Traditional Wireless Sensor Networks Research Articles

Efficient Energy Utilization with Device Placement and Scheduling in the Internet of Things

The low‐power wide‐area network (LPWAN) technologies, such as LoRa, Sigfox, and NB‐IoT, bring new renovation to the wireless communication between end devices in the Internet of things (IoT), which can provide larger coverage and support a large number of IoT devices to connect to the Internet with few gateways. Based on these technologies, we can directly deploy IoT devices on the candidate locations to cover targets or the detection area without considering multihop data transmission to the base station like the traditional wireless sensor networks. In this paper, we investigate the problems of the minimum energy consumption of IoT devices for target coverage through placement and scheduling (MTPS) and minimum energy consumption of IoT devices for area coverage through placement and scheduling (MAPS). In the problems, we consider both the placement and scheduling of IoT devices to monitor all targets (or the whole detection area) such that all targets (or the whole area) are (or is) continuously observed for a certain period of time. The objectives of the problems are to minimize the total energy consumption of the IoT devices. We first, respectively, propose the mathematical models for the MTPS and MAPS problems and prove that they are NP‐hard. Then, we study two subproblems of the MTPS problem, minimum location coverage (MLC), and minimum energy consumption scheduling deployment (MESD) and propose an approximation algorithm for each of them. Based on these two subproblems, we propose an approximation algorithm for the MTPS problem. After that, we investigate the minimum location area coverage (MLAC) problem and propose an algorithm for it. Based on the MLAC and MESD problems, we propose an approximation algorithm to solve the MAPS problem. Finally, extensive simulation results are given to further verify the performance of the proposed algorithms.

Metaheuristic Clustering Protocol for Healthcare Data Collection in Mobile Wireless Multimedia Sensor Networks

Nowadays, healthcare applications necessitate maximum volume of medical data to be fed to help the physicians, academicians, pathologists, doctors and other healthcare professionals. Advancements in the domain of Wireless Sensor Networks (WSN) and Multimedia Wireless Sensor Networks (MWSN) are tremendous. M-WMSN is an advanced form of conventional Wireless Sensor Networks (WSN) to networks that use multimedia devices. When compared with traditional WSN, the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content. Hence, clustering techniques are deployed to achieve low amount of energy utilization. The current research work aims at introducing a new Density Based Clustering (DBC) technique to achieve energy efficiency in WMSN. The DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level, distance, and node centrality. In addition, two static data collector points called Super Cluster Head (SCH) are placed, which collects the data from normal CHs and forwards it to the Base Station (BS) directly. SCH supports multi-hop data transmission that assists in effectively balancing the available energy. A detailed simulation analysis was conducted to showcase the superior performance of DBC technique and the results were examined under diverse aspects. The simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds, which is significantly higher compared to existing methods.

Integration of Steerable Smart Antennas to IETF 6TiSCH Protocol for High Reliability Wireless IoT Networks

Steerable directional antennas are increasingly utilised to improve the overall performance of the traditional wireless sensor networks. Steerable directional antenna based networking solutions increase the network capacity by providing a longer range of transmission and reduced interference as compared to networking solutions with omni-directional antennas. However, the use of smart antennas requires complex algorithms and such algorithms may not be easily leveraged in low power Internet of Things (IoT) networks. This study presents mechanisms for integrating low complexity smart antenna solutions into IETF 6TiSCH protocol with the aim of creating scalable and reliable industrial IoT networks. The solution defines extensions to MAC layer and scheduling mechanisms of IETF 6TiSCH protocol to enable its seamless integration with low complexity steerable smart antennas. The results of this study show that smart antenna enabled 6TiSCH protocol stack outperforms the legacy 6TiSCH stack in terms of data delivery performance especially in high density scenarios.

The Practice of Political Online Teaching of Wireless Sensor Network Course based on ZIGBEE

ZIGBEE technology is based on mature communication standards, and its technology application field is more and more extensive, such as intelligent home appliances, medical health, military reconnaissance, environmental monitoring and other aspects have great development space. With the development of science and technology, information technology, represented by multimedia and network technology, has been widely used in education field. The use of information technology has attracted more and more attention of teachers and students. But the current situation is not ideal for the deep integration of information technology and political teaching. The purpose of this paper is to study the application of wireless sensor network in the political online classroom, to increase the integration of information technology and political education, and to improve the interest of students in political learning. This paper, through the relevant theoretical research, considers the application of WLAN in political classroom, analyzes its advantages and makes teaching process. The shortcomings of traditional wireless sensor network are analyzed and the optimization algorithm is proposed to save energy. In the environment of wireless sensor network deployment, an online teaching platform is built. This paper uses SSH framework technology to realize the functions of the system, and gives the specific introduction of the realization of each function module in the system. Through the system debugging, deployment and implementation, the system successfully completed the course deployment and implementation, which strongly supports the smooth progress of the flipped classroom teaching reform. Finally, through questionnaire and interview, teachers and students have a good attitude towards online teaching platform, the highest satisfaction of the system stability is 100%, 86.67% of the teachers are satisfied with the ease of use of the system operation.

Quantum Clone Elite Genetic Algorithm‐Based Evaluation Mechanism for Maximizing Network Efficiency in Soil Moisture Wireless Sensor Networks

In agriculture, soil moisture wireless sensor networks (SMWSNs) are used to monitor the growth of crops for obtaining higher yields. The purpose of this paper is to improve the network efficiency of SMWSNs. Therefore, we propose a novel network efficiency evaluation mechanism which is suitable for soil moisture sensors and design a sensor target allocation model (STAM) for the actual agricultural situation. After that, a quantum clone elite genetic algorithm (QCEGA) is proposed; then, QCEGA is applied to optimize the STAM for obtaining optimal results. QCEGA uses the parallel mechanism of quantum computing to encode individuals, integrates the quantum revolving gate in quantum computing and the concept of cloning in biology to avoid the algorithm from falling into local optimum, and applies the elite strategy to speed up the convergence of the algorithm. Subsequently, the proposed algorithm is compared with simulated annealing (SA) and particle swarm optimization (PSO). Under the novel network efficiency evaluation mechanism, the simulation results demonstrate that the network efficiency based on QCEGA is higher than that of SA and PSO; what is more, QCEGA has better convergence performance. In comparison with traditional wireless sensor network efficiency evaluation approaches, our methods are more in line with the development of modern agriculture and can effectively improve the efficiency of SMWSNs, thus ensuring that crops can have a better growth condition.

Distributed Query Processing in the Edge-Assisted IoT Data Monitoring System

The massive amount of data generated by the Internet-of-Things (IoT) devices places enormous pressure on sensory data query processing. Due to the limitations of computation and data transmission capabilities in traditional wireless sensor networks (WSNs), the current query processing methods are no longer effective. Furthermore, processing vast amount of sensory data also overloads the cloud. To address these problems, we investigate query processing in an edge-assisted IoT data monitoring system (EDMS). Multiaccess edge computing (MEC) is an emerging topic in IoTs. Unlike WSNs, the edge servers in an EDMS can deploy the computation and storage resources to nearby IoT devices and offer data processing services. Therefore, queries toward massive sensory data can be processed in an EDMS in a distributed manner and the edge servers can handle the sensory data in a distributed manner, reducing the workload of the cloud. In this article, we define a query processing problem in an EDMS, which aims to derive a distributed query plan with the minimum query response latency. We prove that this problem is NP-Hard and propose a corresponding approximation algorithm. The performance of the proposed algorithm is bounded. Furthermore, we evaluate the performance of the proposed algorithm through extensive simulations.

Design and implementation of low power bio signal sensors for wireless body sensing network applications

Continuous human surveillance alone will be useful for elderly people to realize the highest standard of living in human society. However, it is difficult to get such a setting as long as it can operate long hours without maintenances of its battery limitation. Biosensor signals, which are referred to as a device that consumes a lot of power in the system of a Microcontroller Unit (MCU), must be made continuously to signal samples of the biosensor. To decrease the power utilization of the Microcontroller Unit, and a low power application specific integrated circuit for biosensors feature extraction is designed for the replacement of a role of the microcontroller unit to extract the characteristic values of bio signal. In this investigation, an Application-Specific Integrated Circuit (ASIC) of Wireless Body Sensor Networks (WBSNs) is intended to fill in as a convention for medical use. The proposed wireless body sensor network comprise an newly designed sensor interfaces, integrated passive Radio Frequency (RF) receiver, contact ports, low-power Microcontroller Unit (MCU), wireless transistor control, a Power Management Unit (PMU), and an with low power harvesting capacity contains. The proposed method has been designed using 90 nm and 45 nm CMOS technology. Simulation results exhibit up to the change in noise immunity and lessening in power utilization contrasted with the traditional wireless body sensor networks design at a similar delay.

Multilayered Network Model for Mobile Network Infrastructure Disruption.

In this paper, the novel study of the multilayered network model for the disrupted infrastructure of the 5G mobile network is introduced. The aim of this study is to present the new way of incorporating different types of networks, such as Wireless Sensor Networks (WSN), Mobile Ad-Hoc Networks (MANET), and DRONET Networks into one fully functional multilayered network. The proposed multilayered network model also presents the resilient way to deal with infrastructure disruption due to different reasons, such as disaster scenarios or malicious actions. In the near future, new network technologies of 5G networks and the phenomenon known as the Internet of Things (IoT) will empower the functionality of different types of networks and interconnects them into one complex network. The proposed concept is oriented on resilient, smart city applications such as public safety and health and it is able to provide critical communication when fixed network infrastructure is destroyed by deploying smart sensors and unmanned aerial vehicles. The provided simulations shows that the proposed multilayered network concept is able to perform better than traditional WSN network in term of delivery time, average number of hops and data rate speed, when disruption scenario occurs.

Optimization Based Sink Deployment Technique in WSN to Improve Network Performance

Background & Objective: Currently, WSN (Wireless Sensor Networks) provides a variety of services in industrial and commercial applications. WSN consists of nodes that are used to sense the environments like humidity, temperature, pressure, sound, etc. As the use of WSN grows there are some issues like coverage, fault tolerance, a deployment problem, localization, Quality of Service, etc. which needs to be resolved. Sink deployment is a very important problem because it is not the only impact on performance, but also influence on deployment cost. In traditional WSN, a single sink is deployed in the network, which aggregates all the data. Due to this, the whole network is suffering from some serious issues like delay, congestion, network failure that reduces network performance. Methods: One solution is to deploy multiple sinks instead of a single sink. Deploying multiple sinks can improve network performance, but increases sink deployment cost. In this paper, an ISDOA (Improved Sink Deployment Optimization Algorithm) is proposed to find the optimum number of sinks and their optimum location in ROI. Simulation is carried out in Matlab simulator. The impact of sensors and sinks on various network performance parameters like throughput, network lifetime, packet delivery ratio, energy consumption and cost of the network is analyzed. Results & Conclusion: It is shown by simulation results that the number of sinks varies inversely with energy consumption of the nodes; and it is linearly proportional to the network lifetime, throughput and packet delivery ratio. Furthermore, results show that the proposed approach outperforms random deployment with 25% higher throughput, 30% better network lifetime, 15% lesser energy consumption and 21% optimized cost of the network, respectively.

A Novel Framework for the Coverage Problem in Battery-Free Wireless Sensor Networks

Battery-free wireless sensor network (BF-WSN) is a newly proposed network architecture to address the limitation of traditional wireless sensor networks (WSNs). The special features of BF-WSNs make the coverage problem quite different and even more challenging from and than that in traditional WSNs. This paper defines a new coverage problem in BF-WSNs which aims at maximizing coverage quality rather than prolonging network lifetime. The newly defined coverage problem is proved to be at least NP-Hard. Two sufficient conditions, under which the optimal solution of the problem can be derived in polynomial time, are given in this paper. Furthermore, three approximate algorithms are proposed to derive nearly optimal coverage when the sufficient conditions are unsatisfied. The time complexity and approximate ratio of the three algorithms are analyzed. Extensive simulations are carried out to examine the performance of the proposed algorithms. The simulation results show that these algorithms are efficient and effective.

A lightweight authentication and key exchange mechanism for IPv6 over low‐power wireless personal area networks‐based Internet of things

AbstractThe authentication mechanism in IPv6 over low‐power wireless personal area networks (6LoWPAN) uses media access control identifier and IPv6 address. The traditional wireless sensor network considers three‐factor authentication mechanism. These factors, namely, username, password, and biometric create a huge overhead in Internet of things due to resource‐constraint devices in terms of low memory and processing capabilities. In this article, we propose a lightweight key exchange and authentication scheme for 6LoWPAN to efficiently authenticate the resource‐constraint sensor devices. The proposed scheme uses hash‐based approaches in the setup and registration phase to reduce the computational cost and resource consumption overhead. The analytical results over the automated validation of internet security protocols and applications and ProVerif tools validate the security claim of the proposed scheme against threats like replay and man‐in‐the‐middle attack. Furthermore, we analyze the logic correctness of the proposed authentication scheme using Burrows‐Abadi‐Needham logic.

Energy-Efficient Analog Sensing for Large-Scale and High-Density Persistent Wireless Monitoring

The research challenge of current wireless sensor networks (WSNs) is to design energy-efficient, low-cost, high-accuracy, self-healing, and scalable systems for applications such as environmental monitoring. Traditional WSNs consist of low density, power-hungry digital motes that are expensive and cannot remain functional for long periods on a single power charge. In order to address these challenges, a dumb-sensing and smart-processing architecture that splits sensing and computation capabilities is proposed. Sensing is exclusively the responsibility of analog substrate-consisting of low-power, low-cost all-analog sensors-that sits beneath the traditional WSN comprising of digital nodes, which does all the processing of the sensor data received from analog sensors. A low-power and low-cost solution for substrate sensors has been proposed using analog joint source-channel coding (AJSCC) realized via the characteristics of metal-oxide-semiconductor field-effect transistor (MOSFET). Digital nodes (receiver) also estimate the source distribution at the analog sensors (transmitter) using machine learning techniques so as to find the optimal parameters of AJSCC that are communicated back to the analog sensors to adapt their sensing resolution as per the application needs. The proposed techniques have been validated via simulations from MATLAB and LTSpice to show promising performance and indeed prove that our framework can support large-scale high density and persistent WSN deployment.

Cracking Channel Hopping Sequences and Graph Routes in Industrial TSCH Networks

Industrial networks typically connect hundreds or thousands of sensors and actuators in industrial facilities, such as manufacturing plants, steel mills, and oil refineries. Although the typical industrial Internet of Things (IoT) applications operate at low data rates, they pose unique challenges because of their critical demands for reliable and real-time communication in harsh industrial environments. IEEE 802.15.4-based wireless sensor-actuator networks (WSANs) technology is appealing for use to construct industrial networks because it does not require wired infrastructure and can be manufactured inexpensively. Battery-powered wireless modules easily and inexpensively retrofit existing sensors and actuators in industrial facilities without running cables for communication and power. To address the stringent real-time and reliability requirements, WSANs made a set of unique design choices such as employing the Time-Synchronized Channel Hopping (TSCH) technology. These designs distinguish WSANs from traditional wireless sensor networks (WSNs) that require only best effort services. The function-based channel hopping used in TSCH simplifies the network operations at the cost of security. Our study shows that an attacker can reverse engineer the channel hopping sequences and graph routes by silently observing the transmission activities and put the network in danger of selective jamming attacks. The cracked knowledge on the channel hopping sequences and graph routes is an important prerequisite for launching selective jamming attacks to TSCH networks. To our knowledge, this article represents the first systematic study that investigates the security vulnerability of TSCH channel hopping and graph routing under realistic settings. In this article, we demonstrate the cracking process, present two case studies using publicly accessible implementations (developed for Orchestra and WirelessHART), and provide a set of insights.

Visual node module: An open‐source extension to the ns‐3 network simulator

SummaryA wireless visual sensor network is a collective network of directional and battery‐operated sensor nodes equipped with cameras. The field of view of these nodes depends on the camera opening angle, its direction, and its depth of view. Therefore, coverage and object detection in this type of networks are more challenging compared with the traditional wireless sensor networks. Thus, many researchers propose algorithms and solutions in this field that need tests and simulations. In this paper, we focus on network simulator 3 (ns‐3), which is an open‐source and discrete‐event tool suitable for wireless network simulation targeted primarily for research and educational use. The lack of models that can simulate visual sensor nodes in this simulator motivated us to design and develop a new visual node module as an extension of the ns‐3 core libraries and also to adapt the NetAnim tool to present these nodes graphically. This module will help researchers to simulate, test, and visualize their solutions in wireless visual sensor networks field. In this paper, we present the design and implementation of the proposed module. Furthermore, we show how it can be used in ns‐3 to simulate different scenarios of object detection and visualize the results in NetAnim tool.

Routing Protocols for UAV-Aided Wireless Sensor Networks

Recently, unmanned aerial vehicles (UAVs) attracted significant popularity in both military and civilian domains for various applications and services. Moreover, UAV-aided wireless sensor networks (UAWSNs) became one of the interesting hot research topics. This is mainly because UAWSNs can significantly increase the network coverage and energy utilization compared to traditional wireless sensor networks (WSNs). However, the high mobility, dynamic path, and variable altitude of UAVs can cause not only unforeseen changes in the network topology but also connectivity and coverage problems, which can affect the routing performance of the network. Therefore, the design of a routing protocol for UAWSNs is a critical task. In this paper, the routing protocols for UAWSNs are extensively investigated and discussed. Firstly, we classify the existing routing protocols based on different network criteria. They are extensively reviewed and compared with each other in terms of advantages and limitation, routing metrics and policies, characteristics, difference performance factors, and different performance optimization factors. Furthermore, open research issues and challenges are summarized and discussed.

Connectivity on Underwater MI-Assisted Acoustic Cooperative MIMO Networks.

In traditional underwater wireless sensor networks (UWSNs), it is difficult to establish reliable communication links as the acoustic wave experiences severe multipath effect, channel fading, and ambient noise. Recently, with the assistance of magnetic induction (MI) technique, cooperative multi-input-multi-output (MIMO) is utilized in UWSNs to enable the reliable long range underwater communication. Compared with the acoustic-based UWSNs, the UWSNs adopting MI-assisted acoustic cooperative MIMO are referred to as heterogeneous UWSNs, which are able to significantly improve the effective cover space and network throughput. Due to the complex channel characteristics and the heterogeneous architecture, the connectivity of underwater MI-assisted acoustic cooperative MIMO networks is much more complicated than that of acoustic-based UWSNs. In this paper, a mathematical model is proposed to analyze the connectivity of the networks, which considers the effects of channel characteristics, system parameters, and synchronization errors. The lower and upper bounds of the connectivity probability are also derived, which provide guidelines for the design and deployment of underwater MI-assisted acoustic cooperative MIMO networks. Monte Carlo simulations were performed, and the results validate the accuracy of the proposed model.

SenseCrypt: A Security Framework for Mobile Crowd Sensing Applications.

The proliferation of mobile devices such as smartphones and tablets with embedded sensors and communication features has led to the introduction of a novel sensing paradigm called mobile crowd sensing. Despite its opportunities and advantages over traditional wireless sensor networks, mobile crowd sensing still faces security and privacy issues, among other challenges. Specifically, the security and privacy of sensitive location information of users remain lingering issues, considering the “on” and “off” state of global positioning system sensor in smartphones. To address this problem, this paper proposes “SenseCrypt”, a framework that automatically annotates and signcrypts sensitive location information of mobile crowd sensing users. The framework relies on K-means algorithm and a certificateless aggregate signcryption scheme (CLASC). It incorporates spatial coding as the data compression technique and message query telemetry transport as the messaging protocol. Results presented in this paper show that the proposed framework incurs low computational cost and communication overhead. Also, the framework is robust against privileged insider attack, replay and forgery attacks. Confidentiality, integrity and non-repudiation are security services offered by the proposed framework.

An Effective Edge-Assisted Data Collection Approach for Critical Events in the SDWSN-Based Agricultural Internet of Things

In the traditional agricultural wireless sensor networks (WSNs), there is a large amount of redundant data and high latency on critical events (CEs) for data collection systems, which increases the time and energy consumption. In order to overcome these problems, an effective edge computing (EC) enabled data collection approach for CE in smart agriculture is proposed. First, the key features data types (KFDTs) are extracted from the historical dataset to keep the main information on CEs. Next, the KFDTs are selected as the collection data type of the software-defined wireless sensor network (SDWSN). Then, the event types are decided by searching the minimum average variance between the sensing data of active nodes and the average value of the key feature data obtained by EC. Furthermore, the sensing nodes are driven to sense the event-related data with a consideration of latency constraints by the SDWSN servers. A real-world testbed was set up in a smart greenhouse for experimental verification of the proposed approach. The results showed that the proposed approach could reduce the number of needed sensors, sensing time, collection data volume, communication time, and provide the low latency agricultural data collection system. Thus, the proposed approach can improve the efficiency of CE sensing in smart agriculture.

Novel Security Mechanism against D D o S Attack in Mobile Wireless Sensor Network

In modern time, Mobile Wireless Sensor Networks (MWSN) technology has gained tremendous popularity and is widely used in various applications. MWSN is a temporary network designed for a specific purpose. The dynamic nature of these networks makes them more usable in different fields. The autonomous systems of these wireless mobile nodes can be established anytime, anywhere. However, due to its high mobility, lack of centralized authority and the nature of open media, MWSN is more vulnerable to a variety of security threats and hence more vulnerable to security issues than traditional Wireless Sensor Networks (WSN). MWNs are extremely vulnerable to various types of threats, which will greatly reduce network performance in different situations. In this article, we first provide the general description about MWSN along with its component used and then reviewed the well-known work done by various researchers to detect distributed denial-of-service attack (DDoS). The review thoroughly identified different aspects of the proposed methodology. In addition, we also provide comparative analysis of various analyzed parameters that are examined to determine the effectiveness of the designed protection network against DDoS attack.

Using Machine Learning Methods to Provision Virtual Sensors in Sensor-Cloud.

The advent of sensor-cloud technology alleviates the limitations of traditional wireless sensor networks (WSNs) in terms of energy, storage, and computing, which has tremendous potential in various agricultural internet of things (IoT) applications. In the sensor-cloud environment, virtual sensor provisioning is an essential task. It chooses physical sensors to create virtual sensors in response to the users’ requests. Considering the capricious meteorological environment of the outdoors, this paper presents an measurements similarity-based virtual-sensor provisioning scheme by taking advantage of machine learning in data analysis. First, to distinguish the changing trends, we classified all the physical sensors into several categories using historical data. Then, the k-means clustering algorithm was exploited for each class to cluster the physical sensors with high similarity. Finally, one representative physical sensor from each cluster was selected to create the corresponding virtual sensors. The experimental results show the reformation of our scheme with respect to energy efficiency, network lifetime, and data accuracy compared with the benchmark schemes.