Routing Design Issues in Heterogeneous Wireless Sensor Network

WSN has important applications such as habitat monitoring, structural health monitoring, target tracking in military and many more. This has evolved due to availability of sensors that are cheaper and intelligent but these are having battery support. So, one of the major issues in WSN is maximization of network life. Heterogeneous WSNs have the potential to improve network lifetime and also provide higher quality networking and system services than the homogeneous WSN. Routing is the main concern of energy consumption in WSN. Previous research shows that performance of the network can be improve significantly using protocol of hierarchical HWSN. However, the appropriateness of a particular routing protocol mainly depends on the capabilities of the nodes and on the application requirements. This study presents different aspects of Heterogeneous Wireless Sensor network and design issues for routing in heterogeneous environment. Different perspectives from different authors regarding energy efficiency based on resource heterogeneity for heterogeneous wireless sensor networks have been presented

UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis

Indoor location information is an indispensable parameter for modern intelligent warehouse management and robot navigation. Indoor wireless positioning exhibits large errors due to factors such as indoor non-line-of-sight (NLOS) obstructions. In the present study, the error value under the time of arrival (TOA) algorithm was evaluated, and the trilateral positioning method was optimized to minimize the errors. An optimization algorithm for indoor ultra-wideband (UWB) positioning was designed, which was referred as annealing evolution and clustering fusion optimization algorithm. The algorithm exploited the good local search capability of the simulated annealing algorithm and the good global search capability of the genetic algorithm to optimize cluster analysis. The optimal result from sampled data was quickly determined to achieve effective and accurate positioning. These features reduced the non-direct aiming error in the indoor UWB environment. The final experimental results showed that the optimized algorithm significantly reduced noise interference as well as improved positioning accuracy in an NLOS indoor environment with less than 10 cm positioning error

Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Open Data

Open data is freely usable, reusable, or redistributable by anybody, provided there are safeguards in place that protect the data’s integrity and transparency. This book describes how data retrieved from public open data repositories can improve the learning qualities of digital networking, particularly performance and reliability. Chapters address such topics as knowledge extraction, Open Government Data (OGD), public dashboards, intrusion detection, and artificial intelligence in healthcare

Models for Efficient Automated Site Data Acquisition

Accurate and timely data acquisition for tracking and progress reporting is essential for efficient management and successful project delivery. Considerable research work has been conducted to develop methods utilizing automated site data acquisition for tracking and progress reporting. However, these developments are challenged by: the dynamic and noisy nature of construction jobsites; the indoor localization accuracy; and the data processing and extraction of actionable information. Limited research work attempted to study and develop customized design of wireless sensor networks to meet the above challenges and overcome limitations of utilizing off-the-shelf technologies. The objective of this research is to study, design, configure and develop fully customized automated site data acquisition models, with a special focus on near real-time automated tracking and control of construction operations embracing cutting edge innovations in wireless and remote sensing technologies. In this context, wireless and remote sensing technologies are integrated in two customized prototypes to monitor and collect data from construction jobsites. This data is then processed and mined to generate meaningful and actionable information. The developed prototypes are expected to have wider scope of applications in construction management, such as improving construction safety, monitoring the condition of civil infrastructure and reducing energy consumption in buildings. Two families of prototypes were developed in this research; Sensor Aided GPS (SA-GPS) prototype, which is designed and developed for tracking outdoor construction operations such as earthmoving; and Self-Calibrated Wireless Sensor Network (SC-WSN), which is designed for indoor localization and tracking of construction resources (labor, materials and equipment). These prototypes along with their hardware and software are encapsulated in a computational framework. The framework houses a set of algorithms coded in C# to enable efficient data processing and fusion that support tracking and progress reporting. Both the hardware prototypes and software algorithms were progressively tested, evaluated and re-designed using Rapid Prototyping approach. The validation process of the developed prototypes encompasses three steps; (1) simulation to validate the prototypes’ design virtually using MATLAB, (2) laboratory experiments to evaluate prototypes’ functionality in real time, and (3) testing on scaled case studies after fine-tuning the prototype design based on the results obtained from the first two steps. The SA-GPS prototype consists of a microcontroller equipped with GPS module as well as a number of sensors such as accelerometer, barometric pressure sensor, Bluetooth proximity and strain gauges. The results of testing the developed SA-GPS prototype on scaled construction jobsite indicated that it was capable of estimating project progress within 3% mean absolute percentage error and 1% standard deviation on 16 trials, in comparison to the standalone GPS which had approximately 12% mean absolute percentage error and 2% standard deviation. The SC-WSN prototype incorporates two main features. The first is the use of the Kalman filtering and smoothing for the RSSI signal to provide more stable and predictable signal for estimating the distance between a reader and a tag. The second is the use of a developed dynamic path-loss model which continually optimizes its parameters to cope with the dynamically changing construction environment using Particle Swarm Optimization (PSO) algorithm. The laboratory testing indicated the improvement in location estimation, where the produced location estimates using SC_WSN had an average error of 0.66m in comparison to 1.67m using the raw RSSI signal. Also the results indicated 60% accuracy improvement in estimating locations using the developed dynamic model. The developed prototypes are not only expected to reduce the risk of project cost and duration overruns by timely and early detection of deviations from project plan, but also enables project managers to observe and oversee their project’s status in near real-time. It is expected that the accuracy of the developed hardware, can be achieved on large-scale real construction projects. This is attributed to the fact that the developed prototype does not require any scalable improvements on its hardware technology, nor does it require any additional computational changes to its developed algorithms and software

A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming, following and random behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the family of AFSA, encompassing the original ASFA and its improvements, continuous, binary, discrete, and hybrid models, as well as the associated applications. A comprehensive survey on the AFSA from its introduction to 2012 can be found in [1]. As such, we focus on a total of {\color{blue}123} articles published in high-quality journals since 2013. We also discuss possible AFSA enhancements and highlight future research directions for the family of AFSA-based models.Comment: 37 pages, 3 figure