Low-power Wireless Sensor Networks Research Articles

Enhanced compressed sensing for visual target tracking in wireless visual sensor networks

Moving object tracking in wireless sensor networks (WSNs) has been widely applied in various fields. Designing low-power WSNs for the limited resources of the sensor, such as energy limitation, energy restriction, and bandwidth constraints, is of high priority. However, most existing works focus on only single conflicting optimization criteria. An efficient compressive sensing technique based on a customized memory gradient pursuit algorithm with early termination in WSNs is presented, which strikes compelling trade-offs among energy dissipation for wireless transmission, certain types of bandwidth, and minimum storage. Then, the proposed approach adopts an unscented particle filter to predict the location of the target. The experimental results with a theoretical analysis demonstrate the substantially superior effectiveness of the proposed model and framework in regard to the energy and speed under the resource limitation of a visual sensor node.

System Control Applications of Low-Power Radio Frequency Devices

This paper conceptualizes a low-power wireless sensor network design for application employment to reduce theft of portable computer devices used in educational institutions today. The aim of this study is to design and develop a reliable and robust wireless network that can eradicate accessibility of a device’s human interface. An embedded system supplied by an energy harvesting source, installed on the portable computer device, may represent one of multiple slave nodes which request regular updates from a standalone master station. A portable computer device which is operated in an undesignated area or in a field perimeter where master to slave communication is restricted, indicating a possible theft scenario, will initiate a shutdown of its operating system and render the device unusable. Consequently, an algorithm in the device firmware may ensure the necessary steps are executed to track the device, irrespective whether the device is enabled. Design outcomes thus far indicate that a wireless network using low-power embedded hardware, is feasible for anti-theft applications. By incorporating one of the latest Bluetooth low-energy, ANT+, ZigBee or Thread wireless technologies, an anti-theft system may be implemented that has the potential to reduce major portable computer device theft in institutions of digitized learning.

Achieving Accurate and Real-Time Link Estimation for Low Power Wireless Sensor Networks

Link estimation is a fundamental component of forwarding protocols in wireless sensor networks. In low power forwarding, however, the asynchronous nature of widely adopted duty-cycled radio control brings new challenges to achieve accurate and real-time estimation. First, the repeatedly transmitted frames (called wake-up frame ) increase the complexity of accurate statistic, especially with bursty channel contention and coexistent interference. Second, frequent update of every link status will soon exhaust the limited energy supply. In this paper, we propose meter , which is a distributed wake-up frame counter. Meter takes the opportunities of link overhearing to update link status in real time. Furthermore, meter does not only depend on counting the successfully decoded wake-up frames, but also counts the corrupted ones by exploiting the feasibility of ZigBee identification based on short-term sequence of the received signal strength. We implement meter in TinyOS and further evaluate the performance through extensive experiments on indoor and outdoor test beds. The results demonstrate that meter can significantly improve the performance of the state-of-the-art link estimation scheme.

Noncoherent Short Packet Detection and Decoding for Scatter Radio Sensor Networking

Scatter radio, i.e., communication by means of reflection, has been recently proposed as a promising technology for low-power wireless sensor networks (WSNs). Specifically, this paper offers noncoherent receivers in scatter radio frequency-shift keying, for either channel-coded or uncoded scatter radio reception, in order to eliminate the need for training bits of coherent schemes (for channel estimation) at the packet preamble. Noncoherent symbol-by-symbol and sequence detectors based on hybrid composite hypothesis test (HCHT) and generalized likelihood-ratio test, for the uncoded case and noncoherent decoders based on HCHT, for small block-length channel codes, are derived. Performance comparison under Rician, Rayleigh, or no fading, taking into account fixed energy budget per packet is presented. It is shown that the performance gap between coherent and noncoherent reception depends on whether channel codes are employed, the fading conditions (e.g., Rayleigh versus Rician versus no fading), as well as the utilized coding interleaving depth; the choice of one coding scheme over the other depends on the wireless fading parameters and the design choice for extra diversity versus extra power gain. Finally, experimental outdoor results at 13-dBm transmission power corroborate the practicality of the proposed noncoherent detection and decoding techniques for scatter radio WSNs.

Optimal probabilistic encryption for distributed detection in wireless sensor networks based on immune differential evolution algorithm

The problem of binary hypothesis testing is considered in a bandwidth-constrained low-power wireless sensor network operating over insecure links. To prevent passive eavesdropping from enemy fusion center (EFC), the sensor observations are randomly flipped according to pre-deployed flipping rates before transmission. Accordingly, a constrained optimization problem is formulated to minimize the fusion error of ally fusion center (AFC) while maintain EFC’s error at high level. We demonstrated that the fusion error is a non-convex function of the flipping rates, thus an immune based differential evolution algorithm is designed to search the optimal flipping rates, such that the EFC always gets high error probability at the cost of a small degeneration of the AFC’s fusion performance. Furthermore, the optimal thresholds of the fusion rules are calculated based on the statistics of the sensor data, which further degenerates the detection performance of the EFC, since it is not aware of the statistics of the sensor observations after data flipping, resulting in its threshold does not match the observations. Simulation results demonstrated that the AFC can appropriately acquire the original nature state, while the EFC is prevent to detect the target regardless of the signal-to-noise and sensor numbers.

Traffic-aware congestion control (TACC) for wireless multimedia sensor networks

Wireless multimedia sensor networks (WMSNs) have emerged as a revolutionary technology, that has shifted the focus of low power wireless sensor networks (WSNs). These networks are aimed at gathering and delivering both scalar and multimedia data from the environment. WMSNs inherit the miniature size, low power, low processing and short range wireless communication traits from WSNs. Addition of multimedia content in WMSNs demands fulfillment of various QoS parameters like low end-to-end delay, acceptable jitter rate, low packet loss rate and higher throughput. Over the past few years, considerable research efforts have been directed towards the fulfillment of these QoS requirements; by proposing new traffic-aware medium access and routing algorithms. However, the issues of rate adaptation and congestion control are still largely unexplored. Congestion in wireless networks is a major cause of packet loss resulting in degraded network performance. In WMSNs congestion is a common occurrence, due to the communication of high data rate, bursty video traffic over lossy wireless link. Therefore, it is critical to adjust the sending rate of source nodes based on network conditions. This work investigates the issue of rate adaptation and congestion control in WMSNs. A traffic-aware congestion control protocol (TACC) is proposed that operates on end-to-end principle at the transport layer. The proposed protocol uses burst loss information to detect congestion at the destination and directs source nodes to adjust reporting rate accordingly. The proposed protocol is evaluated using simulation analysis which shows significant improvement in terms of packet losses, end-to-end delay, packet delivery ratio and received picture quality.

Long-Term Monitoring of the Sierra Nevada Snowpack Using Wireless Sensor Networks

Historically, the study of mountain hydrology and the water cycle has been largely observational, with meteorological forcing and hydrological variables extrapolated from a few infrequent manual measurements. Recent developments in the Internet of Things (IoT) technology are revolutionizing the field of mountain hydrology. Low-power wireless sensor networks can now generate denser data in real time and for a fraction of the cost of labor-intensive manual measurement campaigns. The American River Hydrological Observatory (ARHO) project has deployed 13 low-power wireless IoT networks throughout the American River basin to monitor California’s snowpack. The networks feature a total of 945 environmental sensors, each reporting a reading every 15 min. The data reported is made available to the scientific community minutes after it is generated. This article provides an in-depth technical description of the ARHO project. It details the requirements and different technical options, describes the technology deployed today, and discusses the challenges associated with large-scale environmental monitoring in extreme conditions.

Interference-Robust Transmission in Wireless Sensor Networks.

Low-power wireless sensor networks (WSNs) operating in unlicensed spectrum bands may seriously suffer from interference from other coexisting radio systems, such as IEEE 802.11 wireless local area networks. In this paper, we consider the improvement of the transmission performance of low-power WSNs by adjusting the transmission rate and the payload size in response to the change of co-channel interference. We estimate the probability of transmission failure and the data throughput and then determine the payload size to maximize the throughput performance. We investigate that the transmission time maximizing the normalized throughput is not much affected by the transmission rate, but rather by the interference condition. We adjust the transmission rate and the transmission time in response to the change of the channel and interference condition, respectively. Finally, we verify the performance of the proposed scheme by computer simulation. The simulation results show that the proposed scheme significantly improves data throughput compared with conventional schemes while preserving energy efficiency even in the presence of interference.

Safety of Underground Coal Mine Using Artificial Intelligence and Wireless Sensor Network

Mining process plays the vital role in the modern era for the development of nation as well as the world wide. By using the mining only we can now a day’s having so many essential goods what we are using everywhere[2]. If we take the case of coal, then it is the most important product through which all the byproduct we are getting like fuel, wax, plastic, peat etc. In this paper purpose was to provide an implementable design scenario for underground coal mines using Artificial intelligence technique along with wireless sensor networks (WSNs)[1]. The main reason being that given the intricacies in the physical structure of a coal mine, only low power WSN nodes can produce accurate surveillance and accident detection data[4,6]. Because of the rapid growth of accident in mines area like roof fall, explosion, etc, the development of intelligent sensors, microcontrollers, and network technology, became essential; so as to make a reliable condition for our automatic real-time monitoring of coal mine[2]. The underground system collects temperature, humidity and methane values of coal mine through these intelligent sensor nodes in the mine, and provide the necessary safety prerequisite to avoid accidents. KeywordsCoal mine, Artificial Intelligence, Wireless Sensor Network, Embedded Board, safety.

CATS

Data sharing among multiple sampling tasks significantly reduces energy consumption and communication cost in low-power wireless sensor networks (WSNs). Conventional proposals have already scheduled the discrete point sampling tasks to decrease the amount of sampled data. However, less effort has been expended for applications that generate continuous interval sampling tasks. Moreover, most pioneering work limits its view to schedule sampling intervals of tasks on a single sensor node and neglects the process of task allocation in WSNs. Therefore, the gained efforts in prior work cannot benefit a large-scale WSN because the performance of a scheduling method is sensitive to the strategy of task allocation. Broadening the scope to an entire network, this article is the first work to maximize data sharing among continuous interval sampling tasks by jointly optimizing task allocation and scheduling of sampling intervals in WSNs. First, we formalize the joint optimization problem and prove it NP-hard. Second, we present the COMBINE operation, which is the crucial ingredient of our solution. COMBINE is a 2-factor approximate algorithm for maximizing data sharing among overlapping tasks. Furthermore, our heuristic named CATS is proposed. CATS is 2-factor approximate algorithm for jointly allocating tasks and scheduling sampling intervals so as to maximize data sharing in the entire network. Extensive empirical study is conducted on a testbed of 50 sensor nodes to evaluate the effectiveness of our methods. In addition, the scalability of our methods is verified by utilizing TOSSIM, a widely used simulation tool. The experimental results indicate that our methods successfully reduce the volume of sampled data and decrease energy consumption significantly.

Design and test of a real-time shelf out-of-stock detector system

The shelf out-of-stock (SOOS) problem is relevant for retailers and producers since the absence of products on the shelf can lead to a significant reduction of shoppers and a consequent drop on sales. For this purpose, it is necessary to study and introduce approaches able to measure and prevent the lack of products on the shelves and thereby promptly ensuring their refill. In this context, the paper describes the design of an embedded wireless sensor network able to detect in real time a shelf out-of-stock event. In particular, we present a shelf detector sensor based on a novel, low cost and low power wireless sensor network design that can automatically discover out-of-stock in a shelf for all the stores of a retail chain. The use of an automatic method for detecting products not available on the shelf is the first being installed for a long time in a high number of stores. This paper aims to present the hardware infrastructure of the embedded sensor network devoted to real time shelf out-of-stock management and to demonstrate the feasibility and the scalability of the system discussing interesting results about scalability, long life battery based installations and data analysis.

A power efficient cluster-based routing algorithm for wireless sensor networks: Honeybees swarm intelligence based approach

The design of low-power scalable wireless sensor networks remains a key challenge of the research. Clustering and routing have been widely studied for extending the lifetime of a network, which is a critical issue in sensor networks. Routing involves non-negligible operations, which considerably affect the network lifetime and the throughput. The clustering technique with data aggregation on cluster heads has an influence on the overall performance of the network since it is favoring a maximum network lifetime. This paper presents a novel cluster-based routing protocol called ABC-SD. The proposed protocol exploits the biologically inspired fast and efficient searching features of the Artificial Bee Colony metaheuristic to build low-power clusters. For the choice of cluster heads, a multi-objective fitness function is designed by using a Linear Programming formulation. The routing problem is addressed by a cost-based function that makes a trade-off between the energy efficiency and the number of hops of the path. The clustering process is achieved at the Base Station with a centralized control algorithm, which exploits energy levels and the neighborhood information of location-unaware sensors. As for the routing of gathered data, it is realized in a distributed manner. Furthermore, unlike the existing protocols in the literature, a realistic energy model is adopted in the considered network model. The proposed protocol is intensively experimented with a number of topologies in various network scenarios and the results are compared with the well-known cluster-based routing protocols that include the swarm intelligence based protocols. The obtained results demonstrate the effectiveness of the proposed protocol in terms of network lifetime, network coverage and the amount of packets delivered to the Base Station.

A Mobility Frame for 6LoWPAN WSN

In the mobility support standards, when a node moves between IP domains, it must be configured with a care-of address (CoA) and registers the new CoA with its home agent. As a result, before a target node is accessed its home agent must be first visited to acquire the information on the foreign network where the target node is located. Therefore, the mobility handover latency is prolonged due to the CoA configuration, and the communication latency is increased due to visiting a target’s home agent. This paper proposes a mobility frame for IPv6 over low-power wireless personal area network wireless sensor network and aims to reduce the handover latency and the communication latency. In this frame, a device managing sensor nodes is identified by a network prefix and can provide the geographical information on these sensor nodes, so the sensor nodes do not need to be configured with a CoA and can directly be accessed without visiting their home agent. In this way, the handover latency and the communication latency are reduced.

Energy-efficient and low-delay scheduling scheme for low power wireless sensor network with real-time data flows

In this paper, we propose an energy-efficient and low-delay scheduling EELDS scheme for interruptible preamble sampling-based medium access control MAC protocols e.g., BoX-MAC-2 and X-MAC in IEEE 802.15.4 wireless sensor network WSN, which considers energy efficiency while reducing delay and contention in the context of real-time data gathering and remote monitoring applications. It is built on MAC layer and exploits local information to coordinate the MAC schedules of nodes in a slowly-changing topology, just with light overhead. The scheme also considers the adaptation to data aggregation with real-time data flows. Therefore, our scheme allows for much longer network lifetime while satisfying the delay constraints. The implementation and simulation results show the performance of the proposed scheme.

Energy-efficient and low-delay scheduling scheme for low power wireless sensor network with real-time data flows

In this paper, we propose an energy-efficient and low-delay scheduling (EELDS) scheme for interruptible preamble sampling-based medium access control (MAC) protocols (e.g., BoX-MAC-2 and X-MAC) in IEEE 802.15.4 wireless sensor network (WSN), which considers energy efficiency while reducing delay and contention in the context of real-time data gathering and remote monitoring applications. It is built on MAC layer and exploits local information to coordinate the MAC schedules of nodes in a slowly-changing topology, just with light overhead. The scheme also considers the adaptation to data aggregation with real-time data flows. Therefore, our scheme allows for much longer network lifetime while satisfying the delay constraints. The implementation and simulation results show the performance of the proposed scheme.

Tracking Elite Swimmers in Real Time with Wearable Low-power Wireless Sensor Networks

Monitoring elite athletes in real-time remains challenging. A lot of research has been conducted towards the application of inertial measurement systems for the quantification of performance in sports applications. The challenge however, remains in creating systems that meet high comfort standards in order to be able to apply them on a daily basis. The problem is mainly related to the requested volume of existing systems, which can be linked to the high power requirements and hence large batteries. This paper presents research towards the performance assessment of elite swimmers using wearable low-power sensor networks. This is the first work to focus on the reduction of power consumption by optimizing the power cost of signal processing and sensor use on a small and comfortable system. A built-in algorithm exploits the sensor data in order to predict which information is valuable to assess the motion of the athlete in real-time. Moreover, the algorithm is designed to run on a low-power microcontroller unit and uses lightweight computational techniques that further reduce the power requirements. By intelligently putting the sensor system into a low-power mode whenever possible, a reduction in sensor power consumption of 96% is estimated over minimally 80% of the swimming time, depending on the swimmer's lap times. Stroke types and turns were detected with high accuracy for each tested swimmer, despite different stroke techniques.

Self-Correcting Time Synchronization Support for Wireless Sensor Networks Targeting Applications on Internet of Things

Abstract: The Internet of Things is a collection of devices that communicate by exchanging a variety of data among them, in which time synchronization is needed for meaningful information creation and transmission. The robustness of the data transmissions becomes an issue, since most of these devices use wireless communication. This paper focuses in proposing and implementing a time synchronization service for low-power wireless sensor networks using low frequency real-time clocks in each node. This work presents the design, implementation and test of an adaptive algorithm, making the timing of the clocks converge as quickly as possible and after this convergence, keeping them most similar as possible. The goal is to achieve the best method that ensures right timing and still having low energy consumption. Experimental results provide evidence of the success in meeting this goal.

Improving the limits of detection in potentiometric sensors

Potentiometric sensors will generally suffer from unwanted responses as a result to changing temperatures by generating an electromotive force. Typically, this voltage drift has a non-linear character and therefore it is difficult to compensate using linear algorithms implemented in the analogue domain. A solution is proposed to improve the sensor characteristics by combining the digitized output of two CO2 rubidium silver iodide sensors with a specially designed digital algorithm to improve the limits of detection (LOD). Experiments show that this method has the capability to improve the LOD of the sensor with a factor 4.5x during temperature variations of 22 °C over a measurement period of 22 h. It enables potentiometric sensors to be used in low power wireless sensor networks for long term air quality control. Furthermore, the influence of depletion of the rubidium silver iodide electrolyte layer can be effectively compensated by determining the decay of the active layer according to the Nernst equation. Knowing the function of depletion over time helps to correct the sensor output and thereby improves the accuracy of the sensor.

Hardware Design of Low Energy Consumption Wireless Senor Network Node for Monitoring Mechanical Vibration Signals

Aiming at the increasing demand of the monitoring of mechanical vibration signals, by improving the existing Sensor Monitoring Network, a low-power wireless sensor network is designed. The data transmission efficiency is increased by using the compressed sensing (CS) technology to compress the mechanical vibration signals of the devices monitored by the network. To realize real-time processing of monitoring signals, the FPGA+DSP architecture is treated as the core of the equipment processing the mechanical vibration signals of wireless sensor network. The modified designs of the hardware circuit and logic circuit with regard to the data collection, sampling, transmission, processing and the store section are introduced initially. Further, the effects of the improved Sensor Monitoring Network are tested by the experimental study on the rotating equipment. The results show that the real-time processing can be well implemented and the power can be cut down to a certain degree.

Review and Comparison of Spatial Localization Methods for Low-Power Wireless Sensor Networks

Spatial localization (colocation) of nodes in wireless sensor networks (WSNs) is an active area of research, with many applications in sensing from distributed systems, such as microaerial vehicles, smart dust sensors, and mobile robotics. This paper provides a comprehensive review and comparison of recent implementations (commercial and academic) of physical measurement techniques used in sensor localization, and of the localization algorithms that use these measurement techniques. Physical methods for measuring distances and angles between WSN nodes are reviewed, followed by a comprehensive comparison of localization accuracy, applicable ranges, node dimensions, and power consumption of the different implementations. A summary of advantages and disadvantages of each measurement technique is provided, along with a comparison of colocalization methods in WSNs across multiple algorithms and distance ranges. A discussion of possible improvements to accuracy, range, and power consumption of selected self-localization methods is included in the concluding discussion. Although the preferred implementation depends on the application, required accuracy, and range, passive optical triangulation is reported as the most energy efficient localization method for low-cost/low-power miniature sensor nodes. It is capable of providing micrometer-level resolution, however, the applicable range (internode distance) is limited to single centimeters.