Radio Frequency Energy Harvesting and Management for Wireless Sensor Networks

Radio Frequency (RF) Energy Harvesting holds a promising future for generating a small amount of electrical power to drive partial circuits in wirelessly communicating electronics devices. Reducing power consumption has become a major challenge in wireless sensor networks. As a vital factor affecting system cost and lifetime, energy consumption in wireless sensor networks is an emerging and active research area. This chapter presents a practical approach for RF Energy harvesting and management of the harvested and available energy for wireless sensor networks using the Improved Energy Efficient Ant Based Routing Algorithm (IEEABR) as our proposed algorithm. The chapter looks at measurement of the RF power density, calculation of the received power, storage of the harvested power, and management of the power in wireless sensor networks. The routing uses IEEABR technique for energy management. Practical and real-time implementations of the RF Energy using Powercast harvesters and simulations using the energy model of our Libelium Waspmote to verify the approach were performed. The chapter concludes with performance analysis of the harvested energy, comparison of IEEABR and other traditional energy management techniques, while also looking at open research areas of energy harvesting and management for wireless sensor networks.Comment: 40 pages, 9 figures, 5 tables, Book chapte

Model Checking for Energy Efficient Scheduling in Wireless Sensor Networks

Networking and power management of wireless energy - conscious sensor networks is an important area of current research. We investigate a network of MicaZ sensor motes using the ZigBee protocol for communication, and provide a model using Timed Safety Automata. Our analysis focuses on estimating energy consumption by model checking in different scenarios using the Uppaal tool. Special interest is devoted to the energy use in marginal situations that rarely occur and consequently might not be seen doing simulation

MMEDD: Multithreading Model for an Efficient Data Delivery in wireless sensor networks

Nowadays, the use of Wireless Sensor Networks (WSNs) is increasingly growing as they allow a large number of applications. In a large scale sensor network, communication among sensors is achieved by using a multihop communication. However, since the sensor is limited by its resources, sensors' operating systems are developed in order to optimize the management of these resources, especially the power consumption. Therefore, the hybrid operating system Contiki uses a low consumption layer called Rime which allows sensors to perform multihop sending with a low energy cost. This is favored by the implementation of lightweight processes called protothreads. These processes have a good efficiency/consumption ratio for monolithic tasks, but the management of several tasks remains a problem. In order to enable multitasking, Contiki provides to users a preemptive multithreading module that allows the management of multiple threads. However, it usually causes greater energy wastage. To improve multithreading in sensor networks, a Multithreading Model for an Efficient Data Delivery (MMEDD) using protothreads is proposed in this paper. Intensive experiments have been conducted on COOJA simulator that is integrated in Contiki. The results show that  MMEDD provides better ratio message reception rate/energy consumption than other architectures

Unified Power Management in Wireless Sensor Networks, Doctoral Dissertation, August 2006

Radio power management is of paramount concern in wireless sensor networks (WSNs) that must achieve long lifetimes on scarce amount of energy. Previous work has treated communication and sensing separately, which is insufficient for a common class of sensor networks that must satisfy both sensing and communication requirements. Furthermore, previous approaches focused on reducing energy consumption in individual radio states resulting in suboptimal solutions. Finally, existing power management protocols often assume simplistic models that cannot accurately reflect the sensing and communication properties of real-world WSNs. We develop a unified power management approach to address these issues. We first analyze the relationship between sensing and communication performance of WSNs. We show that sensing coverage often leads to good network connectivity and geographic routing performance, which provides insights into unified power management under both sensing and communication performance requirements. We then develop a novel approach called Minimum Power Configuration that ingegrates the power consumption in different radio states into a unified optimization framework. Finally, we develop two power management protocols that account for realistic communication and sensing properties of WSNs. Configurable Topology Control can configure a network topology to achieve desired path quality in presence of asymmetric and lossy links. Co-Grid is a coverage maintenance protocol that adopts a probabilistic sensing model. Co-Grid can satisfy desirable sensing QoS requirements (i.e., detection probability and false alarm rate) based on a distributed data fusion model

Energy efficiency in wireless sensor networks

University of Technology Sydney. Faculty of Engineering and Information Technology.Wireless sensor networks (WSNs), as distributed networks of sensors with the ability to sense, process and communicate, have been increasingly used in various fields including engineering, health and environment, to intelligently monitor remote locations at low cost. Sensors (a.k.a. nodes) in such networks are responsible for four major tasks: data aggregation, sending and receiving data, and in-network data processing. This implies that they must effectively utilise their resources, including memory usage, CPU power and, more importantly, energy, to increase their lifetime and productivity. Besides harvesting energy, increasing the lifetime of sensors in the network by decreasing their energy consumption has become one of the main challenges of using WSNs in practical applications. In response to this challenge, over the last few years there have been increasing efforts to minimise energy consumption via new algorithms and techniques in different layers of the WSN, including the hardware layer (i.e., sensing, processing, transmission), network layer (i.e., protocols, routing) and application layer; most of these efforts have focused on specific and separate components of energy dissipation in WSNs. Due to the high integration of these components within a WSN, and therefore their interplay, each component cannot be treated independently without regard for other components; in another words, optimising the energy consumption of one component, e.g. MAC protocols, may increase the energy requirements of other components, such as routing. Therefore, minimising energy in one component may not guarantee optimisation of the overall energy usage of the network. Unlike most of the current research that focuses on a single aspect of WSNs, we present an Energy Driven Architecture (EDA) as a new architecture for minimising the total energy consumption of WSNs. The architecture identifies generic and essential energy-consuming constituents of the network. EDA as a constituent-based architecture is used to deploy WSNs according to energy dissipation through their constituents. This view of overall energy consumption in WSNs can be applied to optimising and balancing energy consumption and increasing the network lifetime. Based on the proposed architecture, we introduce a single overall model and propose a feasible formulation to express the overall energy consumption of a generic wireless sensor network application in terms of its energy constituents. The formulation offers a concrete expression for evaluating the performance of a wireless sensor network application, optimising its constituent’s operations, and designing more energy-efficient applications. The ultimate aim is to produce an energy map architecture of a generic WSN application that comprises essential and definable energy constituents and the relationships between these constituents so that one can explore strategies for minimising the overall energy consumption of the application. Our architecture focuses on energy constituents rather than network layers or physical components. Importantly, it allows the identification and mapping of energy-consuming entities in a WSN application to energy constituents of the architecture. Furthermore, we perform a comprehensive study of all possible tasks of a sensor in its embedded network and propose an energy management model. We categorise these tasks into five energy consuming constituents. The sensor's energy consumption (EC) is modelled based on its energy consuming constituents and their input parameters and tasks. The sensor's EC can thus be reduced by managing and executing efficiently the tasks of its constituents. The proposed approach can be effective for power management, and it also can be used to guide the design of energy efficient wireless sensor networks through network parameterisation and optimisation. Later, parameters affecting energy consumption in WSNs are extracted. The dependency between these parameters and the average energy consumption of a specific application is then investigated. A few statistical tools are applied for parameter reduction, then random forest regression is employed to model energy consumption per delivered packet with and without parameter reduction to determine the reduction in accuracy due to reduction. Finally, an energy-efficient dynamic topology management algorithm is proposed based on the EDA model and the prevalent parameters. The performance of the new topology management algorithm, which employs Dijkstra to find energy-efficient lowest cost paths among nodes, is compared to similar topology management algorithms. Extensive simulation tests on randomly simulated WSNs show the potential of the proposed topology management algorithm for identifying the lowest cost paths. The challenges of future research are revealed and their importance is explained

Knowledge Discovery in the SCADA Databases Used for the Municipal Power Supply System

This scientific paper delves into the problems related to the develop-ment of intellectual data analysis system that could support decision making to manage municipal power supply services. The management problems of mu-nicipal power supply system have been specified taking into consideration modern tendencies shown by new technologies that allow for an increase in the energy efficiency. The analysis findings of the system problems related to the integrated computer-aided control of the power supply for the city have been given. The consideration was given to the hierarchy-level management decom-position model. The objective task targeted at an increase in the energy effi-ciency to minimize expenditures and energy losses during the generation and transportation of energy carriers to the Consumer, the optimization of power consumption at the prescribed level of the reliability of pipelines and networks and the satisfaction of Consumers has been defined. To optimize the support of the decision making a new approach to the monitoring of engineering systems and technological processes related to the energy consumption and transporta-tion using the technologies of geospatial analysis and Knowledge Discovery in databases (KDD) has been proposed. The data acquisition for analytical prob-lems is realized in the wireless heterogeneous medium, which includes soft-touch VPN segments of ZigBee technology realizing the 6LoWPAN standard over the IEEE 802.15.4 standard and also the segments of the networks of cellu-lar communications. JBoss Application Server is used as a server-based plat-form for the operation of the tools used for the retrieval of data collected from sensor nodes, PLC and energy consumption record devices. The KDD tools are developed using Java Enterprise Edition platform and Spring and ORM Hiber-nate technologies

Efficiency of integration between sensor networks and clouds

Numerous wireless sensor networks (WSN) applications include monitoring and controlling various conditions in the environment, industry, healthcare, medicine, military affairs, agriculture, etc. The life of sensor nodes largely depends on the power supply type, communication ability, energy storage capacity and energy management mechanisms. The collection and transmission of sensor data streams from sensor nodes lead to the depletion of their energy. At the same time, the storage and processing of this data require significant hardware resources. Integration between clouds and sensor networks is an ideal solution to the limited computing power of sensor networks, data storage and processing. One of the main challenges facing systems engineers is to choose the appropriate protocol for integrating sensor data into the cloud structure, taking into account specific system requirements. This paper presents an experimental study on the effectiveness of integration between sensor networks and the cloud, implemented through three protocols HTTP, MQTT and MQTT-SN. A model for studying the integration of sensor network - Cloud with the communication models for integration - request-response and publish- subscribe, implemented with HTTP, MQTT and MQTT-SN. The influence of the number of transmitted data packets from physical sensors to the cloud on the transmitted data delay to the cloud, the CPU and memory load was studied. After evaluating the results of sensor network and cloud integration experiments, the MQTT protocol is the most efficient in terms of data rate and power consumption

AI-Based Wireless Sensor IoT Networks for Energy-Efficient Consumer Electronics Using Stochastic Optimization

Wireless Sensor Networks (WSNs) integration with the Internet of Things (IoT) expands its potential by providing ideal communication and data sharing across devices, allowing more considerable monitoring and management in Consumer Electronics (CE). WSNs have an essential limitation in terms of energy resources since sensor nodes frequently run on limited power from batteries. This limitation necessitates the consideration of energy-efficient techniques to extend the network’s lifetime. In this article, an integrated approach has been presented to improve the energy efficiency of Wireless Sensor IoT Networks (WSINs) by leveraging modern machine learning algorithms with stochastic optimization. Recursive Feature Elimination (RFE) is utilized for the feature selection thus optimizing the input features for various machine learning models. These models are rigorously evaluated for their aptness to predict and mitigate energy consumption concerns inside WSINs. Subsequently, the stochastic optimization technique utilizes the uniform and normal distributions to model energy consumption situations. The results show that RFE-driven feature selection has significant effects on model performance and that Random Forest is effective at reaching higher accuracy. This research provides valuable perspectives for the design and implementation of WSINs in CE, supporting sustainable smart devices, by addressing energy consumption concerns using an optimized approach

Visoko-pouzdan prenos podataka kod bežičnih senzorskih mreža sa malom potrošnjom energije primenom 2D-SEC-DED tehnike kodiranja

This dissertation deals with the challenges of energy efficiency in systems with limited resources of homogeneous and heterogeneous wireless sensory networks for data collection applications in real environmentals. This research covers several fields from physical layer optimization up to network layer solutions. The problem which has to be solved is viewed from three different perspectives: the energy profile of the nodes with a special emphasis on the activity of the sensing block, the network protocol with a special focus on finding an adequate coding technique that need to reduce or eliminate the request for retransmission and evaluating the range of transmission for the proposed encoding technique. If energy efficiency in wireless sensor networks is formulated as a load balancing problem then the power management unit can significantly contribute to reduction in power consumption. Power management is implemented by switching on/off individual subblocks of the sensor node independently of the hardware platform. By reducing energy consumption both an extension of the lifetime of the sensor node and sensor network, is achieved. The obtained energy profiles reveal significant differences in energy consumption of wireless sensor nodes depending in terms of external sensors number, resolution of the analog-to-digital converter, network traffic dynamics, topology of the network, applied coding techniques, operating modes and activities during the lifetime of the sensor node and other factors. In this sense, the application of combination of power aware techniques, such as the duty-cycling at system-level, and power gating at the level of sensor elements, i.e. sensors, is proposed. An evaluation of the approach shows that energy consumption reduction three orders of magnitude on average can be achieved, when these two techniques are incorporated into the sensor node. On the other hand, in the wireless sensor networks, the choice of coding scheme, i.e. channel coding depends on the application and characteristics-, model-, type-errors appearing in the wireless channel. For example, one encoding technique is preferred for use when burst errors patterns are dominant, while another coding technique is more acceptable in situations where noise causes random errors that are either single or double in nature. Bearing this in mind, along with the analysis of channel characteristics, in this dissertation, we propose a new massage coding technique by which on extend traditional protocols with aim to improve energy efficiency, while maintaining high reliability in data transmission and low latency of message transfer. Namely, channel evaluation in wireless sensor networks used in industry shows that most of the errors are of single or double nature, and burst type errors are present, but rarely. In this context, in this dissertation, an effective technique for correcting errors at a destination (FEC) based on Hamming's coding scheme of relatively low complexity, called Two Dimensional-Single Error Correction- Double Error Detection (2D-SEC-DED) was developed. The proposed encoding technique is intendet to minimize packet retransmissions, thus saving energy. Evaluation of the proposed encoding scheme shows that the code is able to correct all single errors and 99.99% of double/multiple errors. The analysis was carried out through the implementation, in MATLAB, of two versions of Rendezvous Protocol for Long Life (RPLL), called Modified RPLL (M-RPLL) and Ordinary RPLL (O-RPLL), respectively. The energy gain achieved in this way is used to improve the performance of wireless transmission, such as increasing of the transmission range. As illustration, for indoor environment characterized by the path loss exponent 4 at the target BER of 5 10 4 , the proposed encoding scheme is able to improve the transmission distance by about 18 m , or the received signal strength (RSSI) by about 8.5 dBm compared to wireless sensor networks with encoding schemes without possibility to correct errors