Wireless Sensor Network Architecture Research Articles

Improved Multiple Gateway Node Based Routing Architecture for Heterogeneous Wireless Sensor Network

In modern era, with technologies shifting from wired to wireless technologies, Wireless Sensor Networks (WSNs) play an essential role in advancement of technology. WSNs have limited battery .In this paper, we propose Improved Multiple Gateway Node (MGN) based Routing Architecture (IMRA) that increases both network lifetime and stability period of the network and also improves energy efficiency. It uses the technique of load balancing and considers average energy of the network to improve aforementioned parameters. IMRA is event-driven protocol and has four MGNs with unlimited power which makes this protocol efficient for use in disaster prone areas like earthquakes, forest fires etc. saving millions of lives. Wireless Sensor Networks (WSNs) play an essential role in advancement of technology. Various disasters like earthquakes, forest fires, etc. can be sensed in advance using latest technology in WSNs and preventive measures can be taken. IMRA surpasses other protocols by increasing Stability Period, Half Node Dead and Network lifetime by 58.18%, 55.94% and 52.96% respectively as compared to traditional MRA and by 156.99%, 86.2% and 90.12% respectively as compared to Threshold sensitive Energy Efficient Delay aware Routing Protocol(TEDRP).

A Novel Prevention Mechanism for Replay Attack in Distance Vector - HOP Localization Scheme

Securing the process of node localization in Wireless Sensor Network (WSN) is an important area of research. In this paper, we focus on securing Distance Vector (DV)-Hop localization scheme from Replay Attack. The DV-Hop localization algorithm is a limit free hop based reference Wireless Sensor Network architecture. It faces a couple of issues like a route discovery mechanism and threat prevention issues. This paper focuses on the establishment of a prevention mechanism for DV-Hop scheme against a Replay attack. The proposed architecture uses an Artificial Bee Colony (ABC) and Neural Network in order to prevent the attack. To prove the efficacy of the proposed scheme, the network scenario without replay attack, with replay attack and with attack after prevention mechanism is considered. The simulations are conducted for 200 iterations using MATLAB. The evaluation is done on the basis of parameters like localization error and transmission loss. Localization error of the proposed framework is similar to localization error without an attack which is about 0.57, and the maximum localized error is about 0.92. The maximum transmission loss without attack and after prevention is very close and varies by only 2 %, which is very good. The experimental results reveal that the proposed technique successfully prevents replay attack in WSN

Energy efficient load balancing approach for avoiding energy hole problem in WSN using Grey Wolf Optimizer with novel fitness function

In general architecture of Wireless Sensor Networks (WSNs), gateways far from the Base Station (BS) communicate with the BS via the gateways close to the BS. The energy of gateways which are close to the BS drains faster due to the heavy traffic load. This leads to the energy hole problem around the BS. Therefore, proper clustering of sensor nodes and routing of data are essential for efficient conservation of energy and for avoiding inadvertent network failure due to a power drain. In this paper, we apply the Grey Wolf Optimization (GWO) approach for energy-efficient clustering and routing in WSN. Also, we propose two novel fitness functions for clustering and routing problems. The fitness function for routing is formulated such that overall distance traversal and number of hops are minimized. The fitness function for clustering distributes the overall load according to the distance of gateways to the BS. The proposed GWO-based approach is resulted with higher values of both clustering and routing fitness functions as compared to the existing algorithms, namely, genetic algorithm, particle swarm optimization and multi-objective fuzzy clustering.

Study of multiple-source data collection in wireless sensor networks

<p>A detailed survey on the process of data collection from multiple sources in Wireless Sensor Networks (WSNs) is introduced. The topologies that determine the location of the network components with respect to each other are presented. These topologies are often referred to as Mobility topologies. The performance of the overall WSN architecture significantly depends on these topologies. As a consequence, these topologies are elaborately compared and discussed. The most common network components that efficiently collaborate in data collection process are explained. To highlight the data collection process as a subject of our concern, the phases that describe the stages of the data collection are illustrated. These phases consist of three successive stages: discovery, data transfer, and routing. To sum up, the most recent approaches for developing the process of data collection in multiple-source WSNs are also presented.</p>

Multimedia resource allocation strategy of wireless sensor networks using distributed heuristic algorithm in cloud computing environment

The virtualized resource allocation (mapping) algorithm is the core issue of network virtualization technology. Universal and excellent resource allocation algorithms not only provide efficient and reliable network resources sharing for systems and users, but also simplify the complexity of resource scheduling and management, improve the utilization of basic resources, balance network load and optimize network performance. Based on the application of wireless sensor network, this paper proposes a wireless sensor network architecture based on cloud computing. The WSN hardware resources are mapped into resources in cloud computing through virtualization technology, and the resource allocation strategy of the network architecture is proposed. The experiment evaluates the performance of the resource allocation strategy. The proposed heuristic algorithm is a distributed algorithm. The complexity of centralized algorithms is high, distributed algorithms can handle problems in parallel, and reduce the time required to get a good solution with limited traffic.

On the processing architecture in wireless video sensor networks: node and network level performance evaluation

The architecture of a wireless video sensor network node has an impact on the performance of a wireless sensor network. Due to nature of WVSN applications, the node should be capable of potentially performing image processing algorithms while simultaneously has high networking capabilities. Providing an optimum architecture in which both parameters are addressed is the main challenge in WVSN. This paper reviews suggested architectures for the node and based on that three base architectures are proposed and implemented. The performance of the nodes is then evaluated at node and network level and the results are included in the paper. Based on the results, an optimized architecture is introduced. This architecture separates the node to a network and an application part. This brings the flexibility of implementing the optimized solution for each part, without considering the other. The network part is implemented using a microcontroller. It is capable of adopting operating systems that are widely used in Wireless Sensor Networks. On the other hand, the application part is implemented using FPGA, that provides a power-optimized and stand-alone solution for the node with high computation capabilities.

Study of Wireless Communication Technologies on Internet of Things for Precision Agriculture

Precision agriculture is a suitable solution to these challenges such as shortage of food, deterioration of soil properties and water scarcity. The developments of modern information technologies and wireless communication technologies are the foundations for the realization of precision agriculture. This paper attempts to find suitable, feasible and practical wireless communication technologies for precision agriculture by analyzing the agricultural application scenarios and experimental tests. Three kinds of Wireless Sensor Networks (WSN) architecture, which is based on narrowband internet of things (NB-IoT), Long Range (LoRa) and ZigBee wireless communication technologies respectively, are presented for precision agriculture applications. The feasibility of three WSN architectures is verified by corresponding tests. By measuring the normal communication time, the power consumption of three wireless communication technologies is compared. Field tests and comprehensive analysis show that ZigBee is a better choice for monitoring facility agriculture, while LoRa and NB-IoT were identified as two suitable wireless communication technologies for field agriculture scenarios.

A directional multicasting-based architecture for wireless sensor networks

ABSTRACTEnergy scarcity, limited transmission range and interference are the primary challenges that need to be considered judiciously for Wireless Sensor Networks (WSNs). Directional data transmission can improve the network capacity as well as the network lifetime by facilitating spatial multiplexing and range extension, which inherently yields interference reduction, as the result of concurrent data transmissions. Another promising method of achieving energy conservation is the transmission power control. Transmission power control also contributes to the mitigation of interference thereby promotes throughput by means of rendering multiple hosts to communicate in the same directionality simultaneously without impairing each other’s transmissions. Harnessing the antenna directionality with dynamic power control gives the opportunity to WSNs to consume their scarce energy resources in a more efficient manner. In this article, we propose an energy efficient multi-cast routing-based architecture (MRBA). This architecture mainly bases on the idea of multi-cast routing methodology with hop-by-hop data fragmentation and power-controlled directional transmission. Multiple next-hop utilization during packet transmission together with directional relaying of the sub-packets provide interference reduction together with fair data transfer load sharing among the nodes. Simulation results show that significant improvements can be achieved in terms of energy saving and end-to-end delay enhancement with respect to omnidirectional architectures.

ETPS-MAC: Energy Traffic Priority Scheduling-based QoS-aware MAC protocol for hierarchical WSNs

ABSTRACTThe evolution of the wireless sensor network (WSN) in recent years has reached its greatest heights and applications are increasing day by day, one such application is Smart Monitoring Systems (SMSs) which is in vision of implementation in every urban and rural areas. The implementation of WSN architecture in SMS needs an intelligent scheduling mechanism that efficiently handles the dynamic traffic load without sacrificing the energy efficiency of network. This paper presents a centralized TDMA scheduling based medium access control (MAC) protocol, called Energy Traffic Priority Scheduling MAC (ETPS-MAC) that accommodates variable traffic load while maintaining Quality-of-Service (QoS) assurance in hierarchical WSNs. The ETPS-MAC protocol employs priority scheduling algorithm which considers two factors for assigning priority, the energy factor and the traffic load factor to avoid packet buffering and maintains minimum data packet delay in case of high traffic load. Moreover, a novel rank-based clustering mechanism in FPS-QMAC protocol prolongs the network lifetime by minimizing the distance between the cluster head (CH) and the base station (BS). Both analytical and simulation models demonstrate the superiority of the ETPS-MAC protocol in terms of energy consumption, transmission delay, data throughput and message complexity when compared with the existing TDMA based MAC protocols.

NLES: A Novel Lifetime Extension Scheme for Safety-Critical Cyber-Physical Systems Using SDN and NFV

The cyber-physical system (CPS) is a promising technique that enables a safety-critical industry ecosystem. In general, wireless sensor networks (WSNs) and the Internet of Things are the sensing and communication infrastructures for CPS. Currently, software-defined networking (SDN) have been used as the new networking architecture for typical WSNs and CPS. However, there are two unresolved problems for a software-defined CPS. First, a feasible systemic architecture is a must for software-defined CPS, which should provide a global virtualization management and closed-loop control between the cyber side and the physical side in a CPS. Second, the lifetime of a software-defined CPS scenario needs to be extended for critical applications. To address the above challenges, this paper proposes a systematic virtual networking architecture to perform the global virtualization control and monitoring of a CPS, in which network functions virtualization (NFV) configuration and orchestration can be realized. Moreover, based on the proposed architecture, a novel lifetime extension scheme (NLES), is proposed for a software-defined CPS. To orchestrate the resource dynamically and efficiently, the instant programmability of an SDN and instant deployment capability of NFV are utilized to control the topology of node modes of a CPS. Then, a game theoretic topology decision approach is proposed to control the topology of the clustering and virtual network function deployment of sensors at run-time in a CPS. The experimental results show that NLES has longer lifetime compared to those of the traditional schemes.

Discrete Wavelet Packet Transform-Based Industrial Digital Wireless Communication Systems

The industrial internet of things (IIoT) known as industry 4.0, is the use of internet of things technologies, via the Wireless Sensor Network (WSN), to enhance manufacturing and industrial processes. It incorporates machine learning and big data technologies, to allow machine-to-machine communication that have existed for years in the industrial world. Therefore, it is necessary to propose a robust and functional communication architecture that is based on WSNs, inside factories, in order to show the great interest in the connectivity of things in the industrial environment. In such environment, propagation differs from other conventional indoor mediums, in its large dimensions, and the nature of objects and obstacles inside. Thus, the industrial medium is modeled as a fading channel affected by an impulsive and Gaussian noise. The objective of this paper is to improve robustness and performances of multi-user WSN architecture, based on Discrete Wavelet Transform, under an industrial environment using conventional channel coding and an optimal thresholding receiver.

A Load Balancing Routing Mechanism Based on SDWSN in Smart City

In the wireless sensor network infrastructure of smart cities, whether the network traffic is balanced will directly affect the service quality of the network. Because of the traditional WSN (wireless sensor network) architecture, load balancing technology is difficult to meet the requirements of adaptability and high flexibility. This paper proposes a load balancing mechanism based on SDWSN (software defined wireless sensor network). This mechanism utilizes the advantages of a centralized control SDN (software defined network) and flexible traffic scheduling. The OpenFlow protocol is used to monitor the running status and link load information of the network in real time. According to the bandwidth requirement of the data flow, the improved load balanced routing is obtained by an Elman neural network. The simulation results show that the improved SDSNLB (software-defined sensor network load balancing) routing algorithm has better performance than LEACH (Low Energy Adaptive Clustering Hierarchy) protocol in balancing node traffic and improving throughput.

Optimizing the network energy of cloud assisted internet of things by using the adaptive neural learning approach in wireless sensor networks

Cloud-assisted internet of things (CIoT) is backboned by the wireless sensor network (WSN) architecture. A sensor network is an autonomous self-resource constraint collection of internet of things (IoT) sensor nodes. The nodes communicate in an ad-hoc fashion to transfer cloud information over the virtual environment. Clustering in WSNs helps to improve the quality of the network by controlling energy consumption and improving data gathering accuracy. This improves the service rates of CIoT. Optimizing IoT sensor networks through energy and overhead management requires complex clustering algorithms. A simple clustering scheme cannot achieve the desired performance enhancement during transmission in a virtual environment. This research attempts to propose a reinforcement-based learning technique, adaptive Q-learning (AQL) to improve network performance with minimum energy–overhead tradeoff in a sensor network-aided CIoT. AQL operates in two distinct phases for cluster head selection and forwarder selection. The decision-making system is used to qualify nodes based on their past behavior over transmission. AQL improves both inter- and intra-cluster communication optimization through adaptive forwarder and header selection conditions. The simulation results prove the consistency of the proposed AQL by retaining the live node counts in the network and their persistent energy despite the reduced overheads in the sensor network. With the achievement of constructive features in the sensor networks, the performance of CIoT is considerably improved. The experimental results illustrate the effectiveness of the proposed learning technique by improving network lifetime with a high request–response rate and by minimizing delay, overhead, and request failures.

Wireless Sensor Networks Suitable for Large-Scale Heterogeneous Networking

In order to optimize the network architecture, addressing mechanism, heterogeneous nodes and other functions of wireless sensor networks, this study begins with the issue of networking of large-scale heterogeneous networks. A layered distributed network architecture is proposed, which provides a powerful reference for the future architecture of wireless sensor networks. Based on this architecture, the resource addressing of the corresponding hierarchical network, and the scale and location deployment of heterogeneous nodes such as sink nodes are discussed separately, and corresponding strategies and algorithms are proposed. The research results show that the core idea of the addressing mechanism is data-centric, address-oriented addressing is transformed into service-oriented addressing. Therefore, the proposed LBA addressing algorithm is suitable for other hierarchically structured networks. In addition, although the sink node is taken as an example for research, it is also suitable for the deployment of other heterogeneous nodes such as sink nodes, relay nodes, and base stations. In summary, regardless of the number of nodes or the location of the deployment, energy-saving factors need to be considered. Energy-saving is also an indispensable technology in wireless sensor network technology.

QSDN-WISE: A New QoS-Based Routing Protocol for Software-Defined Wireless Sensor Networks

Today, a wide variety of applications with different requirements are rapidly developed in industrial wireless sensor networks, and providing the Quality of Service (QoS) for this kind of communication network is inevitable. It is difficult to solve the problems of poor adaptability and difficulty in the implementation of the QoS-based network configuration and management in traditional network architecture. We present a hierarchical software-defined network architecture for wireless sensor networks, which makes the complex network management possible and the system more adaptable. Furthermore, we propose a QoS-based routing protocol, called QSDN-WISE, which consists of a clustering algorithm, a routing algorithm, and local network maintenance. A double-cluster head-based uneven clustering algorithm, called DCHUC, avoids the energy hole phenomenon and reduces the workload of a single cluster head. The centralized QSDN-WISE routing algorithm constructs two heterogeneous forwarding paths for nodes, which meets the requirements for different data levels. Local network maintenance reduces the number of control messages in the network. The simulation results indicate that the QSDN-WISE can provide the QoS support for data with different requirements, balance the network energy consumption, and prolong the network's lifetime.

A Novel Low-Rate Denial of Service Attack Detection Approach in ZigBee Wireless Sensor Network by Combining Hilbert-Huang Transformation and Trust Evaluation

Low-rate Denial of Service (LDoS) attack is a special DoS attack. The routing protocol is vulnerable to many types of attacks in a wireless sensor network (WSN), which is an important network type of the Internet of Things (IoT). The novel LDoS attack to the routing protocol is proposed to evaluate the security and trust mechanism in the WSN. In fact, the LDoS attack is difficult to be detected due to its small-signal characteristics, so it is a serious threat to the security and trust of the WSN. A Hilbert-Huang transform (HHT) time-frequency joint analysis approach is utilized to analyze the non-stationary small signal that is produced by the LDoS attack. However, false intrinsic mode function (IMF) components are the challenge problems to precisely detect the LDoS attack. Correlation coefficient and Kolmogorov-Smirnov (KS) test approaches are united to evaluate the trustworthy of IMF components and exclude the false IMF components. Hilbert-Huang transformation and trust evaluation approaches are combined to detect the novel LDoS attack in Zigbee WSN. CC2530 system-on-chip integrated with ZigBee protocol is utilized to build a wireless sensor node. Random routing REQuest (RREQ) flooding attack is used to implement the routing layer LDoS attack in Zigbee WSN. If the correlation coefficient value of IMF component relative to original traffic is more than 0.3 and the KS similarity probability value of the IMF component relative to the original traffic is more than 0.4, the IMF component is identified as high trust IMF components that will be used to detect LDoS attack. If the IMF component only satisfies one of the trust evaluation conditions, the IMF component is identified as low trust IMF component. Otherwise, the IMF component is the false IMF component. We have proposed a scalable LDoS attack detection architecture for both WSN and IoT. The experimental results demonstrate that the novel approach is highly effective to detect the LDoS attack in the ZigBee WSN.

A low-complexity DWT module and CRS minimal instruction set computer architecture for wireless visual sensor networks

Transmitting cost, processing complexity and data security are three important elements in wireless visual sensor networks (WVSNs). This paper presents a complete low-complexity processing system that performs data compression, data correction and data encryption. In this system, the discrete wavelet transform (DWT) first decomposes the original image into DWT coefficients to ensure low transmitting cost, then the coefficients are encrypted using the Cauchy Reed Solomon CRS(20,16) coding scheme to ensure data security. A CRS minimal instruction set computer architecture with a DWT filtering module is proposed to perform the compression, encryption and error correction encoding in a low-complexity processing system. The proposed system is then implemented in a field programmable gated array (FPGA) to demonstrate the feasibility of the proposed system for implementation in WVSNs. Results on a Xilinx Spartan FPGA show that the proposed system requires a lower implementation complexity of 2536 slices as compared to other existing systems such as Crypto-Processor (4828 slices) and SPIHT CRS MISC (5017 slices).

A low-complexity DWT module and CRS minimal instruction set computer architecture for wireless visual sensor networks

A low-complexity DWT module and CRS minimal instruction set computer architecture for wireless visual sensor networks

Combined Reed-Solomon and Convolutional codes for IWSN based on IDWPT/DWPT Architecture

In contemporary industry, the wireless technologies trend is growing fast, because it will help to reduce cable cost, deployment time, flexibility, enabling wireless monitoring and control systems, and help to be more environment friendly, a huge research effort is done on wireless sensor networks (WSNs), however there are several issues facing the reliability of these wireless systems such that they can be used properly in harsh noisy or dynamic industrial environments, which led to outline the research direction for industrial wireless sensor networks (IWSNs). This article presents performances of a wavelet modulation and channel coding-based architecture of industrial wireless sensor network under an industrial channel. A model of the industrial channel is described, and performances of error correcting codes compared between multiple combinations of reed Solomon and convolutional codes for multiuser applications.

A New Approach for Noncontact Imaging Photoplethysmography Using Chrominance Features and Low-Rank in the IoT Operating Room

Advancements in image-processing and medical imaging technologies and other research areas have resulted in the growing importance of surgical navigation systems in the field of minimally invasive surgery. However, sophisticated auxiliary equipment is a prerequisite for full heart-rate monitoring in operative systems. A system puts forward to address this issue with a wireless sensor network architecture comprising acquisition modules, router gateways, remote servers, and a medical monitoring center. Factors such as variations in ambient light and face shaking can also easily affect heart rate detection based on face videos, thus resulting in inaccurate estimations of heart rate from the blood volume pulse (BVP) signals. This study proposes to address this concern by employing a novel method for non-contact heart rate estimation to overcome noise interference. First, chrominance features are selected to extract BVP signals, and the low-rank and sparse matrix decomposition methods are applied to overcome the detrimental effects of noise and interference while ensuring that valuable data are preserved.Next, the data are relayed to the server via the gateway. Finally, users can log on to the health-related cloud platform and gain information regarding their health status in real time. Experimental results reveal the advantages of the proposed technique over conventional face-based heart-rate estimation methods, including the capability to decrease dependence on sophisticated auxiliary equipment, avoid direct skin contact that can cause discomfort to patients undergoing surgery, and improve the comfort of surgical operations. Moreover, the proposed heart-rate measurement technique can contribute to improve the construction of smart cities.