A new wireless sensor networks localization scheme with antenna arrays

We introduce a novel sensor node management and location estimation method referred as sectoral sweeper (SS) scheme that uses an adaptive antenna array (AAA) at a central node in wireless sensor networks (WSNs). With the SS scheme, the central node can activate or deactivate the nodes in a desired region which is specified by beam direction and beam width of the transmit beam and also by minimum and maximum thresholds (R min and R max) for the received signal strength indicator (RSSI) of signals received by the nodes. In order to perform a specified task that is associated with a Task_id, two different beams are transmitted, which are task region beam and routing region beam to switch the nodes into active or routing modes. Since our scheme does not require any additional software or hardware for node management and location estimation in sensor nodes, the deficiencies of tiny sensors are effectively eliminated. The proposed scheme is shown to reduce the number of sensing nodes and the amount of data traffic in the network, thus leading to considerable savings in energy consumption and prolonged sensor lifetime.

We proposed a localization algorithm named LSARSSI for mobile node based on RSSI (received signal strength indicator) between locating sensor node with inertia module built-in and the single anchor. Instead of directly mapping RSSI values into physical distance, contrasting RSSI values received from anchor in different visited locations, LSARSSI utilizes the geometric relationship of perpendicular intersection to compute node positions. Given that the values of RSSI among two visited locations are equal, we regard that their distances to anchor node are equal. After obtaining several sets of such visited locations, the relative location of mobile node and anchor node can be calculated. Because of the limitations of LSARSSI, we put forward an improved algorithm named ILSARSSI. Our scheme uses only one location-known anchor which is useful in low density environment without using additional hardware. The simulations show that LSARSSI achieves high accuracy and ILSARSSI performs high stability and feasibility.

Accurate localization of nodes in a Wireless Sensor Network (WSN) is imperative for several important applications. The use of Global Positioning Systems (GPS) for localization is the natural approach in most domains. In WSN, however, the use of GPS is challenging because of the constrained nature of deployed nodes as well as the often inaccessible sites of WSN nodes’ deployment. Several approaches for localization without the use of GPS and harnessing the capabilities of Received Signal Strength Indicator (RSSI) exist in literature but each of these makes the simplifying assumption that all the WSN nodes are within the communication range of every other node. In this paper, we go beyond this assumption and propose a hybrid technique for node localization in large WSN deployments. The hybrid technique comprises a loose combination of a Machine Learning (ML) based approach for localization involving random forest and a multilateration approach. This hybrid approach takes advantage of the accuracy of ML localization and the iterative capabilities of multilateration. We demonstrate the efficacy of the proposed approach through experiments on a simulated set-up.KeywordsLocalizationMultilaterationRandom forestRSSIWSN

Profiling environmental parameter using a large number of spatially distributed wireless sensor network (WSN) NODEs is an extensive illustration of advanced modern technologies, but high power requirement for WSN NODEs limits the widespread deployment of these technologies. Currently, WSN NODEs are extensively powered up using batteries, but the battery has limitation of lifetime, power density, and environmental concerns. To overcome this issue, energy harvester (EH) is developed and presented in this paper. Solar-based EH has been identified as the most viable source of energy to be harvested for autonomous WSN NODEs. Besides, a novel chemical-based EH is reported as the potential secondary source for harvesting energy because of its uninterrupted availability. By integrating both solar-based EH and chemical-based EH, a hybrid energy harvester (HEH) is developed to power up WSN NODEs. Experimental results from the real-time deployment shows that, besides supporting the daily operation of WSN NODE and Router, the developed HEH is capable of producing a surplus of 971 mA·hr equivalent energy to be stored inside the storage for NODE and 528.24 mA·hr equivalent energy for Router, which is significantly enough for perpetual operation of autonomous WSN NODEs used in environmental parameter profiling.

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.

Localisation methods based on the received signal strength indicator (RSSI) are widely used in wireless sensor networks (WSNs). They offer an energy reduced and cost-effective solutions for wireless localisation. Due to radio irregularity, RSSI localisation is vulnerable to multipath, shadowing, interference and environmental effects, which reduces the accuracy of localisation results. In this paper, a new distance measurement correction algorithm is presented, which is based on feedback filter and hardware characteristic. It implements the feedback filter with consideration of historical values of RSSI. This enables to predict possible values of RSSI and thus nodes positions. Hardware characteristics, such as multiplication and summation capabilities are explored to improve the localisation accuracy. Real experiments were carried out on panStamp sensor nodes at two different temperatures. The new proposed RSSI technique shows a reduced localisation error compared to the simple RSSI method.

In mobile ad hoc networks (MANETs), when information about the location of the destination node is known, efficient communication can be achieved by using location-based routing protocols. Even in location-based routing protocols, with omnidirectional antennas there is some redundant route discovery by blind flooding of route request packets outside the area between the source and destination. These redundant route discovery propagations can be reduced by using both a scheme of restricted flooding and directional antennas. In this paper, we propose a new location-based routing protocol using both restricted flooding and directional antennas, called Adaptive Location-Aware Routing with Directional Antennas (ALAR-DA). To investigate the performance of ALAR-DA, we compare it with existing location-aware routing protocols. Our simulation results show that ALAR-DA outperforms existing protocols from the standpoints of delay and route discovery traffic.

In this paper, we experimentally investigate the combined effect of human, antenna orientation in elevation direction and the ground effect on the Received Signal Strength Indicator (RSSI) parameter in the Wireless Sensor Network (WSN). In experiment, we use MICAz motes and consider different scenarios where antenna of the transmitter node is tilted in elevation direction. The motes were placed on the ground to take into account the ground effect on the RSSI. The effect of one, two and four persons on the RSSI is recorded. For one and two persons, different walking paces e.g. slow, medium and fast pace, are analysed. However, in case of four persons, random movement is carried out between the pair of motes. The experimental results show that some antenna orientation angles have drastic effect on the RSSI, even without any human activity. The fluctuation count and range of RSSI in different scenarios with same walking pace are completely different. Therefore, an efficient human activity algorithm is need that effectively takes into count the antenna elevation and other parameters to accurately detect the human activity in the WSN deployment region.

This paper introduces a hierarchical routing protocol with proposed node locating algorithm and high security level in the case of fixed and mobile nodes. With that new routing protocol there is no need to monitor the routing process to specify the first dead node and last one. Nodes Identifications (IDs) is encrypted by two different ways with updated keys with each packet. Also there is no need to use angle determination to specify the exact node position. The exact node position is determined from Received Signal Strength Indicator (RSSI) only. A great enhancement in WSN performance is proved in this paper.

This paper is concerned with a wireless receiving link evaluation method using statistical means of received signal strength indicator (RSSI) and link quality indicator (LQI) based on the IEEE 802.15.4 protocol for wireless sensor networks. Traditional methods using single RSSI and single LQI based on the IEEE 802.11 protocol have the disadvantage of the inaccurate evaluation. In this paper, we carry out a quantitative emulation experiment via computing statistical means of RSSI and LQI based on wireless sensor networks protocol of IEEE 802.15.4. Tested numerical values are analyzed using MATLAB and SPSS by defining the wireless link evaluation sensitivity. Result curves of RSSI to packet reception rate (PRR) and LQI to PRR we finally derive are shown that statistical means of RSSI and LQI can obtain the status information of receiving links more accurately, compared with the traditional wireless link evaluation using single RSSI and single LQI.

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.

This paper studies on the localization techniques using in wireless sensor network (WSN) for an indoor environment. We compare two main categories of localization techniques: range-based and fingerprinting-based techniques on a single experimental environment. The received signal strength indicator (RSSI) received at sensor nodes are used for all localization techniques. For the range-base techniques, the location estimation approach based on the lateration estimation and the min-max approach are employed. For the fingerprinting based techniques, two pattern matching approaches are used: one is the simple nearest neighbor algorithm and another one is the k-nearest-nearest neighbor algorithm. The experiments are conducted in a meeting room where is a small number of obstacles inside to evaluate the performance of each technique. The results demonstrate that the location estimation algorithm using lateration estimation gives the best accuracy and also less computational time compared to other techniques.

Wireless Sensor Networks (WSN) are widely used in different monitoring systems. Given the distributed nature of WSN, a constantly increasing number of research studies are concentrated on some important aspects: maximizing network autonomy, node localization, and data access security. The node localization and distance estimation algorithms have, as their starting points, different information provided by the nodes. The level of signal strength is often such a starting point. A system for Received Signal Strength Indicator (RSSI) acquisition has been designed, implemented, and tested. In this paper, experiments in different operating environments have been conducted to show the variation of Received Signal Strength Indicator (RSSI) metric related to distance and geometrical orientation of the nodes and environment, both indoor and outdoor. Energy aware data transmission algorithms adjust the power consumed by the nodes according to the relative distance between the nodes. Experiments have been conducted to measure the current consumed by the node depending on the adjusted transmission power. In order to use the RSSI values as input for distance or location detection algorithms, the RSSI values can’t be used without intermediate processing steps to mitigate with the non-linearity of the measured values. The results of the measurements confirmed that the RSSI level varies with distance, geometrical orientation of the sensors, and environment characteristics.

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.

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