This research explores how fuzzy logic-based clustering and network coding work together to improve energy efficiency in Wireless Sensor Networks (WSNs), The study investigates the use of fuzzy logic to enhance cluster head selection based on node energy levels, closeness, and density, resulting in enhanced communication efficiency and decreased energy usage. The study also investigates the capacity of network coding to improve data transmission efficiency and save energy. By conducting thorough simulations on the NS2 platform, the effects of various tactics on the network's operational lifespan and energy consumption patterns are assessed individually and in combination. The results show notable enhancements in network durability and energy conservation through the integration of various methods. This study enhances the theoretical comprehension of energy-saving mechanisms in Wireless Sensor Networks (WSNs) and offers significant perspectives for creating more resilient and energy-efficient sensor networks.

The LTE NB-IoT standard enables battery operated sensor nodes with a battery runtime of several years while using existing network infrastructure for data transmission. The data rate of this standard is very low, and a significant packet delay is inevitable. The use of the MQTT protocol is not optimal for NB-IoT connected applications but this protocol is widely used in IoT applications and an extension of existing applications by a better suitable protocol is difficult. The effects of the various LTE NB-IoT related parameters on the average current consumption is already known. This paper focuses on the impact of the different MQTT related parameters, i.e. QoS level, segmentation caused by very large payloads, and the clean session flag, on the required charge for a complete message transmission cycle. The individual MQTT parameters each have a relevant impact on the average required current consumption. However, the accumulated charge over a complete cycle is mainly caused by the connection handling. Therefore, the payload should be buffered as much as possible.

In agriculture, it becomes more and more important to have detailed data, e.g. about weather and soil quality, not only in large scale classic crop farming applications but also for urban agriculture. This paper proposes a modular wireless sensor node that can be used in a centralized data acquisition scenario. A centralized approach, in this case multiple sensor nodes and a single gateway or a set of gateways, can be easily installed even without local infrastructure as mains supply. The sensor node integrates a LoRaWAN radio module that allows long-range wireless data transmission and low-power battery operation for several months at reasonable module costs. The developed wireless sensor node is an open system with focus on easy adaption to new sensors and applications. The proposed system is evaluated in terms of transmission range, battery runtime and sensor data accuracy.

Battery energy is limited in Wireless Sensor Networks (WSNs); thus energy is a key element in designing WSNs. In particular, the effective utilization of energy becomes the major challenge during the design of routing protocols for WSNs, and the ultimate aim of the routing protocols is to extend the network lifespan of Wireless Sensor Networks by efficiently utilizing node energy. Clustering is one kind of mechanism in Wireless Sensor Networks to prolong the network lifetime and to reduce overall energy consumption. This paper used fuzzy logic for electing cluster heads based on 7 different descriptors–(delay, distance from the base station, RSSI, density, residual energy, location suitability, and Compacting) in each round. Dedicated network coder nodes in the bottleneck zone used network coding algorithms to improve data transmission rate. This sequentially improves the overall network lifetime. NS2 simulation tool and C++ programming language have been used to simulate and implement the proposed routing protocol. The simulation results proved that EEE-FL-NC protocol outperforms based on throughput, energy consumption, and the average lifetime of the network while comparing with the state-of-the art protocols like FL-NC-EEC, LEACH, K-means-LEACH, FL-EEC, and F-LEACH- [1]/D.

Recent development of the Internet of Things (IoT) dramatically increased the importance of energy-efficiency of Wireless Sensor Networks (WSN). In this paper, comparing and analyzing the performance of Enhanced Energy-Aware Multi-Hop Hierarchical Protocol (EEAHP) with the Low Energy Adaptive Clustering Hierarchy protocol (LEACH) and Energy Aware Multi-hop Multi-path Hierarchical protocol (EAMMH) for Wireless Sensor Networks. The average energy of each sensor node, number of dead nodes, number of packets send to Base Station in each round are the main metrics used to analyze the performance of these protocols. MATLAB is used to perform analytical simulations.

Wireless Sensor Networks playing an important role in applications where human interaction is difficult. WSN is extensively used in real-time applications like surveillance systems, environmental monitoring systems, disaster management and health monitoring, etc. Since sensor nodes are deployed in isolated areas, recharging or replacing node batteries is difficult. So for the better performance of the network, it's important to improve network lifetime by increasing sensor nodes' energy efficiency. Data aggregation methods and energy-efficient routing algorithms have an important role in WSN to tackle the problem with network lifetime. Hierarchical energy-efficient routing protocols are trending in the WSN research field, which helps to improve overall network lifetime by increasing the lifetime of sensor nodes by minimizing the energy consumption of each node in the network. In this paper, compared and investigated the performance of the Fuzzy Logic-Network Coding-Energy Efficient (FL-NC-EE) routing protocol with the other energy-efficient clustering protocols like LEACH, LEACH-FL, K Means-LEACH and FL-EE/D using NS2. The result shows that the FL-NC-EE protocol outperforms in terms of energy efficiency and network lifetime compared to the other protocols discussed in this paper.

Abstract Wireless networks can have different architectures depending on the application context. In this paper, we concentrate on a wireless network that supports a gateway to a server being accessible from the internet. Many typical related applications can be found in IoT and Industry 4.0. Focus of this paper is on the design of a sensor node that is energy self-sufficient and supports the LoRa communication standard. By using LoRa long range wireless communication of up to several kilometres range becomes possible. Therefore, large areas can be covered by using multiple sensor nodes. Special attention had been on low-power design aspects to run the node with rechargeable battery and small photovoltaic cell only. Example application is ambient monitoring and support of an aquaponics project.

This paper addresses the NLOS detection and error mitigation of differential time of arrival (TDoA) topologies in localization systems. The localization error of such systems is hard to detect mainly due to the differential structure of the network. The proposed channel detection method is based on the logistic regression method which is very simple and requires low amount of resources for detection of the channel condition. The detection process is based on extraction of the probability of the channel for each anchor. In practical results the overall accuracy of 87% is achieved which is only slightly less than other powerful methods such as support vector machines (SVM) which requires much more resources. The localization engine used for this system is based on the robust H ∞ filter. For mitigation of the error, two approaches are proposed. The first method is based on the modification of the variance and the second one is based on the modification of innovation term in update equation of the filter. The results of practical experiments indicate that the system can effectively determine and suppress the NLOS error up to 80%.

In contrast to classical light bulbs, LEDs used for lighting applications achieve greater energy efficiency. Nevertheless, LEDs are not lossless; power loss generates heat in the semiconductor junctions as well as in the light conversion material. High temperature significantly impairs the luminescent material and results in degeneration of the luminescent encapsulation material whereby its transparency is reduced. Avoiding high temperatures of an LED extends the live span and guarantees light quality. Therefore, temperatures of the p-n junction has to be measured and controlled by the LED driver. One considered constraint for implementing a temperature controller in the LED driver is the use of a hardware description language for the digital part to limit design complexity. Common temperature estimation methods are either mathematical to complex to be implemented into the LED driver or not accurate enough to protect p-n junctions against high temperatures. This paper describes a less complex mathematical approach to determine the p-n junction temperature based on the forward-voltage method. The related calculations are of moderate complexity allowing integration into a small size custom IC.

This paper evaluates and compares the accuracies of the three commonly used Bayesian filters namely extended Kalman Filter (EKF), H-infinity filter and unscented Kalman filter (UKF) in differential time of arrival (TDoA) localization topology in ultra-wide band (UWB) systems. Furthermore, two non-line of sight (NLOS) error suppression mechanisms are introduced and the accuracies of these methods when applied on the mentioned filters are evaluated. The results are evaluated in several trials both in LOS and NLOS conditions using a mobile node carried by a robot. In practical trials, it was witnessed that the H-infinity filter is more robust against non-ideal conditions with excessive noise or inaccurate model of the system whereas the UKF filter has the highest localization accuracy in nearly ideal cases such as LOS. In NLOS conditions, the proposed methods are able to suppress the error up to 50% with the prerequisite that the NLOS condition is correctly determined.

This paper discusses the synchronization issues of the unilateral TDoA method applied in ultra-wideband (UWB) localization systems. Focus of the paper is on implementation aspects of the synchronization. At first, the structure of unilateral TDoA method is explained and a method for synchronization is proposed. In the next step, typical implementation challenges of synchronization such as rounding effect, outliers, filtering, packet loss detection and thermal dependencies are discussed and for each problem a solution is provided. A set of experiments have been performed on different clock sources to study their effect on the accuracy of synchronization. The results of the experiments confirm that the voltage and temperature compensated oscillator has the highest accuracy, lowest frequency jitter but longest steady state time among the other variants applied. At the end, the performance of the synchronization solution in real experiments are demonstrated. The results prove that the proposed methods can successfully reduce the negative effects of clock inaccuracies. According to the results, localization accuracy of 3cm in average is achieved when a proper oscillator is used.

Effective and efficient cooling is essential for the operation of fuel cells as well as batteries. Fuel cells and batteries must be operated in a narrow temperature range for optimal operation and minimal degradation. To promote the development of thermal management systems, this paper presents a mathematical model of the thermal system of a hybrid fuel cell electric vehicle. The model consists of all major heat sources and heat sinks including the drivetrain components, pumps, valves, fluid mixers, heat exchangers and the passenger compartment. The resulting model consists of 27 states while respecting all ten actuators.

This paper focuses on the implementation of a Kalman filter for a sensor fusion task and the testing and validation of the implementation by using a test platform. Implementation device for the sensors and the fusion algorithm is the mini-robot platform Zorro that is equipped with multiple sensors. In order to internally develop a consistent model of the robot’s world sensor data has to be fused. The fused data is used to control the behavior of the robot that should be able to act autonomously. To test the sensor fusion and the resulting behavior a Teleworkbench test system has been developed that supports video recording and analysis of the robot’s behavior complemented by wireless transmission of robot’s internal sensor and state data. Both, the video data and the sensor data are matched and displayed at operator’s computer of the Teleworkbench system for detailed analysis.

In this paper we describe a small, low cost autonomous mobile vehicle for learning and research in autonomous vision guided navigation. The vehicle consists of a mobile base that holds a smartphone. The smartphone provides the camera and the computing engine while the mobile base provides the actuators and additional sensors for autonomous interaction with the environment. The smartphone sends actuation commands to the mobile base and receives sensor data from the base over a USB connection. By taking full advantage of the high performance to cost ratio of mass consumer product technology the autonomous mobile robot built in this way lowers the entry barrier for hands-on learning of autonomous vision guided navigation. We illustrate with an example how the robot together with the Android software development environment provides a learning tool for building the complex software needed for today's and tomorrows highly automated machines.

Effective and efficient cooling is essential for the operation of fuel cells as well as batteries. Fuel cells and batteries must be operated in a narrow temperature range for optimal operation and minimal degradation. This paper presents a thermal management method for a fuel cell hybrid vehicle with a metal hydride storage while accounting for all major heat sources and heat sinks as well as the passenger compartment. The thermal management system consists of a superior state machine, which activates underlying controllers for keeping the battery, fuel cell and interior at the desired temperatures and protecting the motor and the power electronics. The system proves successful. The battery was held at 26°C and the fuel cell at the desired temperature of 55°C. Synergies such as using the waste heat for heating the passenger compartment and using the chiller for cooling battery and vehicle interior have been successfully implemented.

Ultra-wideband systems (UWB) are a common technology which is used for indoor localization applications due to its accuracy. However there are some challenges to implement these systems in indoor area with obstacles and barriers. One of the challenges is non-line of sight conditions (NLOS) between two communicating nodes which results in large localization error. Many techniques have been proposed in the literature for NLOS detection and mitigation but most of them are deployed in time of arrival (ToA) cases. This paper extends the NLOS detection and mitigation techniques to time difference of arrival cases (TDoA). The proposed method is a parametric technique based on the probability distribution function (PDF) of features collected from received signal characteristics. An innovative method is used to apply the parametric detection method on TDoA cases both in detection and mitigation stages. The mitigation technique is based on a modified and biased extended Kalman filter (BEKF) which is used in TDoA localization method. The results prove that this technique successfully detects the NLOS cases, mitigates the error up to a factor of 10 and leads to stability of the EKF method.

This paper addresses the pair selection problem of the unilateral time difference of arrival (TDoA) localization method. Two common concepts of pair selection are star form which uses a unique reference node for pairing all nodes and the chain form which links each node to its next available node. The problem of the star form is the possibility of occurring non-line of sight (NLOS) conditions between some anchors. The chain form has an issue with increasing variance of the noise as the number of anchors increases. A new hybrid form is proposed which avoids NLOS conditions and at the same time pertains the amount of noise at its minimum possible. Practical results confirm the superior performance of the proposed approach.

Global warming, the decline of oil resources as well as the introduction of emission regulations have led to a research focus in new drive technologies. Within this group of alternative drive technologies, fuel cell hybrid electric vehicles (FHEV) are considered to be especially effective. Nevertheless, in order to achieve an efficient operation, an energy management system (EMS) is required. Since system efficiency as well as the operation characteristics is determined by the chosen EMS scheme, current research focuses on new EMS approaches. This paper reviews and evaluate three widely accepted state of the art EMS schemes: classical proportional-integral control, state machine approach and online optimization based equivalent consumption minimization strategy (ECMS). The evaluation is done based on an a physical model of a FHEV. Since, use cases of the vehicle also have significant influence, real word driving was used to generate test cases. Thus, a method to cluster recorded driving data with regards to drive scenarios is proposed. Finally, the extracted reference cycles in combination with the physical model form a virtual test bench, used to evaluate the three EMS approaches under test.

Global warming, the decline of oil resources, as well as the introduction of emission regulations, has led to a research focus in new drive technologies. Whereas hybrid electric vehicles (HEV) can only be considered as a transition technology, electric vehicles (EV) and fuel cell vehicles (FCV) are zero emission technologies; HEV are only considered a bridge technology. FCV enable longer ranges and faster refills at the cost of a moderate increase in fuel cost. Challenges for an overall acceptance of fuel cell vehicles are the degradation mechanisms of the fuel cell that lead to a very limited lifetime. Current research focuses on energy management strategies to reduce the overall energy demand by predicting the vehicles coarse power consumption on the overall trip. This prediction is used to optimize the control of the drivetrain components. This paper reviews energy management strategies and points out the lack of research in the prediction of short-term speed changes. The knowledge of short-term speed changes can drastically reduce degradation mechanisms by avoiding power fluctuations in the fuel cell as well as in the battery. The proposed hierarchical model predictive control strategy is able to incorporate the knowledge of long-term energy consumption to minimize the energy demand as well as the short-term speed predictions to avoid degradation mechanisms. The suggested system will lead to longer lasting vehicles and to a better acceptance of fuel cell vehicles. To incorporate a drive data pool, this paper describes the development of onboard micro trips by evaluating the driving information and splitting them into sub-trips.

In this paper, we advance the thesis that educational robots can make a much larger contribution in the classroom than has hitherto been the case. However, to realise this potential, it is necessary to supply teachers with detailed guides for classroom robotic student activities for achieving specific curricular learning objectives. As illustration, we describe in detail three activities that use the capabilities of the inexpensive smartphone-based mobile robot Zorro developed by us.