Framework For Wireless Sensor Networks Research Articles

Compact and Wideband PIFA Design for Wireless Body Area Sensor Networks

The specific advantage of fractal geometry to realize compact antenna features is exploited in this work for the design of a miniaturized Planar Inverted-F Antenna configuration with a large bandwidth. The conventional quadrilateral radiating element of a Planar Inverted-F Antenna is replaced by a Minkowski pre-fractal-based shape, thus increasing the resonant wavelength without affecting the overall antenna dimensions. Consequently, with the new design, a physically smaller antenna can achieve the same resonant frequency of a larger conventional configuration. Measured as well as simulated reflection coefficient and radiation patterns are presented to validate the assumptions. The impedance bandwidth of the antenna (2.19 to 2.52 GHz) covers the ISM band with a boresight gain of 1.5–2 dB over the entire band. Furthermore, to demonstrate the miniaturization effect, a successful comparison is provided with an identically sized, conventional square Planar Inverted-F Antenna design. The proposed antenna design can be usefully adopted for power-efficient communications in the framework of Wireless Body Area Sensor Networks.

A Hierarchical Energy Conservation Framework (HECF) of Wireless Sensor Networks by Temporal Association Rule Mining for Smart Buildings

The challenge of extending the sensor’s energy consumption is a key research issue in Wireless Sensor Networks (WSNs). Recently, association rule mining has proven to be a potential candidate to prolong the lifetime of sensor nodes in WSN. However, temporal correlations of the contextual values are not taken into account which is useful for the sensors to conserve their energy. Similarly, association rules mining at different tiers of the network has not been considered to reduce the number of transmission messages, by avoiding redundant data which is the major cause of the energy drain of sensors. In this paper, a novel Hierarchical Energy Conservation Framework (HECF) is proposed which aims to conserver’s energy at each layer of a network by using the Hierarchical Temporal Association Rule Mining in multistory buildings. In hierarchal setup, each floor of the building can conserve energy locally at the local sink and conserve entire network energy at the global sink by using temporal association rule mining at different tiers of the network. The HECF is ideal for large multistory buildings where energy conservation is a major issue along with effective monitoring and system performance. The result shows that HECF outperformed other classical energy conversation methods such as LEACH-C and RR-Schedule-Buffer in terms of energy consumption. It extends 16% network lifetime, also 20% less number of messages during data transmission, which is a remarkable improvement for sensors energy conservation.

A secured authentication and DSM‐KL ascertained performance optimization of a hybrid block chain‐enabled framework for a multiple WSN

SummaryBlock chain is extensively seen as a potential alternative in the safety and efficiency problems of the vast internet of things (IoT) data to allow safe and successful data storage/processing/sharing. In this manuscript, a secured authentication and deep slime mould optimized kernel learning (DSM‐KL) ascertained performance optimization of a hybrid block chain‐enabled framework for a multiple wireless sensor network (WSN) is proposed to recover data security performance and efficiency. To reflect the reality of the multi‐WSN network better, local block chain and public block chain are deployed, and a hybrid block chain model is created as per the various capabilities and energy of different nodes. A multi‐WSN network model is intended. It has numerous nodes on IoT. Based on dissimilar functions of nodes, the IoT nodes are divided into base stations, cluster heads and ordinary nodes based on its capabilities that facilitate management and cooperation of nodes. A DSM‐KL algorithm for dynamically selecting/adjusting block producer, consensus algorithm, block size and block interval to recover efficiency to handle both dynamic and broad dimensional properties of IoT systems is designed. The simulation process is executed in the MATLAB platform. The proposed DSM‐KL attains lower drop 27.53%, higher delivery ratio 28.56%, lower drop 28.64%, lower energy depletion rate 38.63%, higher network active time 38.47%, lower overhead 26.47% and higher throughput 27.67%, and the performances are compared with the existing methods such as deep reinforcement learning (DRL) and block chain and reinforcement learning (RLBC), respectively.

EDGF: Empirical dataset generation framework for wireless sensor networks

In wireless sensor networks (WSNs), simulation practices, system models, algorithms, and protocols have been published worldwide based on the assumption of randomness. The applied statistics used for randomness in WSNs are broad, e.g., random deployment, activity tracking, packet generation, etc. Even though authors’ adequate formal and informal information and pledge validation of the proposal became challenging, the minuscule information alteration in implementation and validation can reflect the enormous effect on eventual results. In this proposal, we show how the results are affected by the generalized assumption made on randomness. In sensor node deployment, ambiguity arises due to node error-value (ε), and its upper bound in the relative position is estimated to understand the delicacy of diminutives changes. Besides, the effect of uniformity in the traffic and participation of scheduling position of nodes is also generalized. We propose an algorithm to generate the unified dataset for the general and some specific applications system models in WSNs. The results produced by our algorithm reflect the pseudo-randomness and can efficiently regenerate through seed value for validation.

A novel hierarchical fault management framework for wireless sensor networks: HFMF

A novel hierarchical fault management framework for wireless sensor networks: HFMF

Optimized Energy Management Model on Data Distributing Framework of Wireless Sensor Network in IoT System

Optimized Energy Management Model on Data Distributing Framework of Wireless Sensor Network in IoT System

WSN-SES/MB: System Entity Structure and Model Base Framework for Large-Scale Wireless Sensor Networks.

Large-scale wireless sensor networks are characterized by stringent energy and computation restrictions. It is exceedingly difficult to change a sensor network’s environment configurations, such as the number of sensor nodes, after deployment of the nodes. Although several simulators are able to variously construct simulation models for sensor networks before their deployment, the configurations should be modified with extra human effort as the simulators cannot freely generate diverse models. In this paper, we propose a novel framework, called a system entity structure and model base for large-scale wireless sensor networks (WSN-SES/MB), which is based on discrete event system specification formalism. Our proposed framework synthesizes the structure and models for sensor networks through our modeling construction process. The proposed framework achieves time and cost savings in constructing discrete event simulation-based models. In addition, the framework increases the diversity of simulation models by the process’s pruning algorithm. The simulation results validate that the proposed framework provides up to 8% time savings and up to 23% cost savings as compared to the manual extra effort.

Sensor networks simulation framework for target tracking applications: SN-SiFTTA

This work presents a new simulation framework for wireless sensor networks dedicated for target tracking applications named: SN-SiFTTA. SiFTTA does not address routing protocols as they have been s...

Reinforcement Learning Framework for Delay Sensitive Energy Harvesting Wireless Sensor Networks

A multi-hop energy harvesting wireless sensor network (EH-WSNs) is a key enabler for future communication systems such as the internet-of-things. Optimal power management and routing selection are important for the operation and successful deployment of EH-WSNs. Characterizing the optimal policies increases significantly with the number of nodes in the network. In this paper, optimal control policy is devised based on minimum-delay transmission in a multi-hop EH-WSN using reinforcement learning (RL). The WSN consists of M EH sensor nodes aiming to transmit their data to a sink node with a minimum delay. Each sensor node is equipped with a battery of limited capacity to save the harvested energy and a data buffer of limited size to store both the sensed and relayed data from neighboring nodes. Centralized and distributed RL algorithms are considered for EH-WSNs. In the centralized RL algorithm the control action is taken at a central unit using the state information of all sensor nodes. In the distributed RL algorithm the control action is taken locally at each sensor node using its state of information and the state information of neighboring nodes. The proposed RL algorithms are based on the state-action-reward-state-action (SARSA) algorithm. Simulation results demonstrate the merits of the proposed algorithms.

Lightweight multi-agent framework for a cluster-based wireless sensor network

Sensor applications and wireless sensor networks (WSNs) are becoming a part of our everyday life. A number of network arrangements are used in WSN. In this paper, we focus on the cluster based network to help identify the issues associated with communication within such networks. We present a lightweight multi-agent routing framework for a cluster based WSN to resolve some issues associated with such networks. By using state- of-art protocol in a unique combination and categorizing cluster layers, we take full advantage of the properties of the selected protocols. The simulation results illustrate that the proposed method is light-weight in terms of energy consumption by the sensor nodes communicating information within a cluster based network. Nevertheless, high network throughput and robust data communication are also achieved.

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.

Deployment of sensor nodes for aquaculture in western Godavari delta: results, challenges and issues

In this paper, design and development of a real-time water quality monitoring framework for aquaculture ponds has been presented. This wireless sensor network framework consists of sensor nodes to measure water pH, dissolved oxygen, water temperature, atmospheric temperature and pressure using sensors and transmit this information to the data storage and analysis module. Alerts on physico-chemical parameters of water are made available to the farmers through designed mobile application. The framework has been deployed in two testbeds, namely end-user shrimp farm and an institution pond, for the evaluation of system feasibility and robustness. Statistical analysis of the data collected through the system deployed in these testbeds was conducted, and it was observed that these physico-chemical parameters of water are within range, which depicts that the pond water is suitable for the growth of shrimp and fish. Feedback collected from the end-users (local aquaculture farmers) conveys acceptability of the system by them. Learnings and research challenges observed during system deployment have also been enumerated.

Real-Time Identification of Irrigation Water Pollution Sources and Pathways with a Wireless Sensor Network and Blockchain Framework.

Real-time identification of irrigation water pollution sources and pathways (PSP) is crucial to ensure both environmental and food safety. This study uses an integrated framework based on the Internet of Things (IoT) and the blockchain technology that incorporates a directed acyclic graph (DAG)-configured wireless sensor network (WSN), and GIS tools for real-time water pollution source tracing. Water quality sensors were installed at monitoring stations in irrigation channel systems within the study area. Irrigation water quality data were delivered to databases via the WSN and IoT technologies. Blockchain and GIS tools were used to trace pollution at mapped irrigation units and to spatially identify upstream polluted units at irrigation intakes. A Water Quality Analysis Simulation Program (WASP) model was then used to simulate water quality by using backward propagation and identify potential pollution sources. We applied a “backward pollution source tracing” (BPST) process to successfully and rapidly identify electrical conductivity (EC) and copper (Cu2+) polluted sources and pathways in upstream irrigation water. With the BPST process, the WASP model effectively simulated EC and Cu2+ concentration data to identify likely EC and Cu2+ pollution sources. The study framework is the first application of blockchain technology for effective real-time water quality monitoring and rapid multiple PSPs identification. The pollution event data associated with the PSP are immutable.

Dataset of short-term prediction of CO2 concentration based on a wireless sensor network.

This CO2 data is gathered from WSN (Wireless Sensor Network) sensors that is placed in some areas. To make this observation framework run effectively, examining the relationships between factors is required. We can utilize multiple wireless sensor devices. There are three parts of the system, including the sensor device, the sink node device, and the server. We use those devices to acquire data over a three-month period. In terms of the server infrastructure, we utilized an application server, a user interface server, and a database server to store our data. This study built a WSN framework for CO2 observations. We investigate, analyze, and predict the level of CO2, and the results have been collected. The Random Forest algorithm achieved a 0.82 R2 Score.

A framework for multimodal wireless sensor networks

During the last decade, the Wireless Sensor Networks have been considered as a broad solution for Internet of Things (IoT) based systems such as noise and air pollution monitoring systems. These systems should be able to monitor the physical variables regularly and simultaneously, to react immediately upon the occurrence of an emergency, and to report the event and its associated data to an observer, in a reliable and energy-efficient way. Recently, the MultiModal Wireless Sensor Networks (M2WSNs) have been proposed as a solution for monitoring oriented applications with low bandwidth requirements that operate simultaneously under normal circumstances and emergencies. In this paper, we present a reliable and energy-efficiency framework for M2WSNs based on the IoT and the Wake-up Radio paradigms. The framework is implemented in ContikiOS, an open-source operating system for IoT solutions, and its performance is evaluated in hardware in an indoor testbed. The experimental results show that the proposed framework provides better reliability in terms of the event reporting latency and packet delivery ratio. Besides, a significant energy-saving is achieved when considering a broadcast-based wake-up scheme with one communication hop for data transmission and acknowledgment procedures in a multi-hop network, compared to a single-radio approach based on a traditional low-duty-cycling and networking techniques.

Smart-Green-Mult (SGM): overhear from topological kingpins in software defined wireless sensor networks

Wireless sensor networks (WSNs) are an important component of the Internet of Things (IoT). Each multicast group in a WSN consists of a set of multicast members. In a sparse network, multicast members belonging to the same group may not be very closely spaced. On the other hand, in a dense environment, more than one multicast member of a group may highly reside within downlink regions of the same router which may or may not be a multicast member. In that case, a multicast message can be delivered to more than one multicast member in a shot. This characteristic improves the status of a node from an ordinary router to a topological kingpin. If a good number of topological kingpins are included in a multicast tree, then a great amount of energy is saved increasing network throughput. The present article proposes one such energy efficient multicast scheme: Smart–Green–Mult (SGM), based on Software Defined Wireless Sensor Network (SD-WSN) framework. The network is divided into several zones. The shape of each zone is either circular or elliptical or polygonal. Each zone is under control of an Software Defined Network controller. SDN controller of each zone is aware of the topology of the zone and can compute energy efficient paths from any source to any destination inside or outside the zone. Overall this is a multicast protocol in SD-WSN.

Smart home autonomous water management system for agricultural applications

PurposeIn recent days, there is a huge loss in the income of farmers due to the reasons such as low water lever and increased pesticide attack. Therefore, the purpose of this paper is to establish an efficient reliable low-cost information gathering Reliable Low-Cost Information Gathering Protocol (RLCIG) protocol for agricultural water irrigation using optimal clustering and path selection technique where the RCIG protocol wrinkles the expedient statistics about the moisture and temperature of the soil and it will be installed few inches below the pipeline. Thereafter, the congregated data will augment the irrigation of water by using a decision-making algorithm.Design/methodology/approachThe projected model has been inscribed mathematically by underlying the wireless sensor networks (WSN) framework with deliberation of contemporary challenges. Furthermore, the energy, cost and expanse optimization framework in the WSN framework is presented. The projected technique has been tested using network simulator and the results are also integrated MATLAB.FindingsRecently, for efficacious management in the field of agriculture, the WSN has been successfully assimilated. This instigation accomplishes the irrigation management in terms of energy, cost and communication distance. The simulation result shows that the proposed model yields better results in terms of both the transmission range and cost with efficient lifetime improvement in comparisons with existing techniques.Originality/valueAgriculture is the need of the time whatever invention happens in the scientific world without food production no lives survive on the earth, hence, the scientific invention should also focus on agriculture, in this contrast, the authors have proposed an efficient low-cost information gathering (RCIG) protocol for agricultural water irrigation using optimal clustering and path selection technique.

An Energy Efficient and Secure IoT-Based WSN Framework: An Application to Smart Agriculture.

Wireless sensor networks (WSNs) have demonstrated research and developmental interests in numerous fields, like communication, agriculture, industry, smart health, monitoring, and surveillance. In the area of agriculture production, IoT-based WSN has been used to observe the yields condition and automate agriculture precision using various sensors. These sensors are deployed in the agricultural environment to improve production yields through intelligent farming decisions and obtain information regarding crops, plants, temperature measurement, humidity, and irrigation systems. However, sensors have limited resources concerning processing, energy, transmitting, and memory capabilities that can negatively impact agriculture production. Besides efficiency, the protection and security of these IoT-based agricultural sensors are also important from malicious adversaries. In this article, we proposed an IoT-based WSN framework as an application to smart agriculture comprising different design levels. Firstly, agricultural sensors capture relevant data and determine a set of cluster heads based on multi-criteria decision function. Additionally, the strength of the signals on the transmission links is measured while using signal to noise ratio (SNR) to achieve consistent and efficient data transmissions. Secondly, security is provided for data transmission from agricultural sensors towards base stations (BS) while using the recurrence of the linear congruential generator. The simulated results proved that the proposed framework significantly enhanced the communication performance as an average of 13.5% in the network throughput, 38.5% in the packets drop ratio, 13.5% in the network latency, 16% in the energy consumption, and 26% in the routing overheads for smart agriculture, as compared to other solutions.

A multi-view CNN-based acoustic classification system for automatic animal species identification

Automatic identification of animal species by their vocalization is an important and challenging task. Although many kinds of audio monitoring system have been proposed in the literature, they suffer from several disadvantages such as non-trivial feature selection, accuracy degradation because of environmental noise or intensive local computation. In this paper, we propose a deep learning based acoustic classification framework for Wireless Acoustic Sensor Network (WASN). The proposed framework is based on cloud architecture which relaxes the computational burden on the wireless sensor node. To improve the recognition accuracy, we design a multi-view Convolution Neural Network (CNN) to extract the short-, middle-, and long-term dependencies in parallel. The evaluation on two real datasets shows that the proposed architecture can achieve high accuracy and outperforms traditional classification systems significantly when the environmental noise dominate the audio signal (low SNR). Moreover, we implement and deploy the proposed system on a testbed and analyse the system performance in real-world environments. Both simulation and real-world evaluation demonstrate the accuracy and robustness of the proposed acoustic classification system in distinguishing species of animals.

A Mobile Localization Method in Smart Indoor Environment Using Polynomial Fitting for Wireless Sensor Network

The main factor affecting the localization accuracy is nonline of sight (NLOS) error which is caused by the complicated indoor environment such as obstacles and walls. To obviously alleviate NLOS effects, a polynomial fitting-based adjusted Kalman filter (PF-AKF) method in a wireless sensor network (WSN) framework is proposed in this paper. The method employs polynomial fitting to accomplish both NLOS identification and distance prediction. Rather than employing standard deviation of all historical data as NLOS detection threshold, the proposed method identifies NLOS via deviation between fitted curve and measurements. Then, it processes the measurements with adjusted Kalman filter (AKF), conducting weighting filter in the case of NLOS condition. Simulations compare the proposed method with Kalman filter (KF), adjusted Kalman filter (AKF), and Kalman-based interacting multiple model (K-IMM) algorithms, and the results demonstrate the superior performance of the proposed method. Moreover, experimental results obtained from a real indoor environment validate the simulation results.