An Integrated Hybrid Energy Harvester for Autonomous Wireless Sensor Network Nodes

Herein, an Internet of Things (IoT) system with remote reconfigurable wireless sensor network (WSN) nodes is presented and its application to measure group work activity in a classroom is discussed. This IoT system consists of wiki pages, bots, (defined as remotely controlled devices), gateways, and remotely reconfigurable WSN Nodes. The bots/gateways and WSN nodes of the IoT system are controlled by scripts written on wiki pages that are read by the bot/gateways, which then forward the commands contained in the scripts to the WSN nodes. Each WSN node then executes the commands, obtains the resulting sensor values, and then returns those values to the bot/gateways. The bot/gateways relay the values and write them onto the wiki page. Each WSN node is flexible and reconfigurable because its behavior can be changed by the script commands written onto the wiki page. Using this IoT system, we created a measurement system to evaluate activity levels of group work in a classroom. After developing the measurement system, we found it was possible to adjust the sampling rate for obtaining sensor values simply by rewriting the script on the wiki page without directly manipulating the WSN.

Self-powered wireless sensor networks based on the radioisotope thermoelectric generator for aquatic temperature monitoring

A Markov based unified model for energy harvesting nodes in wireless sensor networks is proposed. Using the presented model, the probability of event loss due to energy run out as well as an analytical vulnerability metric, namely average time to energy run-out, are derived. The results provide insight into the performance of energy harvesting nodes in wireless sensor networks as well as design requirements for such nodes. The proposed vulnerability metric can be used in the various harvesting aware techniques at different protocol layers.

This paper is a performance survey on the residual energy of nodes in a wireless sensor network (WSN). Wireless sensor nodes are limited in application due to constraints in energy of nodes’ battery and addressing the problem of energy management is essential to lengthen the life of a deployed network of wireless sensors. In the method used, Sensor nodes were equally distributed on the complete border of the network field. An exclusive cluster, six hierarchical, and eighteen hierarchical clusters were formed based on even partition of the border of the network field. The choice of Cluster Head (CH) within each cluster formed was based on selection of a sensor node with minimal transmission distance to the Base Station. A comparison of the various hierarchical scenarios was simulated in MATLAB. The mean residue energies (MREs) after the network lifetime are 3%, 52%, and 35% of the initial node energy for single, six and eighteen hierarchical clusters respectively. The residual energies of sensor nodes above the recorded MREs were taken on a class interval of 50’s of the node identity (nid) and respective averages of residual energies of sensor nodes (AEr) of each selected class interval were evaluated. Based on the result, certain categories of sensor nodes at a section of the network field indicated that the battery energy of the affected nodes was not maximally utilized thereby leading to enormous residual energy. It was concluded that the residual energy of nodes in a WSN could be optimized to extend the lifetime.

Localization of mobile nodes in wireless sensor network gets more and more important, because many applications need to locate the source of incoming measurements as precise as possible. Many previous approaches to the location-estimation problem need know the theories and experiential signal propagation model and collect a large number of labeled samples. So, these approaches are coarse localization because of the inaccurate model, and to obtain such data requires great effort. In this paper, a semi-supervised manifold learning is used to estimate the locations of mobile nodes in a wireless sensor network. The algorithm is used to compute a subspace mapping function between the signal space and the physical space by using a small amount of labeled data and a large amount of unlabeled data. This mapping function can be used online to determine the location of mobile nodes in a sensor network based on the signals received. We use independent development nodes to setup the network in metallurgical industry environment, outdoor and indoor. Experimental results show that we can achieve a higher accuracy with much less calibration effort as compared with RADAR localization systems.

In this paper, a Thermal Energy Harvesting (TEH) technique with power management circuit to condition the output power harvested from low-level heat energy sources with the intention to extend the lifetime of the wireless sensor network (WSN) node is presented. By using this approach, it can extends the WSN nodes lifespan from few days or even weeks (in a case of batteries powered) up to years or even infinity depend on the heat gradient availability. In this context, low-level heat (below 150 °C) sources survey is conducted to evaluate the amount of heat gradient produced by electronic devices and mechanical machinery, and studies the amount of electrical energy harvested by employing the TEH technology. Peltier elements thermoelectric cooler TEC manufactured by Laird xc31 connected in series was used as thermal energy harvester in the presented circuit. A step-up DC-DC switching circuit based on MAX757 IC. was used as the power management and conditioning circuit to boost up the harvested voltage up to 5V. A wireless sensor node was designed based on an efficient low power microcontroller equipped with a temperature sensor and a RF module powered by the proposed TEH circuit. It consumed an average electrical power of about 50mW in the active mode and about 15mW at the sleeping mode. This node was able to transmit the measured data to the next node every 10 seconds whenever there is at least temperature gradient of 30°C on the Peltier element surfaces. In contra, 7.4V rechargeable lithium polymer battery at 500mAh from KingMax only allowed the node to transmit the data for 3 continuous days.

Most of the proposed algorithms focus on static networks of sensors with either static or mobile anchors, in which the Monte Carlo localization algorithm is a typical one for localizing nodes in a mobile wireless sensor network. But the radio range being all different or inconstant in this algorithm leads to reduce the accuracy of localization and the efficiency of the algorithm itself. In this article, we propose the novel rang-based stochastic Monte Carlo localization algorithm for wireless sensor networks specifically designed with mobility to improve the accuracy of localization by dealing with the different radio ranges of sensors, and being bound to the narrow sampling area. Our simulation experimental results show that the rang-based stochastic Monte Carlo localization algorithm has improved the accuracy and stability of the estimated locations.

Contemporary Wireless Sensor Network (WSN) nodes can use one of the three options for their power supply: the electricity grid, the batteries or the wide range of energy harvesting systems. The possibility of identification for power source type used on the WSN node provides valuable information on the node potential resources, that could be used e.g. for the improvement of the whole WSN lifetime. Additionally, the possibility of WSN node Power Source Type Identification (PSTID) allows unifying the node's software through implementing the methods for WSN node operation adaptation based on the information about identified power source type and allows the support of efficient WSN node power source changeability without node reprogramming. To the best of our knowledge, this article is the first one which focuses on the problem of WSN node PSTID and suggests the appropriate solution. The suggested method allows implementing efficient PSTID with minimum resource consumption while not requiring any hardware components added to the WSN node. The suggested method has been implemented and tested in practice using different WSN node platforms and power source options. The evaluation results are presented in the paper as well.

In this letter, we propose two novel dynamic duty-cycle scheduling schemes (called DSR and DSP) in order to reduce sleep latency, while achieving balanced energy consumption among sensor nodes in wireless sensor networks (WSNs) with energy harvesting capability. In DSR, each sensor node is allowed to adjust its duty-cycle according to the current amount of residual energy only. Since the residual energy of nodes in energy-harvesting WSNs can increase over time due to their harvesting opportunity, the estimation of prospective increase in their residual energy is useful to achieve our goal. Hence, DSP allows each of sensor nodes to reduce its duty-cycle more aggressively in proportion to such an increase. Through NS-2 simulations, we verified that our proposed schemes outperform the duty-cycle scheduling scheme used in a representative existing MAC protocol such as RI-MAC.

Indoor air quality (IAQ) is a term that describes the air quality of the building occupants. IAQ can be influenced by physical factors like dust particles, chemical or gaseous pollutants (including carbon dioxide, carbon monoxide and volatile organic compounds) and biological factors like molds and bacteria growth which largely depend on temperature and humidity condition of a room. These kinds of pollutants can induce a bad health effects to building occupants. In this study, a web-based system for monitoring indoor air quality is presented. The system has been developed to monitor the environmental parameters of air quality like gaseous pollutants, particulate matter, temperature and humidity that can affect the occupant's comfort, health and indoor work environment if not properly maintained. Generally, the system consists of a desktop computer acting as base station, an array of metal oxide gas sensor, temperature and humidity sensor, particle dust sensor and wireless sensor network (WSN) node as a wireless transmitter. The measured data from sensors are sent to base station via WSN node. The computer will log the collected data using a self-developed server program which can be accessed through the web.

The accurate estimations of distances among nodes in wireless sensor networks(WSN) are premises for the accurate estimations of nodes' positions and the accurate reconstruction of the networks' topology. In a complex environment, nodes stochastically move, and the variance of state noise is unknown and variable, and obstacles possibly exist among nodes. Now, the accurate estimations of distances among nodes in the WSN in a complex environment are still unresolved. In this paper, an adaptive Kalman filter(AKF) is used to solve the accurate estimations of distances among nodes in the WSN. Through simulations, the results indicate that the AKF is capable of accurate estimations of distances among nodes. Therefore, the AKF is a very effective method to solve the accurate estimations of distances among nodes in the WSN in a complex environment.

Designing a low power Wireless Sensor Network (WSN) Node is highly important for getting longer lifetime. Radio transmission and data processing are energy consuming processes. No compensation can be done in the field of energy consumption in radio transmission. This paper concentrates on energy reduction in the field of On the node processing. Due to limited energy supply from batteries the node must trade communication for on the node computation. Many of the WSN data processing algorithms use parallel prefix operations as common denominator. Parallel Prefix Operations have the form of Binary Tree Architecture. Binary tree architecture uses more number of Processing Element (PE) s. Parallel Prefix operations using Binary Tree Architecture (BTA) uses more area and more power. Binary Tree Architecture can be modified in to area and power efficient Folded Tree Architecture (FTA). A Digital comparator is a hardware electronic device that has two binary inputs and determines whether one number is greater than, less than or equal to the other number. The comparators are widely used in Micro Controller Units (MCUs) of WSN nodes. This is a crucial data path element of image and signal processing architectures. By using Folded Tree Architecture Area and Power Efficient Comparator Architecture can be formed. Thus Power and Area efficient WSN node can be formed.

Wireless Sensor Networking is an important area of research due to its wide and varied applications, especially in environmental monitoring and health monitoring. Battery-powered Wireless Sensor Networks (WSNs) face the problem of limited lifetime due to fast depletion of charge of typical batteries. The incorporation of energy harvesting in WSNs thus becomes crucial for extending the lifetime of battery-powered WSNs, while enhancing the network performance. The Medium Access Control (MAC) protocol has to be properly designed so as to ensure proper operation of the sensor nodes in WSNs with energy harvesting. This paper presents a MAC protocol for energy harvesting WSNs, based on IEEE 802.15.4 MAC, which takes into account the power consumption of the sensor nodes during operation and the battery discharge characteristics. This MAC protocol also employs duty cycle adaptation based on node energy.

Wireless Sensor Network (WSN) suffers from different malware attacks. Several traditional approaches are proposed for detecting fault nodes in WSN. It is necessary to redistribute the nodes and detect the fault nodes while modifying the WSN parameters. Faults can occur in the nodes interrupting the continuous communication process of the nodes in the WSN. The main reason for a fault is also the induction of the energy drain in a node to the maximum possible level, failure of links in communicating nodes due to bandwidth constraints, and attacks induced by the malicious nodes. To solve this problems, Extended Finite State Machine based Fault Tolerance (EFSM_FT) in the WSN is used to detect the fault sensor nodes in the WSN. In this approach, called the finite state machine (FSM), state prompted is modified while a set of situations are true. Every node can be absolutely to be faulty or not establishing on the sensor node states. Using sensor energy, drop rate, forward sensor rate, sensor obtained rate, bandwidth, and transmission delay, this method finds the node with the fault. As a result, this approach accurately detects the faulty nodes in the WSN. The simulation outcomes illustrate the proposed method has a better fault detection ratio and minimizes the false negative ratio in the WSN.

Recently, the technology of wireless sensor networks (WSNs) experiences a growing use in home automation or advanced industry infrastructure applications. Despite a strong interest of industries in this technology, key issues like miniaturization and security of WSN nodes has not been solved yet. State-of-the-art WSN nodes do not provide credible security nor satisfying configurability and miniaturized implementations. This publication deals with these limitations and presents a WSN node that provides security, configurability, and a miniaturized design. To show the sensor node feasibility, the WSN node is implemented within a smart home demonstrator. Additionally, a miniaturized pre-study WSN node design is presented using the novel embedded wafer level ball grid array (eWLB) packaging technology. Furthermore, an eWLB based WSN node design is proposed that further miniaturize the presented WSN node.