Risk assessment and network description modules in a multi-level wireless sensor network risk assessment ontology

Development of a Korean offshore wind power HSE risk assessment module based on systems engineering approach

Shallow slope failures are one of the most frequent geological hazards in mountainous areas, which pose great danger to people and properties in affected areas. This paper presents a cell-based platform for quantitative risk assessment for shallow slope failures at a regional scale. There are five components in this platform; namely, a digital terrain module, a spatial rainfall distribution module, a slope stability and reliability evaluation module, a movement prediction module, and a risk assessment module. The locations and volumes of the slope failures are estimated using the slope stability and reliability evaluation module. The movement prediction module is used to predict the potential movement traces of the detached material, which moves along the steepest path. The risks of the slope failures are finally assessed using the risk assessment module, which can be used as an indicator for early warning. The vulnerability of a person to slope failure is determined by the volume of slope failure and angle of reach. The platform is applied to a 164.5 km2hilly terrain in the Wenchuan earthquake zone to test its performance in quantitative risk assessment for regional shallow slope failures. The platform evaluates the risks of shallow slope failures at a regional scale efficiently.

This paper presents a survey on the current state-of-the-art in Wireless Sensor Network (WSN) Operating Systems (OSs). In recent years, WSNs have received tremendous attention in the research community, with applications in battlefields, industrial process monitoring, home automation, and environmental monitoring, to name but a few. A WSN is a highly dynamic network because nodes die due to severe environmental conditions and battery power depletion. Furthermore, a WSN is composed of miniaturized motes equipped with scarce resources e.g., limited memory and computational abilities. WSNs invariably operate in an unattended mode and in many scenarios it is impossible to replace sensor motes after deployment, therefore a fundamental objective is to optimize the sensor motes’ life time. These characteristics of WSNs impose additional challenges on OS design for WSN, and consequently, OS design for WSN deviates from traditional OS design. The purpose of this survey is to highlight major concerns pertaining to OS design in WSNs and to point out strengths and weaknesses of contemporary OSs for WSNs, keeping in mind the requirements of emerging WSN applications. The state-of-the-art in operating systems for WSNs has been examined in terms of the OS Architecture, Programming Model, Scheduling, Memory Management and Protection, Communication Protocols, Resource Sharing, Support for Real-Time Applications, and additional features. These features are surveyed for both real-time and non-real-time WSN operating systems.

With the success of wireless sensor networks (WSNs), traditional engineering and infrastructure industries are starting to develop solutions using WSN technologies. One of the main challenges of designing and developing WSNs for industrial monitoring and control is satisfying their strict reliability requirements. In this paper, we present a network-level reliability model, namely, end-to-end data delivery reliability (E2E-DDR), for estimating and optimizing the reliability performance of WSNs. In the E2E-DDR model, a framework is presented for capturing the mapping function between the packet reception ratio, background noise, and received signal strength (RSS). We use an alpha-stable distribution to accurately represent the background noise and a modified log-normal path loss model to more realistically describe the RSS. We also report a comprehensive performance evaluation performed by applying the E2E-DDR model in a real-world case study to estimate the network-level reliability and optimize the WSN deployment parameters. Note to Practitioners —The goal of this paper is to improve the estimation and optimization of the network-level reliability performance of industrial wireless sensor networks (WSNs) to satisfy their strict control requirements. In harsh industrial application scenarios, many factors affect radio link quality, such as RF transmit power, communication distance, and random background noise. Existing link quality estimation approaches mainly focus on providing a smoothed estimation of radio link quality without any solution for optimizing communication reliability to satisfy certain requirements. This paper suggests a new approach in which WSN nodes are used to measure and estimate the parameters of the scenario in which the nodes will be deployed and then to estimate and optimize the worst case reliability (which is a lower-bound value rather than a smoothed value) to ensure that the network is qualified. Through a real-world case study, we demonstrate how to estimate the lower bound on reliability and how to optimize the reliability by computing the maximum deployment distance between nodes as an example. The experiments suggest that this approach is feasible.

The advantages of deploying mobile sinks (MSs) in wireless sensor networks (WSNs) have attracted tremendous attention recently, because mobility of a sink provides an efficient method to improve data gathering performance in WSNs. However, in such kind of WSN, the efficiency of data dissemination is obviously affected by the trajectory of the MS. Therefore, optimal moving schemes of the MS have been studied in many researches. Nevertheless, in many cases, the trajectory of the MS is not able to change ideally as previous studies assumed but depending on the environmental conditions. In this paper, we focus on the efficiency of data gathering in WSN with a MS whose trajectory cannot change ideally and propose a new data diffusion scheme - implementing a new Pre-Diffusion Algorithm (PDA) in a hierarchical WSN aimed at improving data gathering efficiency. This hierarchical WSN is composed of several clusters, and datatransmission is done by higher powered cluster head (CH) node. Our scheme predetermines the cluster area where the MS is going to arrive in most probability in next period of time. MS fulfills data converging in the cluster head (CH)whose distance to the MS may the smallest compared with other CHs when data gathering starts. Thus more data can be collected with less trajectory length of MS. Simulation results show that our proposed scheme improves data gathering efficiency as compared to conventional scheme.

Wireless sensor networks (WSNs) currently play an important role due to their variety of applications in several fields. Improving the overall WSN performance and avoiding its limitations and challenges have become the subject of many researchers. One of the main critical issues is transmitted data security. A sinkhole attack is one of the most dangerous attacks that can be used as a platform for starting several attacks. It has a direct influence on network performance in terms of information confidentiality, integrity, and even availability. An effective method to detect such an attack leads to improving the overall WSN performance as well as enhancing the sent data secrecy. This article introduces an enhanced intrusion detection system (IDS) to protect WSNs from sinkhole attacks. The IDS is modified with a reputation-based mechanism to make it compatible with WSN requirements. An artificial bee colony (ABC) optimization technique is implemented to improve the IDS performance. Noisy channels are added to take into consideration the nature of the WSN. The proposed system achieves more than 97% overall accuracy and a detection rate of 98% with a false positive rate of less than 1.7%, which seems superior to the results of previous works.

The increasing in the demand for Wireless Sensor Networks (WSNs) has intensified studies which are dedicated to obtain more energy-efficient solutions, since the energy storage limitation is critical in those systems. Additionally, there are other aspects which usually must be ensured in order to get an acceptable performance of WSNs, such as area coverage and network connectivity. This paper proposes a procedure for enhancing the performance of WSNs: a multiobjective hybrid optimization algorithm is employed for solving the Dynamic Coverage and Connectivity Problem (DCCP) in flat WSNs subjected to node failures. This method combines a multiobjective global on-demand algorithm (MGoDA), which improves the current DCCP solution using a Genetic Algorithm, with a local on line algorithm (LoA), which is intended to restore the network coverage soon after any failure. The proposed approach is compared with an Integer Linear Programming (ILP)-based approach and a similar mono-objective approach with regard to coverage, network lifetime and required running time for achieving the optimal solution provided by each method. Results achieved for a test instance show that the hybrid approach presented can improve the performance of the WSN obtaining good solutions with a considerably smaller computational time than ILP. The multiobjective approach still provides a feasible method for extending WSNs lifetime with slight decreasing in the network mean coverage.

In recent year, the most emerging and growing field of research and development is “Internet of Things” (IoT). This is due to advancement in wireless sensor network (WSN) which operate in the unlicensed industrial, scientific and medical (ISM) spectrum band. However, many wireless technologies operate in the same unlicensed spectrum, make it overcrowded and hence resulted in spectrum scarcity among those bands, the performance of WSN will degrade as their popularity increases. According to FCC report, most of the licensed spectrum is underutilized, sharing of underutilized licensed spectrum among unlicensed devices is a promising solution to the spectrum scarcity issue. Cognitive Radio (CR) is one of the capable technology that allows sensor nodes (as a Secondary Users (SUs)) to detect and use the underutilized licensed spectrum temporarily when Primary Users (PUs) not using it. With recent advances in Cognitive Radio (CR) technology, possible to apply the Dynamic Spectrum Access (DSA) model in WSNs to get access to underutilized licensed spectrum, possibly with better propagation characteristics, but as the existing protocols and algorithms developed for CRNs and WSNs are not directly applicable to CR-based WSNs and required new protocols. In this paper, we present a survey on the novel design of CR-based MAC, identify the main advantages and challenges of using CR technology, and compare the different method of improving energy efficiency. We believe that CR-WSN is the next-generation WSN. In this paper, we also discussed the open issues to motivate new research interest in this field.

The use of wireless sensor network (WSN) in industrial site has been increasing significantly. WSN transmits and collects the data through electromagnetic waves. The characteristic of electromagnetic wave is affected by the environment where electromagnetic wave is propagating. The electromagnetic propagation in industrial site encounters various challenges, caused not only by the object around it but also by the noise disturbances coming from the electrical machine. These disturbances can degrade the performance of WSN. Therefore, simulating the performance of WSN prior to the implementation is suggested, so that errors during implementation can be avoided. This paper presents a simulation to determine the effect of disturbances to radiation of electromagnetic wave from operating the WSN. In this research, WSN was modelled by dipole antenna with 2.4 GHz frequency, while the electrical machine was modelled as permanent magnet synchronous machine (PMSM). Both models were simulated in simple 3D industrial site consist of some object. The results show that the characteristic of radiation from dipole antennae, e.g., gain, S-parameter, and directivity was affected by electrical machine.

Wireless sensor networks (WSN) are often applied to monitor the environment such as mine. Due to the rapid attenuation of the wireless signals and the limited power of the WSN nodes, the performances of WSN in these environments are not ideal. In this paper, we present a hybrid radio over fiber wireless sensor network architecture to compensate the weakness of traditional WSN architecture. Considering the remote antenna unit of radio over fiber as a heterogeneous cluster sink node of the WSN, this paper investigates the impact of different rules and behaviors of remote antenna unit on the hybrid architecture. Analyses and simulation results show that a suitable number of remote antenna units can significantly reduce the network delay, energy consumption and improve the practicability of the WSN.

The research consolidated in this thesis is motivated by the recent evolvement of wireless technologies and microelectronic devices, which instigated the emergence of Wireless Sensor Networks (WSNs). Many WSN-based applications have come about; these applications are related, but not limited, to the fields of military, environment and health care. Research in WSNs is still in its early stages, and efforts have been put forward to design fast, reliable, and fault-tolerant protocols that guarantee acceptable levels of quality for events delivery, to meet the limited capabilities of sensor nodes and the effects of unreliable wireless communication. In this thesis, we focus on the design of a Knowledge-based framework for extracting behavioral patterns regarding sensor nodes from WSNs. Three types of behavioral patterns are introduced: Sensor Association Rules, Coverage-based Rules and Sensor Chronological Patterns. The proper steps in the Knowledge Discovery process that pertain to the extraction of the behavioral patterns are defined. These steps are: (i) a formal definition of the required ‘knowledge’; (ii) the data preparation stage that covers the communication aspects of the process of preparing data that is needed to extract these patterns; (iii) the data mining techniques that are essential for extracting the required patterns. A set of schemes have been proposed to attain these steps, and meet the critical properties of WSNs. In contrast to other techniques, the proposed behavioral patterns are mainly about the sensor nodes, instead of the area under monitoring. The direct application of the proposed patterns is enhancing the performance of WSNs by participating in the resource management process of sensor nodes, and reducing the undesired cons of wireless communication; thus improving the Quality of Service of WSNs. Several experiments have been conducted, using synthetic and real data, to report about the performance of proposed schemes.

Wireless charging is a greatly promising technology to resolve the energy limitation problem suffered by wireless sensor networks. In this paper, we propose an efficient mobile energy replenishment scheme based on hybrid mode, namely MERSH, which merges the advantages of the existing online and offline modes to improve the energy supplement efficiency and meanwhile to adapt well to the energy consumption dynamicity of sensors to achieve sustainable operation of wireless sensor networks. In MERSH, nodes are allowed to proactively send charging requests to the mobile charger, called MC, when the residual energy goes down a lower level. With a special-designed low-cost method, MERSH can plan an optimized path for MC every time when it charges the sensors that have sent charging requests. Moreover, to ensure energy supplement and avoid node failure due to the high dynamic energy consumption, MERSH dynamically adjusts the charging duration of each node along the calculated charging path according to its maximum tolerate charging latency and minimum charging wait time. The theoretic analysis shows that MERSH guarantees MC to move along the optimized charging path, which reduces the whole charging cost. Through extensive simulation results, we validate the effectiveness of the proposed MERSH.

Wireless sensor networks (WSNs) are formed using a cluster of sensor nodes (SNs), deployed randomly to perform sensing operations in an area under observation. Due to the unavailability of an external power source, the energy efficiency considered as one of the critical issues in WSNs. Selection of a sensor node (SN) from a wireless sensor network (WSN) cluster to serve as an aggregator or cluster head (CH), considered as an efficient method to increase the lifetime of wireless sensor network (WSN). In this paper, we have proposed an energy efficient CH selection scheme for WSN, to enhance the lifetime and average residual energy of a single WSN cluster. The proposed strategy nominates a group SNs as CHs, based on their channel condition with the base station (BS) and their residual energy. The proposed algorithm is helpful in solving the problem of unbalanced energy consumption in WSNs. Furthermore, the mechanism of using mobile sink during the hand-off stage helps to overcome the delay in data transmission. Moreover, the incorporation of energy harvesting significantly increases the lifetime of WSN. In comparison to a state-of-art technique available in the literature, our scheme shows a 33% increase in lifetime and presents a steady decrease in residual energy for the same rounds of data transmission.

The paper presents some results of investigations regarding the development of a method for ships safety estimation in critical conditions. The objectives of investigations are briefly described including the method introduction. Special attention is paid towards application of the direct and indirect methods. Four modules of the computational model are presented including those connected with the ship/environment definition, hazard identification, hazard and risk assessment. Then, the hazard and risk assessment modules are used to show an application. A simplified cargo type ship is defined to demonstrate how the computational model can enable the risk assessment to be done for the given initiating event and scenario development (it follows from the assumed scenario of flooding the watertight compartments ). The risk assessment results can be used by both the risk reduction and risk management procedures. The preliminary practical remarks regarding the ships safety estimation in critical conditions using the presented method and computational model are given.

A joint routing and localization algorithm for emergency scenario