An efficient approach for data transmission in power-constrained wireless sensor network

Profiling environmental parameter using a large number of spatially distributed wireless sensor network (WSN) NODEs is an extensive illustration of advanced modern technologies, but high power requirement for WSN NODEs limits the widespread deployment of these technologies. Currently, WSN NODEs are extensively powered up using batteries, but the battery has limitation of lifetime, power density, and environmental concerns. To overcome this issue, energy harvester (EH) is developed and presented in this paper. Solar-based EH has been identified as the most viable source of energy to be harvested for autonomous WSN NODEs. Besides, a novel chemical-based EH is reported as the potential secondary source for harvesting energy because of its uninterrupted availability. By integrating both solar-based EH and chemical-based EH, a hybrid energy harvester (HEH) is developed to power up WSN NODEs. Experimental results from the real-time deployment shows that, besides supporting the daily operation of WSN NODE and Router, the developed HEH is capable of producing a surplus of 971 mA·hr equivalent energy to be stored inside the storage for NODE and 528.24 mA·hr equivalent energy for Router, which is significantly enough for perpetual operation of autonomous WSN NODEs used in environmental parameter profiling.

Herein, an Internet of Things (IoT) system with remote reconfigurable wireless sensor network (WSN) nodes is presented and its application to measure group work activity in a classroom is discussed. This IoT system consists of wiki pages, bots, (defined as remotely controlled devices), gateways, and remotely reconfigurable WSN Nodes. The bots/gateways and WSN nodes of the IoT system are controlled by scripts written on wiki pages that are read by the bot/gateways, which then forward the commands contained in the scripts to the WSN nodes. Each WSN node then executes the commands, obtains the resulting sensor values, and then returns those values to the bot/gateways. The bot/gateways relay the values and write them onto the wiki page. Each WSN node is flexible and reconfigurable because its behavior can be changed by the script commands written onto the wiki page. Using this IoT system, we created a measurement system to evaluate activity levels of group work in a classroom. After developing the measurement system, we found it was possible to adjust the sampling rate for obtaining sensor values simply by rewriting the script on the wiki page without directly manipulating the WSN.

This paper designs the mathematical model of fiber line power, optimization of wireless sensor network node and multi distributed IP, and uses C language to program the model, finally get a new energy-saving mechanism model of wireless sensor network.Combined with the distributed IPv6 technology and IEEE802.15.4 protocol, this paper designs a new 6LoWPAN wireless sensor network energy-saving mechanism, which realizes the seamless splicing energy-saving effect between wireless sensor network and the internet, and based on this develops Contiki integrated environment of wireless sensor network energy-saving test.Finally this paper uses Firefox browser with B/S architecture to test energysaving mechanism.Through the test we found that between two nodes of wireless sensor network, they can effectively realize sending and receiving of UDP data, and through IPv6 gateway it can connect with internet, and complete collection and transmission of data.Through the simulation calculation of power consumption, in accepting, transmission and idle periods, the wireless sensor network can save energy effectively, which reduces the splicing cost of internet and wireless sensor network.It provides the technical reference for the design of wireless sensor network.

Routing Algorithm determines the optimal route to transmit data packet from source to destination. The nodes in wireless sensor networks (WSN) are devoid of abundant energy and hence energy efficiency is the vital benchmarks for data transmission in WSN. The basic importance of wireless sensor networks (WSN) in present scenario is to reduce the node energy consumption such that it improves the data transmission efficiency and reliability to extend entire network life time. In these respect, we need to search the optimal route for data transmission in WSN. Here an Improved Ant Colony Optimization (IACO) based energy efficient routing algorithm is proposed in WSN to solve the issue. The function of improved Low Energy Adaptive Clustering Hierarchy (LEACH) routing algorithm is to select cluster heads (CH) by considering residual energy and the distance in between the nodes. To balance the energy consumption between the nodes, residual energy is used as an energy control factor, which affects the network life time and enhances the data transmission efficiency. An ACO algorithm is applied to search the optimal path from the various available routes in between CH and sink for data transmission. Simulation results show that the proposed method Improved Ant Colony Optimization based Low Energy Adaptive Clustering Hierarchy (IACOLEACH) is found to yield better results over other conventional algorithms.

Wireless Sensor Network (WSN) suffers from different malware attacks. Several traditional approaches are proposed for detecting fault nodes in WSN. It is necessary to redistribute the nodes and detect the fault nodes while modifying the WSN parameters. Faults can occur in the nodes interrupting the continuous communication process of the nodes in the WSN. The main reason for a fault is also the induction of the energy drain in a node to the maximum possible level, failure of links in communicating nodes due to bandwidth constraints, and attacks induced by the malicious nodes. To solve this problems, Extended Finite State Machine based Fault Tolerance (EFSM_FT) in the WSN is used to detect the fault sensor nodes in the WSN. In this approach, called the finite state machine (FSM), state prompted is modified while a set of situations are true. Every node can be absolutely to be faulty or not establishing on the sensor node states. Using sensor energy, drop rate, forward sensor rate, sensor obtained rate, bandwidth, and transmission delay, this method finds the node with the fault. As a result, this approach accurately detects the faulty nodes in the WSN. The simulation outcomes illustrate the proposed method has a better fault detection ratio and minimizes the false negative ratio in the WSN.

Produced sand in oil pipelines poses a serious problem in many production situations, since a small amount of sand in the produced fluid can result in significant erosion in a very short time stage. Reliable sand detection is an important component of oil production systems; it would be valuable for assisting with optimizing well productivity and detecting sand as early as possible. In this work, a new reliable distributed sand-monitoring framework using a Wireless Sensor Network (WSN) is proposed. The framework combines three modules: Data Acquisition (DA) module, Wireless Receiving and Transmission (WRT) module, and Distributed Data Fusion module (DDF). The WRT module implementation is based on TinyOS and Crossbow MICAz mote. The DDF module is based on a resource-aware Distributed Kalman Filter (DKF). The data is collected from pipeline using acoustic sensor (SENACO AS100) and Flow Analyzer (MC-II) in real time. The experimental results validate the proposed framework. Sand production is considered as one of the major problems facing the petroleum industry. In the oil and gas industry, the sand production causes considerable erosion damage in the well tubing, piping, fittings, separators, valves, and other equipment. It can cause poor performance in injection wells, and can lead to lost production. It arises in the case of failure of sand control measures. Sand screening is also a critical part of the mining process (1). Every year, cleaning and repairing operations related to sand production and restricted production rates cost the industry millions of dollars. However, a reliable sand monitor system is essential to decide whether sand control measures need to be installed during well operations or not. Sand monitoring allows timely actions to be taken to handle sand production such as: (a) Increasing inspection to detect erosion; (b) Reducing the flow rate or stopping the production in some of the extreme cases; (c) Installation of sand handling systems (2). The sand problem is not new and affects the entire oil industry. In some cases several tones of sand can come from a reservoir in one day. Deciding when it is necessary to deal with sand is a difficult decision. Several fields have installed a sand detection system. Installation of a system to monitor and measure sand production would be helpful to detect sand as early as possible. Early sand detection would then lead to possible remedial action that could avoid the erosion and improve the system production. However, to be able to do this, we need repeatability, sensitivity, and also real time measurements (3). Oil and gas companies started to integrate their legacy sensors with Wireless Sensor Networks (WSNs) technology to help them to increase production, streamline operations, and reduce expenses. We introduced some solutions toward this direction. In (4), a reliable prototype of a remote measuring of flow rate using a MC-II Flow Analyzer was introduced. In (5), we developed a Wireless Sensor Data Acquisition (WSDA) prototyping. It composed of both legacy and modern sensors. Some other solutions introduced centralized and distributed fusion algorithms for sand detection using WSN (6-9). These fusion algorithms include Fuzzy Art (FA), Maximum Likelihood Estimator (MLE), Moving Average Filter (MAF), and Distributed Kalman Filter (DKF). The DKF showed promising results over the other algorithms. Unlike the other algorithms, Kalman filter does not lend itself for easy implementation in WSN; this is because WSN has limited computation capability, small memory, and limited energy. DKF involves seventeen matrix operations: ten matrix multiplications, two matrix inversions, four matrix additions, and one matrix subtraction. These operations are computationally intensive and could strain the energy resources of any single computational node in WSN. In order to share the information gathered by all the sensors, we encounter fundamental problems: communication congestion and scalability. The scalability issue and communication congestion are closely related in DKF algorithm. The more sensors we add to our system, the more communication we will require. Moreover, the scalability problem is not only related to communication issues, but also to computation problems. Higher dimensional measurement vectors mean higher computational demands. In this paper, a new distributed sand-monitoring framework using WSN is proposed. In order to meet with constraints of WSN, a resource-aware DKF is proposed. The proposed DKF is based on fast polynomial to increase the convergence rate. Fast convergence contributes to energy saving because it saves time. In addition, we reduce the communication traffic by exchanging the estimates between the nodes instead of exchanging the estimates and its variance. Each node uses its measurement, its associated variance, and its neighbors' estimates. This leads not only to significant energy saving, but also reduces the memory usage and avoids congestion. Moreover, we redesigned the DKF to reduce the number of arithmetic operations. Thus, the proposed optimizations make the DKF applicable to be implemented in WSN. The experimental results validate the proposed framework.

Recently, the technology of wireless sensor networks (WSNs) experiences a growing use in home automation or advanced industry infrastructure applications. Despite a strong interest of industries in this technology, key issues like miniaturization and security of WSN nodes has not been solved yet. State-of-the-art WSN nodes do not provide credible security nor satisfying configurability and miniaturized implementations. This publication deals with these limitations and presents a WSN node that provides security, configurability, and a miniaturized design. To show the sensor node feasibility, the WSN node is implemented within a smart home demonstrator. Additionally, a miniaturized pre-study WSN node design is presented using the novel embedded wafer level ball grid array (eWLB) packaging technology. Furthermore, an eWLB based WSN node design is proposed that further miniaturize the presented WSN node.

Over the past decades, hardware and software technologies for wireless sensor networks (WSNs) have significantly progressed, and WSNs are widely used in various areas including Internet of Things (IoT). In general, existing WSNs are mainly used for applications that require delay-tolerance and low-computation due to the poor resources of traditional sensor nodes in WSNs. However, compared to the traditional sensor nodes, today’s devices for WSNs have more powerful resource. Thus, sensor nodes these days not only conduct sensing and transmitting data to servers but also are able to process many operations, so more diverse applications can be applied to WSNs. Especially, many applications using audio data have been proposed because audio is one of the most widely used data types, and many mobile devices already have a built-in microphone. However, many of the applications have a requirement that heavy-operations should be done by a tight deadline, so it is difficult for a single node in WSNs to run relatively heavy applications by itself. In this paper, to overcome this limitation of WSNs, we propose a new emerging system, HeaLow, a cooperative computing system for heavy-computation and low-latency processing in WSNs. We designed HeaLow and carried out the practical implementation on real devices. We confirmed the effectiveness of HeaLow through various experiments using the real devices and simulations. Using HeaLow, nodes in WSNs are able to perform heavy-computation processes while satisfying a completion time requirement.

A wireless sensor network (WSN) is a collection of various tiny devices known as sensor nodes, which are also called motes. Due to high-energy consumption, the possibility of hardware, link or node failure, and some malicious attacks, sensor networks are considered error-prone networks. Hence, fault tolerance (FT) in WSN is one of the prominent issues. This article presents a novel FT approach named node-link failure fault tolerance model (NLFFT Model) in WSN, to handle the faults that occur either by link or node failure during data transmission from the sensor to the sink or base station. The NLFFT model consists of an improved quadratic minimum spanning tree (Imp-QMST) approach. This approach helps in finding the alternate link whenever it fails due to various situations and also an improved-handoff (Imp-Handoff) algorithm to support the node failure to the fault tolerance. Improved QMST presents a novel mechanism to find an alternate edge in place of the broken or failed edge in the spanning tree, to improve the fault tolerance in WSN. Imp-Handoff suggests a novel way to find the faulty node owing to less battery power and replaces a defective node by an appropriate neighbor to shift the tasks performed by a faulty node in WSN. Simulation results clearly state that as compared to the basic techniques i.e. Q-MST and Handoff algorithm, the proposed NLFFT model improvises the performance of WSN around by 7%. The results prove that the Imp-QMST gives about 6% improved throughput, 5% less end-to-end delay, and 6% less power consumption than the QMST algorithm. Similarly, Imp-Handoff improves about 4% throughput, 6% less end-to-end delay, and utilizes 7% less power consumption.

This thesis addresses the problem of traffic transfer in wireless sensor networks (WSN). In such networks, the foremost challenge in the design of data communication techniques is that the sensor's transceiver circuitry consumes the major portion of the available power. Thus, due to stringent limitations on the nodes' hardware and power resources in WSN, data transmission must be power-efficient in order to reduce the nodes' power consumption, and hence to maximize the network lifetime while satisfying the required data rate. The transmit power is itself under the influence of data rate and source-destination distance. Thanks to the dense deployment of nodes in WSN, multi-hop communication can be applied to mitigate the transmit power for sending bits of information, i.e., gathered data by the sensor nodes to the destination node (gateway) compared to single-hop scenarios. In our approach, we achieve a reasonable trade-off between power-efficiency and transmission data rate by devising cooperative communication strategies through which the network traffic (i.e. nodes' gathered information) is relayed hop-by-hop to the gateway. In such strategies, the sensor nodes serve as data originator as well as data router, and assist the data transfer from the sensors to the gateway. We develop several data transmission schemes, and we prove their capability in transmitting the data from the sensor nodes at the highest possible rates allowed by the network limitations. In particular, we consider that (i) network has linear or quasi-linear topology, (ii) nodes are equipped with half-duplex radios, implying that they cannot transmit and receive simultaneously, (iii) nodes transmit their traffic at the same average rate. We compute the average data rate corresponding to each proposed strategy. Next, we take an information-theoretic approach and derive an upper bound to the achievable rate of traffic transfer in the networks under consideration, and analyze its tightness. We show that our proposed strategies outperform the conventional multi-hop scheme, and their average achievable rate approaches the upper bound at low levels of signal to noise ratios

Wireless Sensor Network (WSN) design to be efficient expects better energy optimization methods as nodes in WSN are operated only through batteries. In WSN, energy is a challenging one in the network during transmission of data. To overcome the energy issue in WSN, Taylor based Grey Wolf Optimization algorithm proposed, which is the integration of the Taylor series with Grey Wolf Optimization approach finding optimal hops to accomplish multi-hop routing. This paper shows the multiple objective-based approaches developed to achieve secure energyaware multi-hop routing. Moreover, secure routing is to conserve energy efficiently during routing. The proposed method achieves 23.8% of energy, 75% of Packet Delivery Ratio, 35.8% of delay, 53.2% of network lifetime, and 84.8% of scalability.

An efficient cluster head selection for wireless sensor network-based smart agriculture systems

In wireless sensor networks, the mobile agent technology is used in data transmission from one cluster to another cluster. The Optimization of mobile agent in the routing within the clustering algorithm for wireless sensor networks to further reduce the amount of data transfer. the main objectives of wireless sensor network design is to improve the energy efficiency & effective utilization of sensor nodes to prolong the lifespan of a wireless network .This paper presents an effective clustering algorithm, which is an extension to the LEACH routing algorithm., this algorithm fully utilizes the location information of network nodes in routing to reduce the routing cost. Through a clustering approach of intelligent mobile agents, it is possible to reduce the cost of time for each individual agent, here the problem of Multiple Clustering of Mobile Agents (MCCMA) where the decision is to cluster mobile agents such that a group of similar intelligent mobile agents will visit a group of similar sensor nodes are compare with location based clustering algorithm of wsn. both of Simulation results indicate that these algorithm & approach of mobile agent & wireless sensor network Nodes can have balance nodes' energy consumption and prolong the network's life span. It also has good extensibility & stability

The existing routing protocols for wireless sensor networks were not reasonable in design, which limited their application. Most of the existing studies did not take into account the energy consumption of the network and the balanced use of the energy of sensor nodes, which led to the unsatisfactory application effect of wireless sensor networks in some fields. Therefore, from the perspective of energy balance in wireless sensor networks, this paper proposed a construction method of an energy balance routing protocol in wireless sensor networks based on a fuzzy control strategy. Firstly, based on the analysis of the basic composition of wireless sensor networks and the structure of sensor nodes, this paper expounded the basic process of wireless data transmission and summarized the classification and characteristics of routing protocols in wireless sensor networks from different angles. Secondly, according to the node data transmission characteristics of wireless sensor networks, the energy balance use model of sensor nodes was established, and the design method of the energy balance routing protocol based on fuzzy control strategy was proposed, and the data transmission link was optimized. Finally, through experimental comparative analysis, the results showed that the energy balanced routing protocol proposed in this paper can effectively realize the energy balanced use of the network data transmission process. Compared with other common routing protocols, the wireless sensor network routing protocol proposed in this paper can not only improve the data transmission efficiency and reduce the data redundancy but also save energy consumption and prolong the network running time. The design method of routing protocol proposed in this paper will be conducive to the optimization and application of routing protocol in wireless sensor networks and provide a theoretical basis for the related research of wireless sensor networks.

For the past few years, centralized decision-making is being used for malicious node identification in wireless sensor networks (WSNs). Generally, WSN is the primary technology used to support operations, and security issues are becoming progressively worse. In order to detect malicious nodes in WSN, a blockchain-routing- and trust-model-based jellyfish search optimizer (BCR-TM-JSO) is created. Additionally, it provides the complete trust-model architecture before creating the blockchain data structure that is used to identify malicious nodes. For further analysis, sensor nodes in a WSN collect environmental data and communicate them to the cluster heads (CHs). JSO is created to address this issue by replacing CHs with regular nodes based on the maximum remaining energy, degree, and closeness to base station. Moreover, the Rivest–Shamir–Adleman (RSA) mechanism provides an asymmetric key, which is exploited for securing data transmission. The simulation outcomes show that the proposed BCR-TM-JSO model is capable of identifying malicious nodes in WSNs. Furthermore, the proposed BCR-TM-JSO method outperformed the conventional blockchain-based secure routing and trust management (BSRTM) and distance degree residual-energy-based low-energy adaptive clustering hierarchy (DDR-LEACH), in terms of throughput (5.89 Mbps), residual energy (0.079 J), and packet-delivery ratio (89.29%).