Linear Sensor Networks Research Articles

AUV-Based Efficient Data Collection Scheme for Underwater Linear Sensor Networks

The research on underwater wireless sensor networks (UWSNs) has grown considerably in recent years where the main focus remains to develop a reliable communication protocol to overcome its challenges between various underwater sensing devices. The main purpose of UWSNs is to provide a low cost and an unmanned data collection system for a range of applications such as offshore exploration, pollution monitoring, oil and gas pipeline monitoring, surveillance, etc. One of the common types of UWSNs is linear sensor network (LSN), which speciall targets monitoring the underwater oil and gas pipelines. Under this application, in most of the previously proposed works, networks are deployed without considering the heterogeneity and capacity of the various sensor nodes. This negligence leads to the problem of inefficient data delivery from the sensor nodes deployed on the pipeline to the surface sinks. In addition, the existing path planning algorithms do not consider the network coverage of heterogeneous sensor nodes.

ELSA: Energy-Efficient Linear Sensor Architecture for Smart City Applications

A network of sensors and surveillance devices creates a critical infrastructure in smart cities, enabling civic authorities to render faster and more efficient services. Notably, many deployed sensor applications form linear topologies, such as road monitoring sensors (e.g., intelligent lighting, road traffic, pedestrians, etc.), railway track sensors, and pipeline sensors (e.g., sensing leaks, pressure, etc.). These linear sensors convey data to a central control station by daisy-chaining through neighbor sensors. However, it causes critical energy inefficiency and imbalance issues and creates data transmission delay, which provokes complex application and service management hassles. In this paper, we propose an Energy-Efficient Linear Sensor Architecture (ELSA) for building sensor network infrastructure of Smart Cities. We design and develop an energy-efficient Wake-up Receiving (WuRx) method on sensor nodes by using a dual-channel, an infrared (IR) based WuRx channel over a WiFi data channel to enhance the duty cycle efficiency. In addition, we build a pipelined relay algorithm to avoid any network delay performance degradation. Furthermore, we design a centrality-based algorithm to identify linear sensor networks. The experimental results show that IR WuRx ELSA performed nearly ten times better energy efficiency per node and achieved a constant network delay regardless of the number of relaying nodes in a network.

A Scalable and Energy-Efficient MAC Protocol for Linear Sensor Networks

Linear topologies arise naturally in the context of Internet-of-Things (IoT) applications for smart cities, where the infrastructure itself commonly has a linear or semi-linear structure. This is the case of buildings, public transportation systems, road infrastructure, and utility distribution networks. Given the prevalence of this type of topologies, several Medium Access Control (MAC) protocols have been designed to take advantage of their particular properties. Unfortunately, most of them do not scale well as the node density and the distance in hops to the sink increases. The result is that packets generated many hops away from the sink tend to experience unacceptable high end-to-end delay and low delivery probabilities. This paper introduces HP-MAC, a synchronized duty-cycled MAC protocol for Linear Sensor Networks (LSNs) that assigns transmission priorities to nodes to avoid collisions, through the implementation of distributed elections based on hash functions. HP-MAC also implements a packet queuing scheme that acts as a mechanism to control the amount of network resources allocated to data flows generated at different distances to the sink. This way, packets can reach their destination with loss probability and end-to-end delay that do not depend on their distance to the sink. We use a Discrete-Time Markov Chain (DTMC) to model the performance of the proposed protocol. Numerical solutions of this model show that HP-MAC outperforms state-of-the-art representatives in terms of throughput, end-to-end delay, power consumption, and packet loss probability. These results are validated through extensive discrete-event simulations.

Distributed Algorithms for Multiple Path Backbone Discovery in Thick Linear Sensor Networks

Continued advancements in microprocessors, electronics, and communication technology have led to the design and development of sensing devices with increased functionalities, smaller sizes, larger processing, storage, and communication capabilities, and decreased cost. A large number of these sensor nodes are used in many environmental, infrastructure, commercial, and military monitoring applications. Due to the linearity of a good number of the monitored structures such as oil, gas, and water pipelines, borders, rivers, and roads, the wireless sensor networks (WSNs) that are used to monitor them have a linear topology. This type of WSN is called a linear sensor network (LSN). In this paper, two distributed algorithms for topology discovery in thick LSNs are presented: the linear backbone discovery algorithm (LBD) and the linear backbone discovery algorithm with x backbone paths (LBDx). Both of them try to construct a linear backbone for efficient routing in LSNs. However, the LBD algorithm has the objective of minimizing the number of messages used during the backbone discovery process. On the other hand, the LBDx algorithm focuses on reducing the number of hops of the data messages transmitted from the nodes to the sink. LBD and LBDx exhibit good properties and efficient performance, which are confirmed by extensive simulations.

MicroSTARLING: A Millimeter Scale Self-Organizing Optical Communication Processor for Embedded Multihop Sensor and Computational Networks

In the development of small scale, networked nodes, design inspiration can be found in the decentralized and simplistic communication used by starlings in flight. Here, we present a novel node processor based on simple nearest neighbor connections to yield a self-organized linear optical sensor network. We present the processor state machine design and evaluation of the Verilog code. Each node, termed a MicroSTARLING, contains a processor fabricated using 180nm CMOS technology. The chips are 1.2mm ×1.2mm and the digital processor occupies 0.063 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of the chip. We assemble three test bench nodes to evaluate the processor function in wired, wireless communication and fully wireless circuits. When nodes are directly wired, the MicroSTARLING processor functions with a 25MHz input clock enabling a linear multihop network readout of 8 devices in 1.8ms. With a 20 μW, 524KHz input clock, the processor requires 12 μW at 1.2V. We evaluate the processor function under optical communication obtaining 6-hops between 7 nodes with little noticeable reduction in communication quality. Our fully wireless node required 3mW of power harvested via a 940nm light emitting diode (LED) and communicated via separate LEDs. To our knowledge this is the first demonstration of more than 3 hops of linear wireless communication with a mm scale self-organizing node processor. The processor size and power make possible the concept of smart dust networks with multihop communication between nodes for an embedded wireless sensor network in biological tissue structures or as the foundation of an optical neural network for edge computing.

A Parallel Filtering-Communication-Based Cardinality Consensus Approach for Real-Time Distributed PHD Filtering

This paper proposes a new cardinality consensus (CC) approach called “prior-CC” to distributed probability hypothesis density (PHD) filtering based on a decentralized sensor network. In our approach, network-wide average consensus is sought with respect to the prior cardinality estimate. Unlike existing serial filtering-communication approaches, the prior-CC scheme allows the internode communication to be performed in parallel with the local filter calculation and requires only a small amount of interfacing fusion calculation and communication. This enables real time filtering that minimizes data delay and is of great significance in realistic tracking systems. We provide details of the Gaussian mixture implementation of the proposed prior-CC-PHD filter based on a diffuse target birth model and analyze the filtering-communication parallelization. In addition, we evaluate the gain of the prior-CC scheme with respect to the filtering accuracy in comparison with the standard CC scheme via simulations using a stationary linear sensor network and a dynamic nonlinear sensor network, respectively.

RDCPF: A Redundancy-Based Duty-Cycling Pipelined-Forwarding MAC for Linear Sensor Networks.

Existing duty-cycling and pipelined-forwarding (DCPF) protocols applied in battery-powered wireless sensor networks can significantly alleviate the sleep latency issue and save the energy of networks. However, when a DCPF protocol applies to a linear sensor network (LSN), it lacks the ability to handle the bottleneck issue called the energy-hole problem, which is mainly manifested due to the excessive energy consumption of nodes near the sink node. Without overcoming this issue, the lifespan of the network could be greatly reduced. To that end, this paper proposes a method of deploying redundant nodes in LSN, and a corresponding enhanced DCPF protocol called redundancy-based DCPF (RDCPF) to support the new topology of LSN. In RDCPF, the distribution of energy consumption of the whole network becomes much more even. RDCPF also brings improvements to the network in terms of network survival time, packet delivery latency, and energy efficiency, which have been shown through the extensive simulations in comparison with existing DCPF protocols.

A Framework of Linear Sensor Networks with Unmanned Aerial Vehicle for Rainfall-Induced Landslides Detection

This paper proposes a real-time monitoring framework for a landslide susceptibility area based on wireless sensor network using multiple Unmanned Aerial Vehicles (UAVs). Many researchers have considered building a landslide susceptibility map to distinguish different levels of landslide susceptible zones. However, to prevent damage from landslides, it is more important for the disaster control center to identify the time and location of the landslide occurrence in those highly susceptible areas. Hence, a rain-triggered landslide monitoring system is proposed herein for local mountain areas. First, a wireless sensor network framework is constructed to inform the control center as immediately as possible when landslides occur. Second, multiple UAV sensors will be responsible for collecting the stereo images of the slope in highly sensitive zones on schedule. Based on the stereo images and the binocular model, in-depth information can be obtained. With the depth information and Speeded Up Robust Features (SURF) detection, the key point characteristic information is constructed as the input data for Support Vector Machine (SVM). An SVM algorithm is designed with Python program language and executed in real time. Using this algorithm, the real-time images collected by UAVs and the landslide warning information will be sent to the control center for further analysis. Finally, a field experiment is conducted to demonstrate the effectiveness of the proposed method.

A LoRa-Based Linear Sensor Network for Location Data in Underground Mining

In this paper, we describe a LoRa (from “Long Range”)-based, linear sensor network we have developed for transmitting location information of personnel and equipment in an underground mine. The system is intended to be used during emergencies when existing communications infrastructure has failed. Linear networks comprise a sequence of relays that forward data to a common destination, the headend. Relays forward location information transmitted from tags carried by personnel or equipment. Relays will usually be put in place as investigators or rescuers enter the mine. LoRa is used both by the tags to communicate to the relays and by the relays to forward messages to the headend. We have implemented and tested this system, and have carried out simulations and analyses to determine its scalability, reliability and fairness. The need for robustness and reliability has led us to use flooding rather than unicast communication. We also use message sequence numbers and time-to-live fields to prevent broadcast storms. Contention is managed using a simplified Carrier Sense Multiple Access (CSMA) scheme. We also address fairness. When the network is under load messages may be dropped by relays making messages generated more hops from the headend more likely to be dropped than messages nearer the headend. We explore the relationship between unfairness, traffic load and number of relays. We also observe that a network of larger numbers of lightly loaded relays performs more effectively than smaller numbers of heavily loaded relays.

Markovian model analysis for energy harvesting nodes in a modified opportunistic routing protocol

ABSTRACT Opportunistic routing protocol with cross-layer design mainly focuses on energy-harvesting principles and is named as modified energy-harvesting opportunistic routing protocol (MEHOR). In the cross-layer design, we follow the Markovian model where the parameters are exchanged between the different layers in order to optimise the usage of energy. MEHOR focuses on energy constraints at a sensor node because it requires recharge, once its energy level gets depleted. Basically, we put emphasis on regioning in order to overcome the multi-path routing problems, especially the energy availability. We minimise retransmission rate (12%), cost optimisation and reduce collision (8%) considering data packets received in a linear multi-hop energy-harvesting wireless sensor network (EH-WSN). Finally, we compare MEHOR with other advanced routing protocols like opportunistic routing (OR) and energy-harvesting opportunistic routing (EHOR).

Interference‐aware clustering approach improving QoS for linear WSNs using a token‐based MAC protocol

SummaryWireless sensor networks (WSNs) have received a lot of attention from both academia and industry due to the increasing need for ubiquitous computing for monitoring applications, the continuous advances in miniaturization of electronic devices, and the ultra‐low‐power wireless technologies. These innovations in technology have driven the curiosity to use sensor networks in a new kind of applications such as road track or railway monitoring, border monitoring, oil and gas, or even water pipeline monitoring. Due to the underlying linear topology of these applications, a new type of network, called a linear sensor network (LSN), has emerged. Because of the specific characteristics of this application and the resource constraints of sensors, some of the major challenges faced in LSNs are to reduce end‐to‐end delays, to maximize the packet delivery ratio to a sink, and an even distribution of the load between nodes. To achieve these objectives, it is necessary to control node‐to‐node packet traffic conditions and to manage radio interference created by simultaneously active nodes. This paper addresses these challenges and proposes a new method of clustering LSNs that reduces or controls radio interference risks in order to satisfy these objectives, application needs, and the resource limitations of sensor nodes in the best possible way. This method is applied for LSNs using a token‐passing mechanism to access the medium. The performance evaluation is conducted by using a realistic propagation model in the analytical evaluation and also a NS‐2 simulation process.

Performance Analysis of Linear Topology Wireless Sensor Network in Oil and Gas Industry

Oil and gas industry is one of the largest corporations in the world. The process and machinery involved for the petroleum product to be extracted, refined and transported are usually critical and complex. Hence, it is crucial to have a stable and reliable system to control, monitor and manage the security the industrial assets. Since Wireless Sensor Network (WSN) can sense, process and communicate, it is one of the best and popular solution to the problem existed in the oil and gas industry. In this paper, a detailed performance analysis of AODV, DSDV and FIXRT with 32 and 128 packet size is presented by using linear topology in accordance with IEEE 802.11 standards by using ns2.35 simulator. The results showed that FIXRT protocol increases the overall performance of the network by almost 30%.

Scheduling and adaptive listening approaches in MAC for WSN applications: A survey

False data detection, intrusion detection and coverage rate are major challenges in wireless sensor networks (WSN). Scheduling in media access control (MAC) using gateway and relay nodes solve some of the problems. In this paper, we carried out a brief survey on scheduling, delay aware MAC, cooperative communications in MAC, and adaptive listening to keep controlling the energy in sensors. In addition, multichannel MAC and various routing strategy techniques for WSN are discussed. Firefly algorithm for dynamic scheduling pipelined scheduling for linear sensor networks gives better throughput and latency. Heuristic configuration solves the overhearing problem, as node power-based MAC controls power of each node. Based on WSN applications and the performance metrics concentrated, various techniques for MAC are discussed throughout in this paper.

Modeling and Analysis for Data Collection in Duty-Cycled Linear Sensor Networks With Pipelined-Forwarding Feature

Due to the vast demand for monitoring a structure or area in linear topology, linear sensor networks (LSNs) have recently attracted plenty of attention. Since sensor nodes are usually battery-powered, duty-cycling techniques have been widely studied to improve energy efficiency, which, however, introduces a significant issue known as sleep latency. Thereafter pipelined forwarding has been proposed in the literature as a promising way to alleviate this issue. This paper focuses on interference analysis for data collection services in a multihop LSN running a duty-cycling and pipelined-forwarding protocol, where multiple concurrent transmissions along a data collection path can severely interfere with each other. We first obtain the nodal distance distributions associated with all concurrent transmissions. Based on the obtained distance distributions and the path-loss model in an interference-limited environment, we analyze the distributions of signal-to-interference-plus-noise ratio (SINR) and link capacity. The obtained SINR distribution indicates the link outage probability at a given SINR threshold. By investigating the transmission which receives the strongest cumulative interference, our model can provide useful guidelines for duty cycle setting to achieve a desired network performance.

Linear TDA-MAC: Unsynchronized Scheduling in Linear Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks (UASNs) are a key enabling technology for live monitoring of subsea assets. This often involves networks with line topologies, e.g., sensor nodes attached to oil and gas pipelines. In this letter, we propose linear transmit delay allocation MAC (LTDA-MAC) for efficient packet scheduling in linear UASNs without clock synchronization at the sensor nodes. It is achieved via online heuristic optimization that produces schedules tailored to a given deployment scenario. Simulations of a subsea pipeline monitoring use case show that LTDA-MAC significantly outperforms spatial-TDMA in networks with long propagation delays.

An Architecture for Using Autonomous Underwater Vehicles in Wireless Sensor Networks for Underwater Pipeline Monitoring

In this paper, we focus on the use of linear sensor networks (LSNs) to monitor underwater pipelines. We propose the use of autonomous underwater vehicles (AUVs) to move along pipeline segments and collect data from the sensor nodes (SNs). Each AUV then delivers the collected data to the surface sink at the end of the corresponding LSN segment. In turn, the surface sink transmits the collected data to the network control center using any of the communication technologies that are available in the area such as WiMAX, cellular, and satellite. The use of AUVs for data collection offers considerable advantages including flexibility in SN placement, considerable energy savings and reduced interference, hidden terminal problems, and collisions. We also provide, different AUV movement strategies and analyze the effect of various network design parameters on network performance metrics such as packet delivery ratio and end-to-end delay.

A Multi-Hop LoRa Linear Sensor Network for the Monitoring of Underground Environments: The Case of the Medieval Aqueducts in Siena, Italy

In this paper, a pervasive monitoring system to be deployed in underground environments is presented. The system has been specifically designed for the so-called “Bottini”, i.e., the medieval aqueducts dug beneath the Centre of Siena, Italy. The results of a measurement campaign carried out in the deployment scenario show that the transmission range of LoRa (Long Range) technology is limited to a maximum of 200 m, thus, making the adoption of a classical star topology impossible. Hence, a Linear Sensor Network topology is proposed based on multi-hop LoRa chain-type communications. In this scenario, an ad-hoc transmission scheme is presented that optimally evaluates the wake-up time of all nodes with the aim of minimizing the average energy dissipation deriving from clock offsets. Numerical results show that the proposed wake-up time optimization leads in the best case to a 50% reduction in power dissipation with respect to a scheme that evaluates the wake-up time in a non-optimal way.

Gesture Recognition With the Linear Optical Sensor and Recurrent Neural Networks

In this paper, the optical linear sensor, a representative of low-resolution sensors, was investigated in the multiclass recognition of near-field hand gestures. The recurrent neural network (RNN) with a gated recurrent unit (GRU) memory cell was utilized as a gestures classifier. A set of 27 gestures was collected from a group of volunteers. The 27 000 sequences obtained were divided into training, validation, and test subsets. The primary research goal was to define the most appropriate model architecture in terms of the accurate recognition of each gesture. An additional aim of the research was to investigate the kind of input data, i.e., raw data or preprocessed (feature) data, which generally produces better results. Therefore, three datasets were generated: raw data, simple features data, and high-level features data. (This includes information about hand poses which are already recognized.) The random search method was applied to achieve hyperparameter optimization to find the best possible topology for the neural network. The analysis performed shows that selected models were characterized by a test score at a level of 96.89% for the raw data, 95.75% for simple features, and 93.38% for high-level features. Results indicate that the direct use of raw data obtained from the optical linear sensor evaluated on the RNN with GRU memory cells allows for the reliable recognition of even complex gestures. Therefore, such solutions may have the potential to serve as a support to or as an alternative to video-based sensors especially for mobile devices.

SimpliMote: A Wireless Sensor Network Monitoring Platform for Oil and Gas Pipelines

Wireless sensor networks have extensively been utilized over the years for ambient data collection from diverse structural deployments including mesh, ad hoc , and hierarchical layouts. Several other applications of sensor networks may involve placing the nodes in a linear topology, constituting a special class of networks called linear sensor networks. In a densely deployed linear network case, issues related to optimal resource allocation and networking may persist because the standard sensor network protocols attempt to manage the network as a mesh or ad hoc infrastructure. Issues like recovering from holes where a node cannot reach another node in either side or policies to establish a route for data dissemination need to be intelligently solved for linear sensor networks. To solve such issues, we propose a linear sensor network deployment application for oil and gas pipeline using a custom sensor board accompanied with algorithms to solve network creation, leak interrupt detection, and routing of high-priority messages with reliability while keeping network alive at all times. The proposed system provides all the features of leakage detection, localization, parameter sensing, and actuation, while operating at low energy, high data reliability, and low latencies, while comparative results prove the efficacy of the system.

A Selective‐Awakening MAC Protocol for Energy‐Efficient Data Forwarding in Linear Sensor Networks

We introduce the Selective-Awakening MAC (SA‐MAC) protocol which is a synchronized duty‐cycled protocol with pipelined scheduling for Linear Sensor Networks (LSNs). In the proposed protocol, nodes selectively awake depending on node density and traffic load conditions and on the state of the buffers of the receiving nodes. In order to characterize the performance of the proposed protocol, we present a Discrete‐Time Markov Chain‐based analysis that is validated through extensive discrete‐event simulations. Our results show that SA‐MAC significantly outperforms previous proposals in terms of energy consumption, throughput, and packet loss probability. This is particularly true under high node density and high traffic load conditions, which are expected to be common scenarios in the context of IoT applications. We also present an analysis by grade (i.e., the number of hops to the sink, which is located at one end of the LSN) that reveals that LSNs exhibit heterogeneous performance depending on the nodes’ grade. Such results can be used as a design guideline for future LSN implementations.