Synchronization in Distributed Battery-less IoT Networks

The battery-less Internet of Things (IoT) devices are a key element in the sustainable green initiative for the next-generation wireless networks. These battery-free devices use the ambient energy, harvested from the environment. The energy harvesting environment is dynamic and causes intermittent task execution. The harvested energy is stored in small capacitors and it is challenging to assure the application task execution. The main goal is to provide a mechanism to aggregate the sensor data and provide a sustainable application support in the distributed battery-less IoT network. We model the distributed IoT network system consisting of many battery-free IoT sensor hardware modules and heterogeneous IoT applications that are being supported in the device-edge-cloud continuum. The applications require sensor data from a distributed set of battery-less hardware modules and there is provision of joint control over the module actuators. We propose an application-aware task and energy manager (ATEM) for the IoT devices and a vector-synchronization based data aggregator (VSDA). The ATEM is supported by device-level federated energy harvesting and system-level energy-aware heterogeneous application management. In our proposed framework the data aggregator forecasts the available power from the ambient energy harvester using long-short-term-memory (LSTM) model and sets the device profile as well as the application task rates accordingly. Our proposed scheme meets the heterogeneous application requirements with negligible overhead; reduces the data loss and packet delay; increases the hardware component availability; and makes the components available sooner as compared to the state-of-the-art.Comment: 10 pages, 11 figure