Energy Efficient Routing Algorithms for Wireless Sensor Networks and Performance Evaluation of Quality of Service for IEEE 802.15.4 Networks

The popularity of Wireless Sensor Networks (WSN) have increased tremendously in recent time due to growth in Micro-Electro-Mechanical Systems (MEMS) technology. WSN has the potentiality to connect the physical world with the virtual world by forming a network of sensor nodes. Here, sensor nodes are usually battery-operated devices, and hence energy saving of sensor nodes is a major design issue. To prolong the network‘s lifetime, minimization of energy consumption should be implemented at all layers of the network protocol stack starting from the physical to the application layer including cross-layer optimization. In this thesis, clustering based routing protocols for WSNs have been discussed. In cluster-based routing, special nodes called cluster heads form a wireless backbone to the sink. Each cluster heads collects data from the sensors belonging to its cluster and forwards it to the sink. In heterogeneous networks, cluster heads have powerful energy devices in contrast to homogeneous networks where all nodes have uniform and limited resource energy. So, it is essential to avoid quick depletion of cluster heads. Hence, the cluster head role rotates, i.e., each node works as a cluster head for a limited period of time. Energy saving in these approaches can be obtained by cluster formation, cluster-head election, data aggregation at the cluster-head nodes to reduce data redundancy and thus save energy. The first part of this thesis discusses methods for clustering to improve energy efficiency of homogeneous WSN. It also proposes Bacterial Foraging Optimization (BFO) as an algorithm for cluster head selection for WSN. The simulation results show improved performance of BFO based optimization in terms of total energy dissipation and no of alive nodes of the network system over LEACH, K-Means and direct methods. IEEE 802.15.4 is the emerging next generation standard designed for low-rate wireless personal area networks (LR-WPAN). The second part of the work reported here in provides performance evaluation of quality of service parameters for WSN based on IEEE 802.15.4 star and mesh topology. The performance studies have been evaluated for varying traffic loads using MANET routing protocol in QualNet 4.5. The data packet delivery ratio, average end-to-end delay, total energy consumption, network lifetime and percentage of time in sleep mode have been used as performance metrics. Simulation results show that DSR (Dynamic Source Routing) performs better than DYMO (Dynamic MANET On-demand) and AODV (Ad–hoc On demand Distance Vector) routing protocol for varying traffic loads rates

Analysis of the IEEE 802.15.4a ultra wideband physical layer through wireless sensor network simulations in OMNET++

Wireless Sensor Networks are the main representative of pervasive computing in large-scale physical environments. These networks consist of a large number of small, wireless devices embedded in the physical world to be used for surveillance, environmental monitoring or other data capture, processing and transfer applications. Ultra wideband has emerged as one of the newest and most promising concepts for wireless technology. Considering all its advantages it seems a likely communication technology candidate for future wireless sensor networks. This paper considers the viability of ultra wideband technology in wireless sensor networks by employing an IEEE 802.15.4a low-rate ultra wideband physical layer model in the OMNET++ simulation environment. An elaborate investigation into the inner workings of the IEEE 802.15.4a UWB physical layer is performed. Simulation experiments are used to provide a detailed analysis of the performance of the IEEE 802.15.4a UWB physical layer over several communication distances. A proposal for a cognitive, adaptive communication approach to optimize for speed and distance is also presented. AFRIKAANS : Draadlose Sensor Netwerke is die hoof verteenwoordiger vir deurdringende rekenarisering in groot skaal fisiese omgewings. Hierdie tipe netwerke bestaan uit ’n groot aantal klein, draadlose apparate wat in die fisiese wêreld ingesluit word vir die doel van bewaking, omgewings monitering en vele ander data opvang, verwerk en oordrag applikasies. Ultra wyeband het opgestaan as een van die nuutste en mees belowend konsepte vir draadlose kommunikasie tegnologie. As al die voordele van dié kommunikasie tegnologie in ag geneem word, blyk dit om ’n baie goeie kandidaat te wees vir gebruik in toekomstige draadlose sensor netwerke. Hierdie verhandeling oorweeg die vatbaarheid van die gebruik van die ultra wyeband tegnologie in draadlose sensor netwerke deur ’n IEEE 802.15.4a lae-tempo ultra wyeband fisiese laag model in die OMNET++ simulasie omgewing toe te pas. ’n Breedvoerige ondersoek word geloots om die fyn binneste werking van die IEEE 802.15.4a UWB fisiese laag te verstaan. Simulasie eksperimente word gebruik om ’n meer gedetaileerde analiese omtrent die werkverrigting van die IEEE 802.15.4a UWB fisiese laag te verkry oor verskillende kommunikasie afstande. ’n Voorstel vir ’n omgewings bewuste, aanpasbare kommunikasie tegniek word bespreek met die doel om die spoed en afstand van kommunikasie te optimiseer.Dissertation (MEng)--University of Pretoria, 2011.Electrical, Electronic and Computer Engineeringunrestricte

Real-time wireless networks for industrial control systems

The next generation of industrial systems (Industry 4.0) will dramatically transform manyproductive sectors, integrating emerging concepts such as Internet of Things, artificialintelligence, big data, cloud robotics and virtual reality, to name a few. Most of thesetechnologies heavily rely on the availability of communication networks able to offernearly–istantaneous, secure and reliable data transfer. In the industrial sector, these tasks are nowadays mainly accomplished by wired networks, that combine the speed ofoptical fiber media with collision–free switching technology. However, driven by the pervasive deployment of mobile devices for personal com-munications in the last years, more and more industrial applications require wireless connectivity, which can bring enormous advantages in terms of cost reduction and flex-ibility. Designing timely, reliable and deterministic industrial wireless networks is a complicated task, due to the nature of the wireless channel, intrinsically error–prone andshared among all the devices transmitting on the same frequency band. In this thesis, several solutions to enhance the performance of wireless networks employed in industrial control applications are proposed. The presented approaches differ in terms of achieved performance and target applications, but they are all characterized by an improvement over existing industrial wireless solutions in terms of timeliness, reliability and determinism. When possible, an experimental validation of the designed solutions is provided. The obtained results prove that significant performance improvements are already possible, often using commercially available devices and preserving compliance to existing standards. Future research efforts, combined with the availability of new chipsets and standards, could lead to a world where wireless links effectively replace most of the existing cables in industrial environments, as it is already the case in the consumer market

Real-time signal detection and classification algorithms for body-centered systems

El principal motivo por el cual los sistemas de comunicación en el entrono corporal se desean con el objetivo de poder obtener y procesar señales biométricas para monitorizar e incluso tratar una condición médica sea ésta causada por una enfermedad o el rendimiento de un atleta. Dado que la base de estos sistemas está en la sensorización y el procesado, los algoritmos de procesado de señal son una parte fundamental de los mismos. Esta tesis se centra en los algoritmos de tratamiento de señales en tiempo real que se utilizan tanto para monitorizar los parámetros como para obtener la información que resulta relevante de las señales obtenidas. En la primera parte se introduce los tipos de señales y sensores en los sistemas en el entrono corporal. A continuación se desarrollan dos aplicaciones concretas de los sistemas en el entorno corporal así como los algoritmos que en las mismas se utilizan. La primera aplicación es el control de glucosa en sangre en pacientes con diabetes. En esta parte se desarrolla un método de detección mediante clasificación de patronones de medidas erróneas obtenidas con el monitor contínuo comercial "Minimed CGMS". La segunda aplicacióin consiste en la monitorizacióni de señales neuronales. Descubrimientos recientes en este campo han demostrado enormes posibilidades terapéuticas (por ejemplo, pacientes con parálisis total que son capaces de comunicarse con el entrono gracias a la monitorizacióin e interpretación de señales provenientes de sus neuronas) y también de entretenimiento. En este trabajo, se han desarrollado algoritmos de detección, clasificación y compresión de impulsos neuronales y dichos algoritmos han sido evaluados junto con técnicas de transmisión inalámbricas que posibiliten una monitorización sin cables. Por último, se dedica un capítulo a la transmisión inalámbrica de señales en los sistemas en el entorno corporal. En esta parte se estudia las condiciones del canal que presenta el entorno corporal para la transmisión de sTraver Sebastiá, L. (2012). Real-time signal detection and classification algorithms for body-centered systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16188Palanci

Preliminary systems engineering evaluations for the National Ecological Observatory Network.

The National Ecological Observatory Network (NEON) is an ambitious National Science Foundation sponsored project intended to accumulate and disseminate ecologically informative sensor data from sites among 20 distinct biomes found within the United States and Puerto Rico over a period of at least 30 years. These data are expected to provide valuable insights into the ecological impacts of climate change, land-use change, and invasive species in these various biomes, and thereby provide a scientific foundation for the decisions of future national, regional, and local policy makers. NEON's objectives are of substantial national and international importance, yet they must be achieved with limited resources. Sandia National Laboratories was therefore contracted to examine four areas of significant systems engineering concern; specifically, alternatives to commercial electrical utility power for remote operations, approaches to data acquisition and local data handling, protocols for secure long-distance data transmission, and processes and procedures for the introduction of new instruments and continuous improvement of the sensor network. The results of these preliminary systems engineering evaluations are presented, with a series of recommendations intended to optimize the efficiency and probability of long-term success for the NEON enterprise

D13.1 Fundamental issues on energy- and bandwidth-efficient communications and networking

Deliverable D13.1 del projecte europeu NEWCOM#The report presents the current status in the research area of energy- and bandwidth-efficient communications and networking and highlights the fundamental issues still open for further investigation. Furthermore, the report presents the Joint Research Activities (JRAs) which will be performed within WP1.3. For each activity there is the description, the identification of the adherence with the identified fundamental open issues, a presentation of the initial results, and a roadmap for the planned joint research work in each topic.Preprin

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig