An energy-efficient adaptive modulation suitable for wireless sensor networks with SER and throughput constraints

We consider the problem of minimizing transmission energy in wireless sensor networks by taking into account that every sensor may require a different bit rate and reliability according to its particular application. We propose a cross-layer approach to tackle such a minimization in centralized networks for the total transmission energy consumption of the network: in the physical layer, for each sensor the sink estimates the channel gain and adaptively selects a modulation scheme; in the MAC layer, each sensor is correspondingly assigned a number of time slots. The modulation level and the number of allocated time slots for every sensor are constrained to attain their applications bit rates in a global energy-efficient manner. The signal-to-noise ratio gap approximation is used in our exposition in order to jointly handle required bit rates, transmission energies, and symbol error rates.This work has been partially funded by CRUISE NoE (IST-4-027738), MAMBO2 (CCG06-UC3M/TIC-0698) and MACAWI (TEC- 2005-07477-C02-02) projects.Publicad

Powertrace: Network-level Power Profiling for Low-power Wireless Networks

Low-power wireless networks are quickly becoming a critical part of our everyday infrastructure. Power consumption is a critical concern, but power measurement and estimation is a challenge. We present Powertrace, which to the best of our knowledge is the first system for network-level power profiling of low-power wireless systems. Powertrace uses power state tracking to estimate system power consumption and a structure called energy capsules to attribute energy consumption to activities such as packet transmissions and receptions. With Powertrace, the power consumption of a system can be broken down into individual activities which allows us to answer questions such as “How much energy is spent forwarding packets for node X?”, “How much energy is spent on control traffic and how much on critical data?”, and “How much energy does application X account for?”. Experiments show that Powertrace is accurate to 94% of the energy consumption of a device. To demonstrate the usefulness of Powertrace, we use it to experimentally analyze the power behavior of the proposed IETF standard IPv6 RPL routing protocol and a sensor network data collection protocol. Through using Powertrace, we find the highest power consumers and are able to reduce the power consumption of data collection with 24%. It is our hope that Powertrace will help the community to make empirical energy evaluation a widely used tool in the low-power wireless research community toolbox

Calibrating low-cost air quality sensors using multiple arrays of sensors

The remarkable advances in sensing and communication technologies have introduced increasingly low-cost, smart and portable sensors that can be embedded everywhere and play an important role in environmental sensing applications such as air quality monitoring. These user-friendly wireless sensor platforms enable assessment of human exposure to air pollution through observations at high spatial resolution in near-realtime, thus providing new opportunities to simultaneously enhance existing monitoring systems, as well as engage citizens in active environmental monitoring. However, data quality from such platforms is a concern since sensing hardware of such devices is generally characterized by a reduced accuracy, precision, and reliability. Achieving good data quality and maintaining error free measurements during the whole system lifetime is challenging. Over time, sensors become subject to several sources of unknown and uncontrollable faulty data which comprise the accuracy of the measurements and yield observations far from the expected values. This paper investigates calibration of low-cost air quality sensors in a real sensor network deployment. The approach leverages on the availability of sensor arrays in a wireless node to estimate parameters that minimize the calibration error using fusion of data from multiple sensors. The obtained results were encouraging and show the effectiveness of the approach compared to a single sensor calibration.Peer ReviewedPostprint (author's final draft

Optimizing Transmission and Shutdown for Energy-Efficient Packet Scheduling in Sensor Networks

Energy-efficiency is imperative to enable the deployment of sensor networks with satisfactory lifetime. Conventional power management in radio communication primarily focuses independently on the physical layer, medium access control (MAC) or routing and approaches differ depending on the levels of abstraction. At the physical layer, the fundamental trade-off that exists between transmission rate and energy is exploited. This leads to the lazy scheduling approach, which consists of transmitting with the lowest power over the longest feasible duration. At MAC level, power reduction techniques tend to keep the transmission as short as possible to maximize the radio\u27s power-off interval. Those two approaches seem conflicting and it is not clear which one is the most appropriate for a given network scenario. In this paper, we propose a transmission strategy that combines both techniques optimally. We present a cross-layer solution to determine the best transmission strategy taking into account the transceiver power consumption characteristics, the system load and the scenario constraints. Based on this approach, we derive a low complexity, on-line scheduling algorithm that can be used to optimally organize the forwarding of the sensed information from cluster heads to the data sink (uplink) in a hierarchical sensor network. Results, considering Coded Frequency Shift Keying (FSK) modulation, show that depending on the scenario, a 50% extra power reduction is achieved in a realistic uplink data gathering context, compared to the case where only transmission rate scaling or shutdown is considered

Monitoring wireless sensor network nodes with a low intrusion hybrid monitor

[EN] This work presents an active hybrid monitor with low intrusion, to be applied on sensor network nodes. Intrusion caused to the sensor node has been evaluated on three aspects: time, additional code, and power consumption.[ES] Se presenta un monitor híbrido activo de baja intrusión, aplicable a nodos de redes de sensores. La intrusión causada al nodo sensor ha sido evaluada en tres aspectos: tiempo, código adicional, y el consumo de energía.Navia Mendoza, MR. (2015). Monitoring wireless sensor network nodes with a low intrusion hybrid monitor. http://hdl.handle.net/10251/67823Archivo delegad

Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey

Growing progress in sensor technology has constantly expanded the number and range of low-cost, small, and portable sensors on the market, increasing the number and type of physical phenomena that can be measured with wirelessly connected sensors. Large-scale deployments of wireless sensor networks (WSN) involving hundreds or thousands of devices and limited budgets often constrain the choice of sensing hardware, which generally has reduced accuracy, precision, and reliability. Therefore, it is challenging to achieve good data quality and maintain error-free measurements during the whole system lifetime. Self-calibration or recalibration in ad hoc sensor networks to preserve data quality is essential, yet challenging, for several reasons, such as the existence of random noise and the absence of suitable general models. Calibration performed in the field, without accurate and controlled instrumentation, is said to be in an uncontrolled environment. This paper provides current and fundamental self-calibration approaches and models for wireless sensor networks in uncontrolled environments

Automated linear regression tools improve RSSI WSN localization in multipath indoor environment

Received signal strength indication (RSSI)-based localization is emerging in wireless sensor networks (WSNs). Localization algorithms need to include the physical and hardware limitations of RSSI measurements in order to give more accurate results in dynamic real-life indoor environments. In this study, we use the Interdisciplinary Institute for Broadband Technology real-life test bed and present an automated method to optimize and calibrate the experimental data before offering them to a positioning engine. In a preprocessing localization step, we introduce a new method to provide bounds for the range, thereby further improving the accuracy of our simple and fast 2D localization algorithm based on corrected distance circles. A maximum likelihood algorithm with a mean square error cost function has a higher position error median than our algorithm. Our experiments further show that the complete proposed algorithm eliminates outliers and avoids any manual calibration procedure

Wireless sensor networks: performance analysis in indoor scenarios

We evaluate the performance of realistic wireless sensor networks in indoor scenarios. Most of the considered networks are formed by nodes using the Zigbee communication protocol. For comparison, we also analyze networks based on the proprietary standard Z-Wave. Two main groups of network scenarios are proposed: (i) scenarios with direct transmissions between the remote nodes and the network coordinator, and (ii) scenarios with routers, which relay the packets between the remote nodes and the coordinator. The sensor networks of interest are evaluated considering different performance metrics. In particular, we show how the received signal strength indication (RSSI) behaves in the considered scenarios. Then, the network behavior is characterized in terms of end-to-end delay and throughput. In order to confirm the experiments, analytical and simulation results are also derived

Middleware Solutions for the Internet of Things: A Survey

The Internet of Things (IoT), along with its wider variants including numerous technologies, things, and people: the Internet of Everything (IoE) and the Internet of Nano Things (IoNT), are considered as part of the Internet of the future and ubiquitous computing allowing the communication among billions of smart devices and objects, and have recently drawn a very significant research attention. In these approaches, there are varieties of heterogeneous devices empowered by new capabilities and interacting with each other to achieve specific applications in different domains. A middleware layer is therefore required to abstract the physical layer details of the smart IoT devices and ease the complex and challenging task of developing multiple backend applications. In this chapter, an overview of IoT technologies, architecture, and main applications is given first and then followed by a comprehensive survey on the most recently used and proposed middleware solutions designed for IoT networks. In addition, open issues in IoT middleware design and future works in the field of middleware development are highlighted