Energy Efficient and Scalable Search in Dense Wireless Sensor Networks
TL;DR: It is shown by analysis, simulation, and implementation in testbed that IRS and k-IRS are highly scalable, the cost of search (total number of transmitted bytes) is independent of node density, and it is much lower than that of existing proposals under high node density.
Abstract: In this paper, we consider the problem of information discovery in a densely deployed wireless sensor network (WSN), where the initiator of search is unaware of the location of target information. We propose two protocols: increasing ray search (IRS), an energy efficient and scalable search protocol, and k-IRS, an enhanced variant of IRS. The priority of IRS is energy efficiency and sacrifices latency whereas k-IRS is configurable in terms of energy-latency trade-off and this flexibility makes it applicable to varied application scenarios. The basic principle of these protocols is to route the search packet along a set of trajectories called rays that maximizes the likelihood of discovering the target information by consuming least amount of energy. The rays are organized such that if the search packet travels along all these rays, then the entire terrain area will be covered by its transmissions while minimizing the overlap of these transmissions. In this way, only a subset of total sensor nodes transmits the search packet to cover the entire terrain area while others listen. We believe that query resolution based on the principles of area coverage provides a new dimension for conquering the scale of WSN. We compare IRS and k-IRS with existing query resolution techniques for unknown target location such as expanding ring search (ERS), Random walk search, and variants of Gossip search. We show by analysis, simulation, and implementation in testbed that IRS and k-IRS are highly scalable, the cost of search (total number of transmitted bytes) is independent of node density, and it is much lower than that of existing proposals under high node density.
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01 Jun 2021
TL;DR: This work analyzes normal and anomaly detection based on intrusion detection system in wireless sensor networks using gated mechanism, that is, long short‐term memory (LSTM) and gated recurrent unit (GRU) in deep learning models.
81 citations
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Cites background from "Energy Efficient and Scalable Searc..."
...Recently, the study of wireless sensor networks has become one of the most important areas of research [1–6]....
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TL;DR: This work proposes a framework for trust evaluation to identify the malicious behavior of sensor nodes and gives a localized distributed protocol, called energy and trust aware mobile agent migration (ETMAM) protocol for periodic data gathering application.
38 citations
TL;DR: Simulations show that the proposed algorithm provides a good solution for CRITICAL-SQUARE-GRID COVERAGE, and suggest that it is more practical and efficient to monitor critical areas rather than common areas.
Abstract: Wireless sensor networks are formed by connected sensors that each have the ability to collect, process, and store environmental information as well as communicate with others via inter-sensor wireless communication. These characteristics allow wireless sensor networks to be used in a wide range of applications. In many applications, such as environmental monitoring, battlefield surveillance, nuclear, biological, and chemical (NBC) attack detection, and so on, critical areas and common areas must be distinguished adequately, and it is more practical and efficient to monitor critical areas rather than common areas if the sensor field is large, or the available budget cannot provide enough sensors to fully cover the entire sensor field. This provides the motivation for the problem of deploying the minimum sensors on grid points to construct a connected wireless sensor network able to fully cover critical square grids, termed CRITICAL-SQUARE-GRID COVERAGE. In this paper, we propose an approximation algorithm for CRITICAL-SQUARE-GRID COVERAGE. Simulations show that the proposed algorithm provides a good solution for CRITICAL-SQUARE-GRID COVERAGE.
36 citations
TL;DR: The authors describe each step of the problem-solving algorithm employing efficient coding techniques focusing on improvement planning at Bit-error rsate (BER) vs signal-to-noise ratio (SNR) curves, in dense WSN, between CHs and the base station (BS) node.
Abstract: Random network coding (RNC) for Error Correction have been the subject of intense research lately. However, in wireless sensor networks (WSNs) the correlation between the received symbols, from the same cluster head (CH), provoke the column correlation on the generator matrix of array Low-Density Parity-Check (LDPC) codes, whereby many columns of this matrix do not offer any additional information, thus the most important properties are lost despite the fact that the generator matrices are more easily dispersed. In this paper, the authors describe each step of the problem-solving algorithm employing efficient coding techniques focusing on improvement planning at Bit-error rsate (BER) vs signal-to-noise ratio (SNR) curves, in dense WSN, between CHs and the base station (BS) node. They use efficient coding techniques approach based on RNC and LDPC codes using the Time Division Multiple Access (TDMA) method. They generalize the notion of RNC-LDPC, study their properties and formulate the main steps of the proposed algorithm reconstruction method. The simulation results demonstrate that this approach is more effective in improving the BER using the proposed algorithm. Moreover, subsequently ameliorate the technical properties offered by this codification type.
21 citations
References
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TL;DR: The current state of the art of sensor networks is captured in this article, where solutions are discussed under their related protocol stack layer sections.
Abstract: The advancement in wireless communications and electronics has enabled the development of low-cost sensor networks. The sensor networks can be used for various application areas (e.g., health, military, home). For different application areas, there are different technical issues that researchers are currently resolving. The current state of the art of sensor networks is captured in this article, where solutions are discussed under their related protocol stack layer sections. This article also points out the open research issues and intends to spark new interests and developments in this field.
14,048 citations
"Energy Efficient and Scalable Searc..." refers background in this paper
...A Wireless Sensor Network (WSN) [1] consists of a large number of tiny, battery-operated, possibly mobile, selfadjusting nodes with limited on-board processing, environmental sensing, and wireless communication capabilities....
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03 Nov 2004
TL;DR: B-MAC's flexibility results in better packet delivery rates, throughput, latency, and energy consumption than S-MAC, and the need for flexible protocols to effectively realize energy efficient sensor network applications is illustrated.
Abstract: We propose B-MAC, a carrier sense media access protocol for wireless sensor networks that provides a flexible interface to obtain ultra low power operation, effective collision avoidance, and high channel utilization. To achieve low power operation, B-MAC employs an adaptive preamble sampling scheme to reduce duty cycle and minimize idle listening. B-MAC supports on-the-fly reconfiguration and provides bidirectional interfaces for system services to optimize performance, whether it be for throughput, latency, or power conservation. We build an analytical model of a class of sensor network applications. We use the model to show the effect of changing B-MAC's parameters and predict the behavior of sensor network applications. By comparing B-MAC to conventional 802.11-inspired protocols, specifically SMAC, we develop an experimental characterization of B-MAC over a wide range of network conditions. We show that B-MAC's flexibility results in better packet delivery rates, throughput, latency, and energy consumption than S-MAC. By deploying a real world monitoring application with multihop networking, we validate our protocol design and model. Our results illustrate the need for flexible protocols to effectively realize energy efficient sensor network applications.
3,631 citations
"Energy Efficient and Scalable Searc..." refers methods in this paper
...The MAC protocol used is B-MAC [ 37 ], which is the default MAC protocol in TinyOS....
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TL;DR: In this article, the authors explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network analytically and experimentally and demonstrate that directed diffusion can achieve significant energy savings and can outperform idealized traditional schemes under the investigated scenarios.
Abstract: Advances in processor, memory, and radio technology will enable small and cheap nodes capable of sensing, communication, and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. In this paper, we explore the directed-diffusion paradigm for such coordination. Directed diffusion is data-centric in that all communication is for named data. All nodes in a directed-diffusion-based network are application aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network (e.g., data aggregation). We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network analytically and experimentally. Our evaluation indicates that directed diffusion can achieve significant energy savings and can outperform idealized traditional schemes (e.g., omniscient multicast) under the investigated scenarios.
2,550 citations
TL;DR: The authors' sensor-network application for volcanic data collection relies on triggered event detection and reliable data retrieval to meet bandwidth and data-quality demands.
Abstract: Augmenting heavy and power-hungry data collection equipment with lighten smaller wireless sensor network nodes leads to faster, larger deployments. Arrays comprising dozens of wireless sensor nodes are now possible, allowing scientific studies that aren't feasible with traditional instrumentation. Designing sensor networks to support volcanic studies requires addressing the high data rates and high data fidelity these studies demand. The authors' sensor-network application for volcanic data collection relies on triggered event detection and reliable data retrieval to meet bandwidth and data-quality demands.
1,306 citations
"Energy Efficient and Scalable Searc..." refers background in this paper
...The usage of election algorithms for reducing the redundant storage of sensed events can be further emphasized by the following facts: measurement flash of Crossbow MicaZ mote is 512 Kbytes [11], flash memory of Moteiv Tmote Sky is 1024 Kbytes [12], and TMote Sky’s flash memory fills in roughly 20 minutes when recording two channels of data at 100 Hz [13]....
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03 Nov 2004
TL;DR: A scalable simulation environment for wireless sensor networks that provides an accurate, per-node estimate of power consumption and employs a novel code-transformation technique to estimate the number of CPU cycles executed by each node, eliminating the need for expensive instruction-level simulation of sensor nodes.
Abstract: Developing sensor network applications demands a new set of tools to aid programmers. A number of simulation environments have been developed that provide varying degrees of scalability, realism, and detail for understanding the behavior of sensor networks. To date, however, none of these tools have addressed one of the most important aspects of sensor application design: that of power consumption. While simple approximations of overall power usage can be derived from estimates of node duty cycle and communication rates, these techniques often fail to capture the detailed, low-level energy requirements of the CPU, radio, sensors, and other peripherals.In this paper, we present, a scalable simulation environment for wireless sensor networks that provides an accurate, per-node estimate of power consumption. PowerTOSSIM is an extension to TOSSIM, an event-driven simulation environment for TinyOS applications. In PowerTOSSIM, TinyOS components corresponding to specific hardware peripherals (such as the radio, EEPROM, LEDs, and so forth) are instrumented to obtain a trace of each device's activity during the simulation runPowerTOSSIM employs a novel code-transformation technique to estimate the number of CPU cycles executed by each node, eliminating the need for expensive instruction-level simulation of sensor nodes. PowerTOSSIM includes a detailed model of hardware energy consumption based on the Mica2 sensor node platform. Through instrumentation of actual sensor nodes, we demonstrate that PowerTOSSIM provides accurate estimation of power consumption for a range of applications and scales to support very large simulations.
1,174 citations
"Energy Efficient and Scalable Searc..." refers methods in this paper
...We consider an energy model based on the power model of Mica2 mote [33] where the current consumption for reception is 7....
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