OELB - IH Algorithm for Secure Data Routing to Improve the Network Location Advisory Privacy Performance in WSN

Wireless network performance greatly depends on the number of factors such as output, delay packet delivery rate, packet drop rate, and many others. Each quality of service parameter greatly depends on other parameters also. However, the only obstacle which stops the performance achievement is security issues. In most cases, the adversary involves learning the network data to identify the routing strategy, data transmission strategy, and so on. When the adversary is capable of identifying the traffic and routing strategy, the adversary can perform different network. To improve the network performance and safeguard the network transmission using an Iterative heuristic algorithm, an efficient neighbor discovery-based security enhancement algorithm with Optimized Elastic Load Balancing (OELB) protocol is applied. In this Optimized Elastic Load Balancing Routing with Iterative Heuristic (OELB-IH) algorithm to provide secure communication in the sensor network. In this work, the Receiving Signal Strength Indication (RSSI) value to estimate the transmission support and transmitting signal range estimate to identify the nearest coverage nodes. The iterative heuristic algorithm performs tracking and seeking to achieve the node location and transmission error. In this OELB protocol, to identify the lower transmission path with lower energy consumption, it helps to multipath communication over the network. In this proposed has produced efficient results on security performance and throughput performance compared to other existing methods (SPAC, CPSLP, RRA)

More with less: Lowering user burden in mobile crowdsourcing through compressive sensing

Mobile crowdsourcing is a powerful tool for collecting data of various types. The primary bottleneck in such systems is the high burden placed on the user who must manually collect sensor data or respond in-situ to simple queries (e.g., experience sampling studies). In this work, we present Compressive CrowdSensing (CCS) - a framework that enables compressive sensing techniques to be applied to mobile crowdsourcing scenarios. CCS enables each user to provide significantly reduced amounts of manually collected data, while still maintaining acceptable levels of overall accuracy for the target crowd-based system. Näive applications of compressive sensing do not work well for common types of crowdsourcing data (e.g., user survey responses) because the necessary correlations that are exploited by a sparsifying base are hidden and non-Trivial to identify. CCS comprises a series of novel techniques that enable such challenges to be overcome. We evaluate CCS with four representative large-scale datasets and find that it is able to outperform standard uses of compressive sensing, as well as conventional approaches to lowering the quantity of user data needed by crowd systems

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Generative adversarial networks review in earthquake-related engineering fields

Within seismology, geology, civil and structural engineering, deep learning (DL), especially via generative adversarial networks (GANs), represents an innovative, engaging, and advantageous way to generate reliable synthetic data that represent actual samples' characteristics, providing a handy data augmentation tool. Indeed, in many practical applications, obtaining a significant number of high-quality information is demanding. Data augmentation is generally based on artificial intelligence (AI) and machine learning data-driven models. The DL GAN-based data augmentation approach for generating synthetic seismic signals revolutionized the current data augmentation paradigm. This study delivers a critical state-of-art review, explaining recent research into AI-based GAN synthetic generation of ground motion signals or seismic events, and also with a comprehensive insight into seismic-related geophysical studies. This study may be relevant, especially for the earth and planetary science, geology and seismology, oil and gas exploration, and on the other hand for assessing the seismic response of buildings and infrastructures, seismic detection tasks, and general structural and civil engineering applications. Furthermore, highlighting the strengths and limitations of the current studies on adversarial learning applied to seismology may help to guide research efforts in the next future toward the most promising directions

Collaborative Summarization of Topic-Related Videos

Large collections of videos are grouped into clusters by a topic keyword, such as Eiffel Tower or Surfing, with many important visual concepts repeating across them. Such a topically close set of videos have mutual influence on each other, which could be used to summarize one of them by exploiting information from others in the set. We build on this intuition to develop a novel approach to extract a summary that simultaneously captures both important particularities arising in the given video, as well as, generalities identified from the set of videos. The topic-related videos provide visual context to identify the important parts of the video being summarized. We achieve this by developing a collaborative sparse optimization method which can be efficiently solved by a half-quadratic minimization algorithm. Our work builds upon the idea of collaborative techniques from information retrieval and natural language processing, which typically use the attributes of other similar objects to predict the attribute of a given object. Experiments on two challenging and diverse datasets well demonstrate the efficacy of our approach over state-of-the-art methods.Comment: CVPR 201

Energy-Efficient Communication in Wireless Networks

This chapter describes the evolution of, and state of the art in, energy‐efficient techniques for wirelessly communicating networks of embedded computers, such as those found in wireless sensor network (WSN), Internet of Things (IoT) and cyberphysical systems (CPS) applications. Specifically, emphasis is placed on energy efficiency as critical to ensuring the feasibility of long lifetime, low‐maintenance and increasingly autonomous monitoring and control scenarios. A comprehensive summary of link layer and routing protocols for a variety of traffic patterns is discussed, in addition to their combination and evaluation as full protocol stacks

Sensor-based datasets for human activity recognition - a systematic review of literature

The research area of ambient assisted living has led to the development of activity recognition systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and the health care of the elderly and dependent people. However, before making them available to end users, it is necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks in experimental scenarios. For that reason, the scientific community has developed and provided a huge amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and is key to further progress in this area of research. This work presents a systematic review of the literature of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables taken from indexed publications related to this field was performed. The sources of information are journals, proceedings, and books located in specialized databases. The analyzed variables characterize publications by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed identification of the data set most used by researchers. On the other hand, the descriptive and functional variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation, representation, feature selection, balancing and addition of instances, and classifier used for recognition. This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most appropriate dataset to evaluate ARS and the classification techniques that generate better results

Sensor-based datasets for human activity recognition - a systematic review of literature

The research area of ambient assisted living has led to the development of activity recognition systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and the health care of the elderly and dependent people. However, before making them available to end users, it is necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks in experimental scenarios. For that reason, the scientific community has developed and provided a huge amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and is key to further progress in this area of research. This work presents a systematic review of the literature of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables taken from indexed publications related to this field was performed. The sources of information are journals, proceedings, and books located in specialized databases. The analyzed variables characterize publications by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed identification of the data set most used by researchers. On the other hand, the descriptive and functional variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation, representation, feature selection, balancing and addition of instances, and classifier used for recognition. This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most appropriate dataset to evaluate ARS and the classification techniques that generate better results