Links In Wireless Sensor Networks Research Articles

Virtualization in Wireless Sensor Networks: Fault Tolerant Embedding for Internet of Things

Recently, virtualization in wireless sensor networks (WSNs) has witnessed significant attention due to the growing service domain for Internet of Things (IoT). Related literature on virtualization in WSNs explored resource optimization without considering communication failure in WSNs environments. The failure of a communication link in WSNs impacts many virtual networks running IoT services. In this context, this paper proposes a framework for optimizing fault tolerance (FT) in virtualization in WSNs, focusing on heterogeneous networks for service-oriented IoT applications. An optimization problem is formulated considering FT and communication delay as two conflicting objectives. An adapted nondominated sorting-based genetic algorithm (A-NSGA) is developed to solve the optimization problem. The major components of A-NSGA include chromosome representation, FT and delay computation, crossover and mutation, and nondominance-based sorting. Analytical and simulation-based comparative performance evaluation has been carried out. From the analysis of results, it is evident that the framework effectively optimizes FT for virtualization in WSNs.

Semitensor Product Compressive Sensing for Big Data Transmission in Wireless Sensor Networks

Big data transmission in wireless sensor network (WSN) consumes energy while the node in WSN is energy-limited, and the data transmitted needs to be encrypted resulting from the ease of being eavesdropped in WSN links. Compressive sensing (CS) can encrypt data and reduce the data volume to solve these two problems. However, the nodes in WSNs are not only energy-limited, but also storage and calculation resource-constrained. The traditional CS uses the measurement matrix as the secret key, which consumes a huge storage space. Moreover, the calculation cost of the traditional CS is large. In this paper, semitensor product compressive sensing (STP-CS) is proposed, which reduces the size of the secret key to save the storage space by breaking through the dimension match restriction of the matrix multiplication and decreases the calculation amount to save the calculation resource. Simulation results show that STP-CS encryption can achieve better performances of saving storage and calculation resources compared with the traditional CS encryption.

Joint Optimization of Transmission Power Level and Packet Size for WSN Lifetime Maximization

In pursuit of better energy efficiency and enhanced network lifetime in wireless sensor networks (WSNs), two crucial factors are data packet size and transmission power level. On one hand, smaller packet size reduces the overall impact of bit error rates on packet loss. However, the consequence of smaller packet size is fragmentation into more data packets and thereby dissipation of increased energy. Hence, there emerges a delicate engineering tradeoff in deciding the data packet size, where both low and high data packet size decisions lead to certain energy inefficiency issues. On the other hand, increasing transmission power level decreases packet loss probability, which is another decision variable to optimize for maximizing network lifetime. Joint consideration of these two factors exacerbates the complexity of the optimization problem for the objective of the network lifetime maximization. In this paper, we develop a realistic WSN link layer model built on top of the empirically verified energy dissipation characteristics of Mica2 motes and WSN channel models. We make use of the aforementioned link layer model to design a novel mixed integer programming (MIP) framework for the joint optimization of transmission power level and data packet size to take up the challenge introduced above. Numerical evaluations of the MIP framework with the analysis of the results over a large parameter space are performed to characterize the effects of joint optimization of packet size and power level on WSN lifetime.

English

The propagation of radio signals is affected by several factors that contribute to the degradation of its quality and these are more significant on the propagation of wireless signals with low-power radios, typically used in Wireless Sensor Networks (WSNs). This makes radio links in WSNs to be unpredictable. This paper presents the development of an indoor channel prediction model of Wireless Sensor Network (WSN). A WSN testbed comprising of four TelosB sensor nodes was built at the corridor of first floor of Goddian Ezekwem building, faculty of Engineering, Nnamdi Azikiwe University, Awka. Three of the sensor nodes were placed at angles and the remaining one was attached to a laptop and used as the sink. Extensive measurements of RSSI were taken at distances of 1m to 7m at the interval of 1m. Channel prediction model of the environment was developed from the measured data using Linear Regression Analysis. The developed model was tested and the regression data analysis gives the R Square (R2) as 0.86, Multiple R as 0.93 and Adjusted R Square as 0.70.

Constructing Load-Balanced Data Aggregation Trees in Probabilistic Wireless Sensor Networks

Data Gathering is a fundamental task in Wireless Sensor Networks (WSNs). Data gathering trees capable of performing aggregation operations are also referred to as Data Aggregation Trees (DATs). Currently, most of the existing works focus on constructing DATs according to different user requirements under the Deterministic Network Model (DNM). However, due to the existence of many probabilistic lossy links in WSNs, it is more practical to obtain a DAT under the realistic Probabilistic Network Model (PNM). Moreover, the load-balance factor is neglected when constructing DATs in current literatures. Therefore, in this paper, we focus on constructing a Load-Balanced Data Aggregation Tree (LBDAT) under the PNM. More specifically, three problems are investigated, namely, the Load-Balanced Maximal Independent Set (LBMIS) problem, the Connected Maximal Independent Set (CMIS) problem, and the LBDAT construction problem. LBMIS and CMIS are well-known NP-hard problems and LBDAT is an NP-complete problem. Consequently, approximation algorithms and comprehensive theoretical analysis of the approximation factors are presented in the paper. Finally, our simulation results show that the proposed algorithms outperform the existing state-of-the-art approaches significantly.

Constructing load‐balanced virtual backbones in probabilistic wireless sensor networks via multi‐objective genetic algorithm

AbstractA connected dominating set (CDS) is used as a virtual backbone (VB) for efficient routing and broadcasting in wireless sensor networks (WSNs). Currently, almost all existing works focus on constructing minimum‐sized CDS under the deterministic network model. However, because of the existence of many probabilistic lossy links in WSNs, it is more practical to obtain a VB under the realistic probabilistic network model (PNM). Moreover, load‐balance factor cannot be neglected when constructing a VB to prolong network lifetime. Hence, in this paper, we propose a multi‐objective genetic algorithm to construct a load‐balanced VB under PNM. Through simulations, we demonstrate that our proposed methods extend network lifetime by 69% on average compared with the existing state‐of‐the‐art approaches. Copyright © 2014 John Wiley & Sons, Ltd.

Ubiquitous, Yet Deceptive: Hardware-Based Channel Metrics on Interfered WSN Links

The ease of acquiring hardware-based link quality indicators (LQIs) is an alluring property for fast channel estimation in time- and safety-critical wireless sensor network (WSN) applications, such as closed-loop control and interlocking. The two rudimentary hardware-based channel quality metrics, i.e., received signal strength (RSS) and LQI, are the key constituents of channel estimation and a plethora of other WSN functionalities, from routing to transmit power control. Nevertheless, this paper highlights three deficient aspects of these two indicators: 1) Overall deceptiveness, i.e., the inability to reveal the presence of interference, falsely indicating excellent channel conditions in an unacceptably high fraction of cases; 2) the burstiness of missed detection which compromises the attempts to eliminate the deceptiveness by averaging; and 3) high mutual discrepancy of the two indicators, which is observed in 39%–73% of packets, throughout different scenarios. The ability of RSS and LQI to indicate IEEE 802.11 interference is scrutinized in a variety of scenarios in typical industrial environments using commercial-off-the-shelf hardware and realistic network topologies, giving the findings of this paper high general validity and practical relevance.

Background Subtraction for Online Calibration of Baseline RSS in RF Sensing Networks

Radio frequency (RF) sensing networks are a class of wireless sensor networks (WSNs) which use RF signals to accomplish tasks such as passive device-free localization and tracking. The algorithms used for these tasks usually require access to measurements of baseline received signal strength (RSS) on each link. However, it is often impossible to collect this calibration data (measurements collected during an offline calibration period when the region of interest is empty of targets). We propose adapting background subtraction methods from the field of computer vision to estimate baseline RSS values from measurements taken while the system is online and obstructions may be present. This is done by forming an analogy between the intensity of a background pixel in an image and the baseline RSS value of a WSN link and then translating the concepts of temporal similarity, spatial similarity, and spatial ergodicity, which underlie specific background subtraction algorithms to WSNs. Using experimental data, we show that these techniques are capable of estimating baseline RSS values with enough accuracy that RF tomographic tracking can be carried out in a variety of different environments without the need for a calibration period.

CrossVit: Enhancing Canopy Monitoring Management Practices in Viticulture

A new wireless sensor network (WSN), called CrossVit, and based on MEMSIC products, has been tested for two growing seasons in two vineyards in Italy. The aims are to evaluate the monitoring performances of the new WSN directly in the vineyard and collect air temperature, air humidity and solar radiation data to support vineyard management practices. The WSN consists of various levels: the Master/Gateway level coordinates the WSN and performs data aggregation; the Farm/Server level takes care of storing data on a server, data processing and graphic rendering; Nodes level is based on a network of peripheral nodes consisting of a MDA300 sensor board and Iris module and equipped with thermistors for air temperature, photodiodes for global and diffuse solar radiation, and an HTM2500LF sensor for relative humidity. The communication levels are: WSN links between gateways and sensor nodes by ZigBee, and long-range GSM/GPRS links between gateways and the server farm level. The system was able to monitor the agrometeorological parameters in the vineyard: solar radiation, air temperature and air humidity, detecting the differences between the canopy treatments applied. The performance of CrossVit, in terms of monitoring and reliability of the system, have been evaluated considering: its handiness, cost-effective, non-invasive dimensions and low power consumption.

Successful Deployment of a Wireless Sensor Network for Precision Agriculture in Malawi

This paper demonstrates how an irrigation management system (IMS) can practically be implemented by deploying a wireless sensor network (WSN). Specifically, the paper describes an IMS which was set up in Manja township, city of Blantyre. Deployment of IMS in rural areas of developing countries like Malawi is a challenge as grid power is scarce. For the system to be self-sustained in terms of power, the study used solar photovoltaic and rechargeable batteries to power all electrical devices. The system incorporated a remote monitoring mechanism through a General Packet Radio Service modem to report soil temperature, soil moisture, WSN link performance, and photovoltaic power levels. Irrigation valves were activated to water the field. Preliminary results in this study have revealed a number of engineering weaknesses of deploying such a system. Nevertheless, the paper has highlighted areas of improvement to develop a robust, fully automated, solar-powered, and low-cost IMS to suit the socioeconomic conditions of small scale farmers in developing countries.

A Loss Inference Algorithm for Wireless Sensor Networks to Improve Data Reliability of Digital Ecosystems

Digital ecosystems (DEs) are based on a large amount of distributed data, and these data are gathered from physical devices, particularly from wireless sensor networks (WSNs). Due to the inherent stringent bandwidth and energy constraints, energy-efficient mechanisms of performance measurement are the key to the proper operation of WSNs and thereby important for the data reliability of DEs. This paper presents a novel algorithm, i.e., Loss Inference based on Passive Measurement (LIPM), to infer WSN link loss performance. The LIPM algorithm passively monitors the application traffic between sensor nodes and the sink (base station), and then uses network tomography technology to infer the network internal performance. Furthermore, contour maps, the well-known representation of data, are first taken into account in WSN loss performance inference, which can help the LIPM algorithm identify lossy areas rapidly. Finally, the algorithm is validated through simulations and exhibits good performance and scalability.