Traffic Transfer Assisted by Super Nodes for Strip-Shaped Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consist of a large number of low powered nodes that need to operate for months unattended. Since modern WSNs are used in various applications, their topology is becoming complicated. Due to limited precision of deployment, holes may occur in the network, which often lead traditional Greedy Forwarding algorithms to fail. Thus, bypassing the holes is one of the important issues for WSNs. Since each node has limited energy, its energy consumption needs to be optimized to prolong network lifetime. In the well-known Virtual Position (ViP) scheme, each node routes data using virtual positions instead of actual geographic positions to improve the packet delivery rate. A Hole-bypassing Routing with Context-awareness scheme achieves balanced energy consumption by changing current path to one of the candidate paths, based on the residual energy of nodes. However, this scheme tends to extend the size of holes. Since existing hole detour schemes that do not consider efficient energy consumption, they cause imbalanced energy consumption and make network lifetime relatively shorter than other hole detour schemes. Similar to ViP, our scheme uses virtual positions to bypass holes. However, the virtual positions are computed using both geographic positions and the residual energies of neighbor nodes. Our approach outperforms the ViP scheme in terms of network lifetime and hole extension.

In wireless sensor network with a long-thin and tree-based topology, sensor nodes might fail to be configured with an address due to the lack of the address space and the routes along a tree might not be optimal. In order to overcome these deficiencies, this paper proposes an addressing-based routing optimization scheme for IPv6 over low-power wireless personal area network in vehicular scenarios AROV. In AROV, the architecture based on super nodes is proposed. Via one addressing process, all nodes included in one super node can be configured with an address. Because a super node covers sensor nodes in a multi-hop scope, it effectively avoids the addressing failure. Moreover, a super node makes use of its neighbor super nodes to calculate an optimal path, so the routing latency is reduced. AROV is evaluated, and the data results show that AROV effectively shortens the routing latency.

Among important issues in the current energy-efficient routing protocols for wireless sensor networks (WSNs) based on clustering approaches are how to determine the optimal number of clusters, how to generate clusters and how select cluster-heads to improve WSNs performance. These approaches aim to reduce energy consumption of the WSN nodes and to extend the network lifetime. This paper reviews and compares the performance of three clustering techniques for determining number of clusters: rule of thumb, elbow, and silhouette. Each of these techniques involves a distributed K-means approach to generate clusters. Moreover, we compare these three clustering methods with low energy adaptive clustering hierarchy (LEACH), Imp LEACH and MODLEACH in terms of network lifetime, energy consumption and the number of packets sent to the base station (BS). The results obtained indicate that rule of thumb method provides better performance compared to other clustering techniques in terms of energy consumption and network lifetime.

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.

An energy-efficient cross-layer-based opportunistic routing protocol and partially informed sparse autoencoder for data transfer in wireless sensor network

Energy efficient routing protocol is the requirement of today’s wireless sensor networks. Various protocols have been developed in order to create an energy efficient wireless sensor network, but still some loopholes exist in this domain and energy hole is one of them. Energy hole refers to the early energy diminution of those nodes that are near to the sink. This study introduced a mobile sink based energy aware clustering mechanism to enhance the lifetime of the network by overcoming the issue of energy holes. In proposed work, the network is initially divided into the number of rectangular regions and each region is comprised of one cluster head (CH). The nature-inspired firefly optimization algorithm is used to select cluster heads where residual energy, average node to node distance and distance from the node to sink are the decisive parameters of the process. The sink moves in the observing field after estimating the centroid location of the CHs. The performance of the proposed work is compared with the LEACH, LEACH-GA, A-LEACH, MIEEPB, and MSIEEP by using Matlab simulation platform. The result section represents the proficiency of the proposed MSECA protocol over traditional techniques in term of network lifetime, packet delivery ratio and packet delay.

Wireless sensor network (WSN) is a network consist of multiple sensor nodes which sense and transmit data to a base station for data collection. Since the nodes in WSN are battery powered devices and nodes loss energy mostly due to transmission, an energy efficient routing protocol is needed to reduce the nodes energy consumption and prolong the network lifetime. However, existing routing protocols produce some drawbacks such as selection of cluster head with low residual energy and formation of different sizes of cluster which leads to uneven energy consumption among the nodes. Therefore, an Ant Colony Optimization based Low Energy Adaptive Clustering Hierarchy (LEACH-ACO) protocol for energy efficient transmission in WSN is proposed in this paper. This research aims to develop a routing protocol that utilize the existing LEACH routing protocol to improve the performance of WSN and prolong the lifetime of the network. The results showed that LEACH-ACO protocol outperformed Direct Transmission (DT) and LEACH protocol in terms of network lifetime, energy consumption and energy per transmission.

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) 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.

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.

Clustering is essential in Wireless Sensor Networks (WSNs) as it facilitates less energy consumption routing, network partitioning, and the optimization of routing pathways depending upon the parameters such as energy consumption and distance. Numerous Clustering Routing Algorithms (CRAs) have been formulated for a long time. Among them, a Relay-optimized Dual Mode energy-minimized SCA-Levy (RDMSCA-Levy)-based CRA can choose the optimal Relay Node (RN) in multi-hop WSNs to enhance inter- and intra-cluster transmission. However, the major limitation of the static node deployment in WSN is the possibility of energy holes’ formation, even with high residual energy. Therefore, this article proposes an Enhanced RDMSCA-Levy (ERDMSCA-Levy) algorithm for multi-hop WSNs to detect and prevent energy holes. It incorporates the Voronoi geometry-based hole detection and relay positioning model-based hole prevention approaches. In the hole detection phase, the network region is split into Voronoi cells, where each sensor node serves as a generating point in the geometry. Then, the distance between the sensor nodes within the Region of Interest (ROI) and the edge points of their corresponding Voronoi cells Is determined. Any abnormal distances are detected as potential holes in the network. After that, a relay-based hole prevention is introduced, which involves repositioning RNs within the network based on the energy and distance. The updated locations of these RNs are then integrated into the Deep QLearning (DQL) algorithm to choose the best RN position and optimize the coverage. Finally, the findings reveal that the ERDMSCA-Levy attained a better efficiency when evaluated with the existing CRAs.

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.

Wireless sensor network (WSN) can achieve real-time data collection and transmission of environment, soil, meteorology, crop physiology and other information in agriculture. The data provided by WSN could be used for decision making and management, which is very important in precision agriculture. Wheat farmland wireless sensor network has the characteristics of wide coverage area, long planting period, inconvenient energy supply, and serious impact of crop environment on wireless signal transmission. Routing protocol is an important method to achieve long-term WSN monitoring by selecting an appropriate path with low energy consumption for data transmission. According to the phenomenon of uneven environment and channel parameters caused by intensive crop growth in farmland, a non-uniform clustering routing protocol based on effective energy consumption (UCEEC) was proposed in this work. The method combined with the characteristics of multi-path fading of farmland environment signals. The idea of image segmentation was introduced. Nodes with high similarity were divided into a cluster area by the dissimilarity between nodes in order to improve the intracluster communication performance. Meanwhile, a multi-hop path selection method between cluster-heads based on the estimation of two-hop effective energy consumption is designed. The energy consumption cost factor is calculated by the effective energy consumption and the average energy consumption within the cluster to achieve the minimum and balance of the overall energy consumption of the network. Simulation results show that, compared with the existing Maximum Residual Energy Based Routing (MREBR) protocol, minimum Energy Consumption Based Routing (MEC) routing protocols, UCEEC improves the energy balance effect between nodes, prolongs the network life cycle, and realizes efficient energy utilization of wireless sensor network data collection in the complex environment of wheat field. Keywords: farmland, wireless sensor networks, multi-path fading channel, routing protocol, energy optimization DOI: 10.25165/j.ijabe.20211403.6540 Citation: Miao Y S, Zhao C J, Wu H R. Non-uniform clustering routing protocol of wheat farmland based on effective energy consumption. Int J Agric & Biol Eng, 2021; 14(3): 163–170.

Efficient Statistical Clustering Techniques for Optimizing Cluster Size in Wireless Sensor Network