Small-scale Wireless Sensor Networks Research Articles

Optimal Deployment of WSN Nodes for Crop Monitoring Based on Geostatistical Interpolations.

This paper proposes an integrated method for the estimation of soil moisture in potato crops that uses a low-cost wireless sensor network (WSN). Soil moisture estimation maps were created by applying the Kriging technique over a WSN composed of nodes. Our goal is to estimate the soil moisture of the crop with a small-scale WSN. Using a perfect mesh approach on a potato crop, experimental results demonstrated that 25 WSN nodes were optimal and sufficient for soil moisture characterization, achieving estimations errors . We provide a strategy to select the number of nodes to use in a WSN, to characterize the moisture behavior for spatio-temporal analysis of soil moisture in the crop. Finally, the implementation cost of this strategy is shown, considering the number of nodes and the corresponding margin of error.

WEED-MC: Wavelet Transform for Energy Efficient Data Gathering and Matrix Completion

Compressed sensing based data gathering is in-efficient in small scale wireless sensor networks because of uncorrelated observations at the sink. Failure to exploit the low rank and suitable transform domain to explore the correlation structure of sensor data, results in significantly low recovery accuracy in such matrix completion algorithms for small scale networks. Targeting the spatio temporal correlation structure of the data, a novel data gathering and matrix completion scheme, which exploits the low rank property and compactness of sensor data which exists in wavelet transform domain, is proposed in this work. The compactness of the sensor data in wavelet transform domain is used for recovering the missing entries of the matrix. Experiments and simulations, for single-node and multi-node scenarios, prove the efficacy of the proposed approach over existing schemes significantly in terms of recovery accuracy even at extremely low sampling rate.

Maximum Likelihood Estimators for Three-Dimensional Rigid Body Localization in Internet of Things Environments

Different from the conventional point source localization, rigid body localization (RBL) not only aims to estimate the position of the target but also to acquire the attitude information, which is also essential information in many Internet of Things (IoT) applications, such as the virtual reality systems, smart parking systems. This paper develops three maximum likelihood estimators (MLEs) for the RBL purpose in 3 dimensional space via a single base station. The MLEs are designed for the RBL framework, which adopts the direction of arrival (DoA) of the signal from a small scale wireless sensor network (SSWSN) mounted on the surface of the rigid target as measurement and can be realized by a single base station. The three MLEs respectively exploit the SSWSN topology information, the DoA measurement information only, as well as the equality constraint of the rotation matrix and the DoA measurement information. In addition, we implement the modified Guass-newton algorithm for the MLEs of the rotation matrix and the translation vector. Simulations show that the proposed MLE fusing the equality constraint of the rotation matrix and the DoA measurement information most approaches the Cramer-Rao Lower Bound and also outperforms the other two MLEs in terms of convergence success rate and the computational cost.

Nodes Deployment for Coverage in Rechargeable Wireless Sensor Networks

This paper considers a novel problem in rechargeable wireless sensor networks (WSNs), given a set of locations with one or more targets, determine the minimum number of sensor nodes to deploy in order to ensure a given coverage quality. This problem is significant as sensor nodes are often used to monitor one or more valuable assets or critical infrastructure. We formulate the problem as an integer linear program (ILP) and use it to compute the minimum number of sensor nodes required to monitor targets in small-scale WSNs. For large-scale WSNs, we relax the integer variables of the ILP and devise three approximation algorithms: greedy round node placement, target protection node placement, and energy efficient node placement (EENP). We prove the worst case performance bound of these algorithms. We also conducted simulation to compare these algorithms against the optimal solution produced by the ILP. Our results show that the solution computed by EENP is within one percentage point from the optimal solution.

A Novel Framework for Reliable Network Prediction of Small Scale Wireless Sensor Networks (SSWSNs)

A Novel Framework for Reliable Network Prediction of Small Scale Wireless Sensor Networks (SSWSNs)

Maintenance strategies for wireless sensor networks: from a reactive to a proactive approach

ABSTRACTIn this paper, we have introduced three proactive maintenance strategies for static wireless sensor networks (WSNs) using a limited number of mobile maintainer robots: the centralised proactive maintenance strategy denoted by CPMS, the fixed distributed proactive maintenance strategy denoted by FDPMS and the adaptive distributed proactive maintenance strategy denoted by ADPMS. The proposed maintenance strategies are based on a simple energy dissipation analytical model to estimate the occurrence times of the expected sensor failures in the network. Once identified, the anticipated failures are replaced by the available robots before they happen. To select the appropriate maintainer robot for each expected failure, CPMS opts for a centralised scheduling method based on the genetic algorithm fundamentals. However, the distributed maintenance strategies (FDPMS and ADPMS) use two different WSN area partitioning methods to share the sensor maintenance tasks among the available robots. Simulation results have shown that CPMS gives the minimal network dysfunction time representing the interruption service time induced by the detected faulted nodes. However, due to its significant signalling cost, we have remarked that CPMS can be deployed only in small scale WSNs. In large‐scale ones, ADPMS has demonstrated its efficiency in terms of the network dysfunction time, the robot travelled distance and the introduced signalling cost. In particular cases, when sensor failures are uniformly distributed on the network map, FDPMS has given the best performances. Copyright © 2013 John Wiley & Sons, Ltd.

MED-BS Clustering Algorithm for the Small-Scale Wireless Sensor Networks

With the spectacular progress of technology, we have witnessed the appearance of wireless sensor networks (WSNs) in several fields. In a hospital for example, each patient will be provided with one or more wireless sensors that gather his physiological data and send them towards a base station to treat them on behalf of the clinicians. The WSNs can be integrated on a building surface to supervise the state of the structure at the time of a destroying event such as an earthquake or an explosion. In this paper, we presented a Mobility-Energy-Degree-Distance to the Base Station (MED-BS) Clustering Algorithm for the small-scale wireless Sensor Networks. A node with lower mobility, higher residual energy, higher degree and closer to the base station is more likely elected as a clusterhead. The members of each cluster communicate directly with their ClusterHeads (CHs) and each ClusterHead aggregates the received messages and transmits them directly to the base station. The principal goal of our algorithm is to reduce the energy consumption and to balance the energy load among all nodes. In order to ensure the reliability of MED-BS, we compared it with the LEACH (Low Energy Adaptive Clustering Hierarchy) clustering algorithm. Simulation results prove that MED-BS improves the energy consumption efficiency and constructs a stable structure which can support new sensors without returning to the clusters reconstruction phase.

Low latency and energy efficient routing tree for wireless sensor networks with multiple mobile sinks

We propose the minimum Wiener index spanning tree (MWST) as a routing topology that is suitable for sensor networks with multiple mobile base nodes. However, it was proved that finding a spanning tree with the minimum Wiener index from a weighted graph is NP-hard. To address this problem and analyze the effectiveness of the MWST as the routing tree on sensor networks with multiple mobile base nodes, we designed two algorithms: a branch and bound algorithm for small-scale wireless sensor networks and a simulated annealing algorithm for large-scale wireless sensor networks. The simulation results show that MWST outperforms the minimum spanning tree (MST), one of the representative spanning trees used in many routing protocols for sensor networks, in terms of energy efficiency and packet delay.

Evaluating alternative approaches to mobile object localization in wireless sensor networks with passive architecture

In this study, we evaluate the performance of three types of techniques, namely neural based, Kalman filter based and trilateration based techniques, having been proposed to tackle the problem of real-time mobile sensor node tracking in a wireless sensor network with passive architecture. To investigate the performance of the aforementioned techniques under real-world circumstances, a small-scale wireless sensor network is deployed in an environment prone to multiple noise sources, multi-path and signal attenuation phenomena. The network makes use of a 433MHz MICA2 based Cricket platform, which is comprised of 6 Cricket motes, at least one of which is mobile. The network utilizes a passive architecture in which any mobile mote receives the Beacon signals to localize itself. Subsequently, a neural based approach is compared with a trilateration and a Kalman filter based technique. The results obtained corroborate the efficiency and advanced performance of the neural based approach.

MULTI-CLASSIFIER FOR HIGHLY RELIABLE DRIVER DROWSINESS DETECTION IN ANDROID PLATFORM

For the past decade, it is well defined in the literature that fatigue is one of the most prospective factor in affecting the driver behavior. This paper presents a novel evaluation of driver fatigue condition based on multi-classifier technique and fusion of attributes approach. The process involved fusion of attributes including image of eye movement and photoplethysmography (PPG) signals that are given as inputs to multi-classifier. In order to develop the best inference classifiers, artificial neural network (ANN), dynamic bayesian network (DBN), support vector machine (SVM), independent component analysis (ICA) and genetic algorithm (GA) were tested in our study. The output from each inference classifier are scaled and product in an intervention module to indicate driver aptitude in real-time. Implementation of monitoring system is practically designed in Android-based smartphone device where it can received all the sensory information from the dedicated sensors installed at the steering wheel via a small scale wireless sensor network. Device built-in front camera was utilized to capture driver facial image. No supplementary monitor is required to be installed in the vehicle as the all the information is to be displayed on the smartphone device itself. Warning system is triggered to warn driver once fatigue is suspected. System testing statistical results revealed that the manifold used of the proposed system demonstrates the advantages of performing information fusion, particularly with discrete methods, and the multi-classifier enabled a more authentic and ample driver fatigue evaluation.

플러딩 라우팅 프로토콜을 이용한 WSN 기반의 연안 환경 모니터링 시스템

The rapid water pollution in stream, river, lake and sea in recent years raises an urgent need for continuous monitoring and policymaking to conserve the global clean environment. In particular, the increasing water pollution in coastal marine areas adds to the importance of the environmental monitoring systems. In this paper, the mobile server is designed to gathers information of the water quality at coastal areas. The obtained data by the server is transmitted from field servers to the base station via multi-hop communication in wireless sensor network. The information collected includes dissolved oxygen(DO), hydrogen ion exponent(pH), temperature, etc. By the information provided the real-time monitoring of water quality at the coastal marine area. In addition, wireless sensor network-based flooding routing protocol was designed and used to transfer the measured water quality information efficiently. Telosb sensor node is programmed using nesC language in TinyOS platform for small scale wireless sensor network monitoring from a remote server.

Design of a Simple Self-Organizing and Multi-Hop Routing Algorithm Based on ZigBee Node

This paper briefly described the basic sensor network node architecture, point introduces the hardware design of the ZigBee Sensor nodes based on ATmega128L and CC2420, Power consumption of design node is low, smaller, higher integration, open to independent wireless communication module, multilayer, reducing the interference. And based on this hardware platform, Designed a self-organization Routing Protocol .Through networking experiments, verify the feasibility of the protocol. The protocol is very suitable for small-scale wireless sensor networks.

Integrated Real-Time Scheduling Strategy Based on Small-Scale Wireless Sensor Networks

Integrated Real-Time Scheduling Strategy Based on Small-Scale Wireless Sensor Networks

Energy saving with node sleep and power control mechanisms for wireless sensor networks

Energy efficiency sleep scheduling in wireless sensor networks is one of the most crucial technologies. In this paper, we propose a simple and feasible synchronous node sleeping and waking mechanisms for small scale wireless sensor networks. Sensor nodes are divided into forwarding nodes and listening nodes. Beacon frame containing sleep command from the coordinator can be forwarded to listening nodes via forwarding nodes. All the nodes in the network can enter sleep at about the same time. Through such network synchronization mechanisms, we can realize synchronous sleep and wake of the entire network. Furthermore, a new power control scheme based on routing protocol (PCBRP) in the medium access control (MAC) layer is proposed. It operates with the help of routing protocol and calculates optimal transmission power according to the distance between neighbor nodes. A mapping table including optimal transmission power and node address is established during the route building procedure. The transmission power can be obtained by searching the table with the address of next-hop neighbor in subsequent data transmissions. The proposed mechanisms are implemented in sensor nodes and are evaluated in a test-bed. The analysis and evaluation based on the experimental results confirm that the proposed energy-saving mechanisms are feasible and effective.

Cross-layer design and optimisation for wireless sensor networks

Cross-layer design and optimisation is a new technique which can be used to design and improve the performance in both wireless and wireline networks. The central idea of cross-layer design is to optimise the control and exchange of information over two or more layers to achieve significant performance improvements by exploiting the interactions between various protocol layers. In this paper, we propose a cross-layer design and optimisation framework and the concept of using an Optimisation Agent (OA) to provide the exchange and control of information between the various protocol layers to improve the performance in Wireless Sensor Networks (WSNs). The approach is to investigate the effects of the wireless channel and the physical layer performance of a small-scale WSN to develop insights that can be used in the design and development of the OA in the proposed cross-layer framework.