Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks

The paper introduces a method which improves localization accuracy of the signal strength fingerprinting approach. According to the proposed method, entire localization area is divided into regions by clustering the fingerprint database. For each region a prototype of the received signal strength is determined and a dedicated artificial neural network (ANN) is trained by using only those fingerprints that belong to this region (cluster). Final estimation of the location is obtained by fusion of the coordinates delivered by selected ANNs. Sensor nodes have to store only the signal strength prototypes and synaptic weights of the ANNs in order to estimate their locations. This approach significantly reduces the amount of memory required to store a received signal strength map. Various ANN topologies were considered in this study. Improvement of the localization accuracy as well as speed-up of learning process was achieved by employing fully connected neural networks. The proposed method was verified and compared against state-of-the-art localization approaches in realworld indoor environment by using both stationary andmobile sensor nodes

An event-aware cluster-head rotation algorithm for extending lifetime of wireless sensor Network with smart nodes

Smart sensor nodes can process data collected from sensors, make decisions, and recognize relevant events based on the sensed information before sharing it with other nodes. In wireless sensor networks, the smart sensor nodes are usually grouped in clusters for effective cooperation. One sensor node in each cluster must act as a cluster head. The cluster head depletes its energy resources faster than the other nodes. Thus, the cluster-head role must be periodically reassigned (rotated) to different sensor nodes to achieve a long lifetime of wireless sensor network. This paper introduces a method for extending the lifetime of the wireless sensor networks with smart nodes. The proposed method combines a new algorithm for rotating the cluster-head role among sensor nodes with suppression of unnecessary data transmissions. It enables effective control of the cluster-head rotation based on expected energy consumption of sensor nodes. The energy consumption is estimated using a lightweight model, which takes into account transmission probabilities. This method was implemented in a prototype of wireless sensor network. During experimental evaluation of the new method, detailed measurements of lifetime and energy consumption were conducted for a real wireless sensor network. Results of these realistic experiments have revealed that the lifetime of the sensor network is extended when using the proposed method in comparison with state-of-the-art cluster-head rotation algorithms

Self-Organizing Mobility Control in Wireless Sensor and Actor Networks Based on Virtual Electrostatic Interactions

This paper introduces a new mobility control method for surveillance applications of wireless sensor and actor networks. The proposed method is based on virtual electrostatic forces which act on actors to coordinate their movements. The definition of virtual forces is inspired by Coulomb’s law from physics. Each actor calculates the virtual forces independently based on known locations of its neighbours and predetermined borders of the monitored area. The virtual forces generate movements of actors. This approach enables effective deployment of actors at the initial stage as well as adaptation of actors’ placement to variable conditions during execution of the surveillance task without the need of any central controller. Effectiveness of the introduced method was experimentally evaluated in a simulation environment. The experimental results demonstrate that the proposed method enables more effective organization of the actors’ mobility than state-of-the-art approaches

Introductory Chapter: Data Acquisition

New biomedical technologies can support faster development of disease treatments, prevention, and diagnostic procedures. They are expected to make significant contributions to the quality of life, improve patient healthcare, and reduce the related costs. Advancement of data acquisition techniques is a key prerequisite for the development in biomedical engineering. Recent advances in data acquisition systems, sensor design, and sensor networks allow collection of large volumes of detailed biomedical data. For instance, body area networks with wireless sensors can be used to non-invasively and continuously monitor several physiological parameters and recognize human activities [1]. Other examples are visual sensor networks for supervision of patients during rehabilitation and Internet of Things (IoT) systems with medical devices connected to the internet that can collect valuable data, enable detailed analysis of symptoms and facilitate remote healthcare(...)

Various aspects of vehicles image data-streams reduction for road traffic sufficient description

The on-line image processing was implemented for video-camera usage for traffic control. Due to reduce the immense data sets dimension various speculations of data sampling methods were introduced. At the beginning the needed sampling ratio has been found then simple but effective image processing algorithms have to be chosen, finally the hardware solutions for parallel processing are discussed. The PLA computing engine was involved for coping with this task; for fulfilling the assumed characteristics. The developer has to consider several restrictions and preferences. None universal algorithm is available up to now. The reported works, concern vehicles stream recorders development that has to do all recording and computing procedures in strictly defined time limits

Human Activity Detection Based on the iBeacon Technology

Paper presents a new method of patient activity monitoring, by using modern ADL (Activities of Daily Living) techniques. Proposed method utilizes energy efficient Bluetooth iBeacon BLE (Bluetooth Low Energy) modules, developed by Apple. Main advantage of this technology is the ability to detect neighboring devices, which belong to the same device family. Proposed method is based on observing changes of received signal strength indicator (RSSI) in the time domain. The RSSI analysis is performed in order to asses a human activity. Such observation may be particularly useful for monitoring consciousness of elder people, where reaction time of emergency rescuers and appropriate rescue operations may save the human lives