One of the main design aspects of Wireless Sensor Networks (WSNs) is the deployment strategy of the sensors. In general, WSN deployment methods fall under two categories: planned deployment and random deployment. In this paper, we focus on planned deployment which is defined as selectively deciding the locations of the sensors to optimize one or more design objectives of the WSN under some given constraints. There have been a large number of studies which proposed algorithms for solving the planned deployment problem. In this paper, we present a novel classification of the algorithms proposed in the literature for planned deployment of WSNs, based on the mathematical approach used for modeling and solving the deployment problem. Four distinct mathematical approaches are presented: Genetic Algorithms, Computational Geometry, Artificial Potential Fields and Particle Swarm Optimization. For each approach, we provide a discussion of its background and basic mathematical foundation. We then review the algorithms which belong to each approach and provide a comparison between them in terms of their objectives, assumptions and performance. Based on our extensive survey, we discuss the strengths and limitations of the four approaches and compare them in terms of the different WSN design factors.

Classification of Wireless Sensor Networks Deployment Techniques

Distributed data aggregation is an important task, allowing the decentralized determination of meaningful global properties, which can then be used to direct the execution of other applications. The resulting values are derived by the distributed computation of functions like Count , Sum , and Average . Some application examples deal with the determination of the network size, total storage capacity, average load, majorities and many others. In the last decade, many different approaches have been proposed, with different trade-offs in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of aggregation algorithms, it can be difficult and time consuming to determine which techniques will be more appropriate to use in specific settings, justifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally defines the concept of aggregation, characterizing the different types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.

/pdf/a-survey-of-distributed-data-aggregation-algorithms-16eiyu3imz.pdf

A Survey of Distributed Data Aggregation Algorithms

Intrusive multi-step attacks, such as Advanced Persistent Threat (APT) attacks, have plagued enterprises with significant financial losses and are the top reason for enterprises to increase their security budgets. Since these attacks are sophisticated and stealthy, they can remain undetected for years if individual steps are buried in background "noise." Thus, enterprises are seeking solutions to "connect the suspicious dots" across multiple activities. This requires ubiquitous system auditing for long periods of time, which in turn causes overwhelmingly large amount of system audit events. Given a limited system budget, how to efficiently handle ever-increasing system audit logs is a great challenge. This paper proposes a new approach that exploits the dependency among system events to reduce the number of log entries while still supporting high-quality forensic analysis. In particular, we first propose an aggregation algorithm that preserves the dependency of events during data reduction to ensure the high quality of forensic analysis. Then we propose an aggressive reduction algorithm and exploit domain knowledge for further data reduction. To validate the efficacy of our proposed approach, we conduct a comprehensive evaluation on real-world auditing systems using log traces of more than one month. Our evaluation results demonstrate that our approach can significantly reduce the size of system logs and improve the efficiency of forensic analysis without losing accuracy.

High Fidelity Data Reduction for Big Data Security Dependency Analyses

DLDM: Deep learning-based defense mechanism for denial of service attacks in wireless sensor networks

Holistic aggregations are popular queries for users to obtain detailed summary information from Wireless Sensor Networks. An aggregation operation is holistic if there is no constant bound on the size of the storage needed to describe a sub-aggregation. Since holistic aggregation cannot be distributable, it requires that all the sensory data should be sent to the sink in order to obtain the exact holistic aggregation results, which costs lots of energy. However, in most applications, exact holistic aggregation results are not necessary; instead, approximate results are acceptable. To save energy as much as possible, we study the approximated holistic aggregation algorithms based on uniform sampling. In this article, four holistic aggregation operations, frequency, distinct-count, rank, and quantile, are investigated. The mathematical methods to construct their estimators and determine optional sample size are proposed, and the correctness of these methods are proved. Four corresponding distributed holistic algorithms to derive (e, δ)-approximate aggregation results are given. The solid theoretical analysis and extensive simulation results show that all the proposed algorithms have high performance on the aspects of accuracy and energy consumption.

https://dl.acm.org/doi/pdf/10.1145/3027488

Approximate Holistic Aggregation in Wireless Sensor Networks

Aggregations of sensed data are very important for users to get summary information about monitored area in applications of wireless sensor networks (WSNs). As the approximate aggregation results are enough for users to perform analysis and make decisions, many approximate aggregation algorithms are proposed for WSNs. However, most of the algorithms have fixed error bounds and cannot meet arbitrary precision requirement, the uniform sampling based algorithm which can reach arbitrary precision is just suitable for the static networks. Considering the dynamic property of WSNs, in this paper, we propose an approximate aggregation algorithm based on Bernoulli sampling to satisfy arbitrary precision requirement. Besides, two adaptive algorithms are also proposed, one is for adapting the sample with varying of precision requirement, the other is for adapting the sample with varying of sensed data. The theoretical analysis and experiment results show that the proposed algorithms have high performance in terms of accuracy and energy consumption.

Bernoulli sampling based (ε, δ)-approximate aggregation in large-scale sensor networks

Spatial-temporal multi-feature fusion network for long short-term traffic prediction

Target tracking is a well studied topic in wireless sensor networks. However, uncertainty existed in sensor networks presents new challenges for it. Besides the energy conservation of networks, target tracking has to deal with different kinds of uncertainty, such as the impreciseness of positioning systems, environment noise and limited sensitivity of sensors. In this paper, we study the problem of target tracking under uncertainty in wireless sensor networks. We first investigate the uncertainty existed in sensor networks and propose a series of general models. Then, we introduce the problem of probabilistic ?-nearest neighbors (P?NN) and provide an efficient tracking algorithm based on P?NN retrieval under the proposed models. Finally, a comprehensive set of simulations are presented. We conclude that the proposed tracking algorithm yields excellent tracking performance in wireless sensor networks.

Target Tracking under Uncertainty in Wireless Sensor Networks

Heat shock transcription factor (Hsf) exists widely in eukaryotes and responds to various abiotic stresses by regulating the expression of downstream transcription factors, functional enzymes, and molecular chaperones. In this study, TaHsfA2-13, a heat shock transcription factor belonging to A2 subclass, was cloned from wheat (Triticum aestivum) and its function was analyzed. TaHsfA2-13 encodes a protein containing 368 amino acids and has the basic characteristics of Hsfs. Multiple sequence alignment analysis showed that TaHsfA2-13 protein had the highest similarity with TdHsfA2c-like protein from Triticum dicoccoides, which reached 100%. The analysis of tissue expression characteristics revealed that TaHsfA2-13 was highly expressed in root, shoot, and leaf during the seedling stage of wheat. The expression of TaHsfA2-13 could be upregulated by heat stress, low temperature, H2O2, mannitol, salinity and multiple phytohormones. The TaHsfA2-13 protein was located in the nucleus under the normal growth conditions and showed a transcriptional activation activity in yeast. Further studies found that overexpression of TaHsfA2-13 in Arabidopsis thaliana Col-0 or athsfa2 mutant results in improved tolerance to heat stress, H2O2, SA and mannitol by regulating the expression of multiple heat shock protein (Hsp) genes. In summary, our study identified TaHsfA2-13 from wheat, revealed its regulatory function in varieties of abiotic stresses, and will provide a new target gene to improve stress tolerance for wheat breeding.

https://www.frontiersin.org/articles/10.3389/fpls.2022.922561/pdf

Characteristics and Regulating Roles of Wheat TaHsfA2-13 in Abiotic Stresses

Multi-scale convolutional networks for traffic forecasting with spatial-temporal attention

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