Outdoor Wireless Sensor Networks Research Articles

A Novel Polytope Algorithm based On Nelder-mead Method for Localization in Wireless Sensor Network

Background and Objective: Magnificent localization precision and low operating expenses are the main keys and essential issues to managing and operating outdoor wireless sensor networks. This work proposes a novel and rigorous efficiency localization algorithm utilizing a simplex optimization approach for node localization. This novel optimization method is a direct search approach, and is usually directed to solve nonlinear optimization problems that may not have wellknown derivatives, and it is called the Nelder-mead Method (NMM). Methods: It is suggested that the objective function that will be optimized using NMM is the mean squared error of the range of all neighboring anchor nodes installed in the studied WSNs. This paper emphasizes employing a ranging technique called Received Signal Strength Indicator (shortly RSSI) to calculate the length of distances among all the nodes of WSNs. Results: Simulation results perfectly showed that the suggested localization algorithm based on NMM can carry out a better performance than that of other localization algorithms utilizing other optimization approaches, including a particle swarm optimization, ant colony (ACO) and bat algorithm (BA). This obviously appeared in several metrics of performance evaluation, such as accuracy of localization, node localization rate, and implementation time. Conclusions: The proposed algorithm that utilized NMM is more functional to enhance the precision of localization because of particular characteristics that are the flexible implementation of NMM and the free cost of using the RSSI technique.

Adaptive range-based localization algorithm based on trilateration and reference node selection for outdoor wireless sensor networks

Locating the nodes of outdoor wireless sensor networks (WSNs) using (tri)lateration with a low-cost ranging technique, such as the received signal strength indicator (RSSI), often results in inaccurate location estimates. This can mostly be explained by the combined effect of distance estimate errors and localization geometry, both of which are subject to the reference nodes used. To develop techniques for reducing localization error, the distance estimate errors and localization geometry must be analyzed and taken into account. To address these challenges, this paper aims to seek ways to improve the quality of range-based trilateration localization for WSN nodes in varying outdoor conditions. Based on simulations, we analyze the effects of ranging error and localization geometry on localization error. For that purpose, we introduce a simple measure to evaluate the geometry of reference triangle (GRT). To improve localization accuracy and precision, we propose an adaptive range-based localization (ARBL) algorithm that is based on trilateration and reference node selection. In ARBL, the GRT values are calculated for each 3-combination of a preselected reference node set, based on which the combinations are selected. The algorithm exploits these reference node 3-combinations aiming to find the best ones at a given time using a selection criteria that is based on ranging error and localization geometry. The simulation and experimental results indicate that the proposed algorithm reduces localization error considerably. This shows that it is possible to achieve sufficient localization accuracy using range-based trilateration localization, even based on the RSSI in challenging outdoor conditions, by employing applicable techniques and information.

Wireless Sensor Networks Using Sub-Pixel Optical Camera Communications: Advances in Experimental Channel Evaluation.

Optical wireless communications in outdoor scenarios are challenged by uncontrollable atmospheric conditions that impair the channel quality. In this paper, different optical camera communications (OCC) equipment are experimentally studied in the laboratory and the field, and a sub-pixel architecture is raised as a potential solution for outdoor wireless sensor networks (WSN) applications, considering its achievable data throughput, the spatial division of sources, and the ability of cameras to overcome the attenuation caused by different atmospheric conditions such as rain, turbulence and the presence of aerosols. Sub-pixel OCC shows particularly adequate capabilities for some of the WSN applications presented, also in terms of cost-effectiveness and scalability. The novel topology of sub-pixel projection of multiple transmitters over the receiver using small optical devices is presented as a solution using OCC that re-uses camera equipment for communication purposes on top of video-monitoring.

Temporal and Spatial Nearest Neighbor Values Based Missing Data Imputation in Wireless Sensor Networks.

Data missing is a common problem in wireless sensor networks. Currently, to ensure the performance of data processing, making imputation for the missing data is the most common method before getting into sensor data analysis. In this paper, the temporal and spatial nearest neighbor values-based missing data imputation (TSNN), a new imputation based on the temporal and spatial nearest neighbor values has been presented. First, four nearest neighbor values have been defined from the perspective of space and time dimensions as well as the geometrical and data distances, which are the bases of the algorithm that help to exploit the correlations among sensor data on the nodes with the regression tool. Next, the algorithm has been elaborated as well as two parameters, the best number of neighbors and spatial–temporal coefficient. Finally, the algorithm has been tested on an indoor and an outdoor wireless sensor network, and the result shows that TSNN is able to improve the accuracy of imputation and increase the number of cases that can be imputed effectively.

Performance Evaluation and Interference Characterization of Wireless Sensor Networks for Complex High-Node Density Scenarios.

The uncontainable future development of smart regions, as a set of smart cities’ networks assembled, is directly associated with a growing demand of full interactive and connected ubiquitous smart environments. To achieve this global connection goal, large numbers of transceivers and multiple wireless systems will be involved to provide user services and applications anytime and anyplace, regardless the devices, networks, or systems they use. Adequate, efficient and effective radio wave propagation tools, methodologies, and analyses in complex indoor and outdoor environments are crucially required to prevent communication limitations such as coverage, capacity, speed, or channel interferences due to high-node density or channel restrictions. In this work, radio wave propagation characterization in an urban indoor and outdoor wireless sensor network environment has been assessed, at ISM 2.4 GHz and 5 GHz frequency bands. The selected scenario is an auditorium placed in an open free city area surrounded by inhomogeneous vegetation. User density within the scenario, in terms of inherent transceivers density, poses challenges in overall system operation, given by multiple node operation which increases overall interference levels. By means of an in-house developed 3D ray launching (3D-RL) algorithm with hybrid code operation, the impact of variable density wireless sensor network operation is presented, providing coverage/capacity estimations, interference estimation, device level performance and precise characterization of multipath propagation components in terms of received power levels and time domain characteristics. This analysis and the proposed simulation methodology, can lead in an adequate interference characterization extensible to a wide range of scenarios, considering conventional transceivers as well as wearables, which provide suitable information for the overall network performance in crowded indoor and outdoor complex heterogeneous environments.

Analysis and evaluation of adaptive RSSI-based ranging in outdoor wireless sensor networks

Estimating inter-node distances based on received radio signal strength (RSSI) is the foundation of RSSI-based outdoor localization in wireless sensor networks (WSNs). However, the accuracy of RSSI-based ranging depends on environmental and weather conditions. Therefore, it is important that RSSI-based ranging adapts to prevailing conditions to improve its range and location accuracy. This paper analyzes and evaluates RSSI-based ranging and adaptive techniques in outdoor WSNs to improve the range quality. The findings highlight the effects of path loss exponent (PLE) estimation error and temperature change on RSSI-based ranging. Consequently, we analyze techniques for mitigating these detrimental effects and propose an adaptive RSSI-based ranging algorithm in order to improve the ranging quality in changing outdoor conditions. The algorithm comprises link RSSI estimation, temperature compensation, PLE estimation, and inter-node distance estimation. Furthermore, we evaluate the performance of the proposed algorithm and compare different WSN-specific PLE estimation techniques by employing real measurement data of 2.4 GHz IEEE 802.15.4-compliant WSN nodes. The results indicate that although ranging error can be mitigated using the proposed adaptive techniques, the accuracy when a single PLE estimate is used is, in general, limited due to high inter-link PLE variation.

Compressed Sensing in Multi-Hop Large-Scale Wireless Sensor Networks Based on Routing Topology Tomography

Data acquisition from multi-hop large-scale outdoor wireless sensor network (WSN) deployments for environmental monitoring is full of challenges. This is because of the severe resource constraints on tiny battery-operated motes (e.g., bandwidth, memory, power, and computing capacity), the data acquisition volume from large-scale WSNs, and the highly dynamic wireless link conditions in outdoor harsh communication environments. We present a novel compressed sensing approach, which can recover the sensing data at the sink with high fidelity when a very few data packets need to be collected, leading to a significant reduction of the network transmissions and thus an extension of the WSN lifetime. Interplaying with the dynamic WSN routing topology, the proposed approach is both efficient and simple to implement on the resource-constrained motes without motes’ storing of any part of the random projection matrix, as opposed to other existing compressed sensing-based schemes. We further propose a systematic method via machine learning to find a suitable representation basis, for any given WSN deployment and data field, which is both sparse and incoherent with the random projection matrix in compressed sensing for data collection. We validate our approach and evaluate its performance using a real-world outdoor multi-hop WSN testbed deployment in situ . The results demonstrate that our approach significantly outperforms existing compressed sensing approaches by reducing data recovery errors by an order of magnitude for the entire WSN observation field while drastically reducing wireless communication costs at the same time.

A Networked Sensor System for the Analysis of Plot-Scale Hydrology.

This study presents the latest updates to the Audubon Society of Western Pennsylvania (ASWP) testbed, a $50,000 USD, 104-node outdoor multi-hop wireless sensor network (WSN). The network collects environmental data from over 240 sensors, including the EC-5, MPS-1 and MPS-2 soil moisture and soil water potential sensors and self-made sap flow sensors, across a heterogeneous deployment comprised of MICAz, IRIS and TelosB wireless motes. A low-cost sensor board and software driver was developed for communicating with the analog and digital sensors. Innovative techniques (e.g., balanced energy efficient routing and heterogeneous over-the-air mote reprogramming) maintained high success rates (>96%) and enabled effective software updating, throughout the large-scale heterogeneous WSN. The edaphic properties monitored by the network showed strong agreement with data logger measurements and were fitted to pedotransfer functions for estimating local soil hydraulic properties. Furthermore, sap flow measurements, scaled to tree stand transpiration, were found to be at or below potential evapotranspiration estimates. While outdoor WSNs still present numerous challenges, the ASWP testbed proves to be an effective and (relatively) low-cost environmental monitoring solution and represents a step towards developing a platform for monitoring and quantifying statistically relevant environmental parameters from large-scale network deployments.

A low frequency piezoelectric energy harvester with trapezoidal cantilever beam: theory and experiment

With the development of low power MEMS device and outdoor wireless sensor network, new energy supply has been a key to replace the traditional energy which has some disadvantages. We have found that obtaining energy from the daily low frequency vibration environment is a very efficient solution to ease the pressure of energy sources. In this paper, a utility low frequency piezoelectric energy harvester with trapezoidal cantilever beam is presented. Then, a relevant theoretical model, optimization method and comparison experiment are made. The results show that the theoretical analysis results are in agreement with the experimental results; under the condition of constant beam length, open-circuit voltage and output power of the trapezoidal beam piezoelectric energy harvester are 81.6 and 167% more than those of the rectangular beam piezoelectric energy harvester. In addition, In order to further broaden the frequency band and improve output characteristics, we use the nonlinear method to redesign the experiment based on the above experiments. The results show that distance and magnet polarity change have an important effect on output characteristics.

LiveWSN: A memory-efficient, energy-efficient, reprogrammable, and fault-tolerant platform for wireless sensor network

Memory optimization, energy conservation, over-the-air reprogramming, and fault tolerance are the critical challenges for the proliferation of the wireless sensor network. To address these challenges, a new wireless sensor network platform termed LiveWSN is presented in this article. In LiveWSN, several new design concepts are implemented, such as the hierarchical shared-stack scheduling and the pre-linking native-code reprogramming. By doing so, the data memory cost of the LiveWSN scheduling system can be optimized by 25% if compared with that of the traditional multithreaded MANTIS OS. Moreover, the application reprogramming code size can be decreased by 72.6% if compared with that of the Contiki dynamic-linking reprogramming. In addition to the new design concepts, the new research approach which addresses the energy conservation and fault tolerance challenges by combining the software technique and the multi-core hardware technique is applied in LiveWSN. By means of the multi-core hardware infrastructure, the lifetime of the LiveWSN nodes can be prolonged by 34% if compared with the single-core Live node. Moreover, the fault-tolerant performance of the wireless sensor network node can be optimized significantly. With the above features, LiveWSN becomes memory efficient, energy efficient, reprogrammable, and fault tolerant, and it can run on the high resource-constrained nodes to execute the outdoor real-time wireless sensor network applications.

Improvement of range-free localization technology by a novel DV-hop protocol in wireless sensor networks

Localization is a fundamental issue for many applications in wireless sensor networks. Without the need of additional ranging devices, the range-free localization technology is a cost-effective solution for low-cost indoor and outdoor wireless sensor networks. Among range-free algorithms, DV-hop (Distance Vector-hop) has the advantage to localize the mobile nodes which has less than three neighbour anchors. Based on the original DV-hop algorithm, this paper presents two improved algorithms (Checkout DV-hop and Selective 3-Anchor DV-hop). Checkout DV-hop algorithm estimates the mobile node position by using the nearest anchor, while Selective 3-Anchor DV-hop algorithm chooses the best 3 anchors to improve localization accuracy. Then, in order to implement these DV-hop based algorithms in network scenarios, a novel DV-hop localization protocol is proposed. This new protocol is presented in detail in this paper, including the format of data payloads, the improved collision reduction method E-CSMA/CA, as well as parameters used in deciding the end of each DV-hop step. Finally, using our localization protocol, we investigate the performance of typical DV-hop based algorithms in terms of localization accuracy, mobility, synchronization and overhead. Simulation results prove that Selective 3-Anchor DV-hop algorithm offers the best performance compared to Checkout DV-hop and the original DV-hop algorithm.

An adaptive range-free localisation protocol in wireless sensor networks

It is well known that localisation is a fundamental issue for many wireless network applications. Without the need of additional ranging devices, the range–free localisation technology is a cost–effective solution for low–cost indoor and outdoor wireless sensor networks. However, we noted that most existing algorithms were only studied using tools like MATLAB neglecting possible problems in a real wireless network context such as frame collision and node synchronisation. Thus, we propose an adaptive range–free localisation protocol (ALP) based on IEEE 802.15.4 standard, which can evaluate localisation algorithms. Using our localisation protocol, we investigate and compare the performance of our new approach to some existing range–free algorithms in terms of localisation accuracy, mobility, synchronisation and overhead. Our results show that our Mid–perpendicular, Checkout DV–hop and Selective 3–Anchor DV–hop algorithms support robust and dynamic localisation in the context of our adaptive localisation protocol.

NetEye: a user‐centered wireless sensor network testbed for high‐fidelity, robust experimentation

SUMMARYExperimentation infrastructures for wireless sensor networks (WSNs) have been widely adopted over the past decade for high‐fidelity validation and rapid prototyping of applications and for discovering and modeling phenomena that have led to improvements in analysis and simulation capabilities. Nonetheless, the community is still looking for more heterogeneous WSN testbeds for purposes such as federation, experiment regression analysis, and improved testbed availability. In addition, most existing WSN testbeds lack robustness and predictability in the presence of failures, and they do not provide user‐friendly support for scientific experimentation (e.g., quick repetition of experiments). We develop the NetEye testbed as a high‐fidelity, robust WSN experimentation infrastructure that provides the ease of scientific experimentation. Consisting of 130 TelosB motes with IEEE 802.15.4 radios and 15 Linux laptops with IEEE 802.11a/b/g radios, NetEye provides a WSN deployment that enables high‐fidelity experimentation with single‐hop as well as multihop WSN networking, and the lab‐scale deployment mimics network properties of building‐scale and outdoor WSN deployments. Building upon the authors’ experience in developing and using WSN testbeds in the past years, NetEye also has enhanced features that enables easy, robust scientific experimentation. In particular, NetEye provides users with a simple, powerful web portal, which streamlines users’ experience in the whole lifecycle of scientific experimentation (e.g., experiment scheduling, status monitoring, and data exfiltration). NetEye employs a stable, fault‐tolerant state machine to improve the robustness of the testbed in the presence of failures. NetEye also develops a health monitoring service NetEye Doctor that monitors hardware and software status; the real‐time health monitoring information about the testbed is seamlessly integrated with the lifecycle of experiment scheduling, experiment status monitoring, and experiment data analysis for both robust experimentation and for informed experiment analysis. To address the log‐data congestion problem in long‐running, large scale experiments, NetEye employs a TDMA (Time Division Multiple Access) mechanism to ensure reliability in exfiltrating experiment data to users. To facilitate quick, repetitive scientific experimentation, NetEye also speeds up the mote programming cycle through fast job status transition and multi‐thread‐based mote programming. NetEye has been successfully running for over 3.5 years, and it has been integrated with the federated WSN experimental infrastructure KanseiGenie. NetEye is widely used by researchers from USA and Asia, and more than 10 experiments are executed through NetEye per day on average. Copyright © 2012 John Wiley & Sons, Ltd.

Constrained multiple deployment problem in wireless sensor networks with guaranteed lifetimes

We aimed to deploy wireless sensor networks with guaranteed lifetimes for outdoor monitoring projects. The provision of a guaranteed lifetime has rarely been studied in previous deployment problems. The use of battery packs as the power source for sensors is common in many applications involving outdoor wireless sensor networks (WSNs). Because unified battery power is unable to provide both efficient collection and balance workload, we address the deployment procedure by considering adjustable battery packs. The key issue is determining the minimum number of battery packs required to guarantee both the system efficiency and lifetime. We formulate a constrained multiple deployment problem with energy models of the battery energy budget and sensor operations. The optimal solution is obtained using integer linear programming. We derived a lower bound of the deployment cost in terms of the number of battery packs. Due to the high time complexity for solving the optimal solution, we also propose two heuristics with polynomial-time complexity: (1) a battery-aware routing algorithm that selects routing paths based on consideration of the battery usage, and (2) a refinement procedure to improve existing WSN deployments by adjusting traffic in order to reduce the cost. Theoretical analyses of the proposed algorithms revealed their time complexity. We performed extensive simulations to evaluate the proposed algorithms in terms of the deployment cost and residual energy. The results show that our algorithm generates deployments close to the lower bound.

On the Relevance of Using OpenWireless Sensor Networks in Environment Monitoring

This paper revisits the problem of the readiness for field deployments of wireless sensor networks by assessing the relevance of using Open Hardware and Software motes for environment monitoring. We propose a new prototype wireless sensor network that fine-tunes SquidBee motes to improve the life-time and sensing performance of an environment monitoring system that measures temperature, humidity and luminosity. Building upon two outdoor sensing scenarios, we evaluate the performance of the newly proposed energy-aware prototype solution in terms of link quality when expressed by the Received Signal Strength, Packet Loss and the battery lifetime. The experimental results reveal the relevance of using the Open Hardware and Software motes when setting up outdoor wireless sensor networks.

Thunder

Localization for outdoor wireless sensor networks has been a challenge for real applications. Although many solutions have been proposed, few of them can be used in real applications because of their high cost, low accuracy or infeasibility due to practical issues. In this paper, we propose a practical acoustic localization scheme called Thunder. Thunder employs an asymmetric architecture and shifts most of the complexities and hardware requirements from each node to a single powerful centralized device. The solution is efficient, and requires virtually zero cost in terms of extra per node hardware and innetwork communication. This paper also presents an efficient scheduling algorithm called Equilateral Triangle Scheduling to schedule Thunder for very large sensor networks and a resilient algorithm called Adaptive Fuzzy Clustering to provide robust localization without sacrificing efficiency in the presence of a high percentage of large ranging errors. To validate and evaluate Thunder, we built an experimental localization system based on the Mica2 platform, which achieved localization errors of about 1 meter in medium scale localization experiments.

Probabilistic localization for outdoor wireless sensor networks

Recent advances in wireless communication, low power sensors and microcontrollers enable the deployment of large-scale wireless sensor networks. Localization is a fundamental service required by many wireless sensor network applications. We consider a distributed, probabilistic approach, suitable for outdoor systems with inaccurate range measurements. The approach restricts the possible locations of the nodes by using a combination of positive and negative constraints. We reduce the computational complexity of the algorithm by using two-dimensional fast Fourier transforms (FFTs). We evaluated the proposed probabilistic approach through simulations based on real-world measurements; the results are compared with two other localization schemes and the Cramer-Rao lower bound (CRLB). The results show that, for inaccurate range measurements, the proposed probabilistic approach outperforms existing methods and approaches the CRLB.