Large Wireless Sensor Networks Research Articles

Design and Performance Analysis of Secure Multicasting Cooperative Protocol for Wireless Sensor Network Applications

This letter proposes a new security cooperative protocol, for dual phase amplify-and-forward large wireless sensor networks. In such a network, a portion of the ${K}$ relays can be potential eavesdroppers. The source agrees to share with the destination a given channel state information (CSI) of a source-trusted relay-destination link to encode the message. Then, in the first hop, the source will use this CSI to map the right message to a certain sector while transmitting fake messages to the other sectors using sectoral transmission. In the second hop, the relays retransmit their received signals to the destination, using the distributed beamforming technique. We derived the secrecy outage probability and demonstrated that the probability of receiving the right encoded information by an untrustworthy relay is inversely proportional to the number of sectors. We also showed that the aggressive behavior of the cooperating untrusted relays is not effective compared to the case where each untrusted relay is trying to intercept the transmitted message individually.

AREOR–Adaptive ranking based energy efficient opportunistic routing scheme in Wireless Sensor Network

Abstract Energy efficient and cluster based routing techniques are gaining popularity due to complexities associated with the power and maintenance factors of large Wireless Sensor Networks during transmission. An adaptive ranking based energy efficient opportunistic routing protocol- AREOR is researched in this paper. It identifies the most effective node to participate as a cluster head by using adaptive participatory criteria. A novel forwarder node selection method, based on adaptive ranking system is proposed. Node’s remaining energy and geographic position is utilized to compute the ranks. The proposed approach is verified through variable sized clustered networks to investigate the impact of opportunistic routing. Evaluation parameters such as Message Success Rate, Energy Consumption, End-to-End delay and Packet Delivery Ratio are used to demonstrate the effectiveness of the proposed energy optimization approach. AREOR reports reduction in energy consumption for the duration of the transmission. Adaptive ranking and energy optimal forwarder node selection has shown an edge over competitive peer techniques as per demonstrative simulations performed on realistic scenarios.

Magnetostrictive Energy Harvesting: Materials and Design Study

In recent years, vibrational energy harvesting has established itself as a promising alternative to the use of batteries for powering microelectromechanical systems for large wireless sensor networks used in aerospace and building infrastructures. This paper has focused on the design and materials used in magnetostrictive cantilever energy harvesters. The study involved using both finite-element modeling to predict the resonance frequencies for different cantilever designs and magnetostrictive materials, followed by experimental measurements for validation. Two different magnetostrictive ribbons were investigated, Fe100– x Ga x with four different compositions ( $x = 17.5$ ; 19.5; 21; 28 at.%) and amorphous metallic glass Metglas 2605SC (Fe81B13.5Si3.5C2). From the modeling, it was determined that the resonance frequency was strongly dependent on the cantilever length, thickness, and density. Changing the cantilever design to a “T” shape was found to decrease the resonance frequency. The experimental results found that the output voltage measured depended on the cantilever dimensions, especially the thickness, the Ga concentration, and the cantilever design. The output voltages for Fe80.5Ga19.5 cantilevers were comparable with the same dimension Metglas cantilevers. The results of the finite-element modeling were validated by good agreement between the computational and experimental resonance frequencies measured.

Enabling Optimization-Based Localization for IoT Devices

In this paper, we propose an embedded optimization approach for the localization of Internet of Things (IoT) devices making use of range measurements from ultra-wideband (UWB) signals. Low-cost, low-power UWB radios provide time-of-arrival measurements with decimeter accuracy over large distances. UWB-based localization methods have been envisioned to enable feedback control in IoT applications, particularly, in GPS-denied environments, and large wireless sensor networks. In this paper, we formulate the localization task as a nonlinear least-squares optimization problem based on two-way time-of-arrival measurements between the IoT device and several UWB radios installed in a 3-D environment. For the practical implementation of large-scale IoT deployments we further assume only approximate knowledge of the UWB radio locations. We solve the resulting optimization problem directly on IoT devices equipped with off-the-shelf microcontrollers using state-of-the-art code generation techniques for plug-and-play deployment of the nonlinear-programming algorithms. This paper further provides practical implementation details to improve the localization accuracy for feedback control in experimental IoT applications. The experimental results finally show that subdecimeter localization accuracy can be achieved using the proposed optimization-based approach, even when the majority of the UWB radio locations are unknown.

Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review.

During the past few years, Wireless Sensor Networks (WSNs) have become widely used due to their large amount of applications. The use of WSNs is an imperative necessity for future revolutionary areas like ecological fields or smart cities in which more than hundreds or thousands of sensor nodes are deployed. In those large scale WSNs, hierarchical approaches improve the performance of the network and increase its lifetime. Hierarchy inside a WSN consists in cutting the whole network into sub-networks called clusters which are led by Cluster Heads. In spite of the advantages of the clustering on large WSNs, it remains a non-deterministic polynomial hard problem which is not solved efficiently by traditional clustering. The recent researches conducted on Machine Learning, Computational Intelligence, and WSNs bring out the optimized clustering algorithms for WSNs. These kinds of clustering are based on environmental behaviors and outperform the traditional clustering algorithms. However, due to the diversity of WSN applications, the choice of an appropriate paradigm for a clustering solution remains a problem. In this paper, we conduct a wide review of proposed optimized clustering solutions nowadays. In order to evaluate them, we consider 10 parameters. Based on these parameters, we propose a comparison of these optimized clustering approaches. From the analysis, we observe that centralized clustering solutions based on the Swarm Intelligence paradigm are more adapted for applications with low energy consumption, high data delivery rate, or high scalability than algorithms based on the other presented paradigms. Moreover, when an application does not need a large amount of nodes within a field, the Fuzzy Logic based solution are suitable.

Sustainable and Efficient Data Collection in Cognitive Radio Sensor Networks

C ognitive Radio is a promising technology that maximize spectrum efficiency and can apply to Wireless Sensor Networks. This paper proposes a system architecture which introduces enhancements at lower layers of a Software Defined Network of Wireless Sensors Network with Cognitive Radio capabilities for efficient sensors’ power management, energy consuming channel handoffs elimination, efficient spectrum brokerage, and QoS provision in terms of data rate to the sensors’ applications via the SDN flows. The large Wireless Sensors Network is divided into clusters for power efficiency - as sensor operate in lower power - which connect to a cloud-assisted Central Controller. The protocol encompasses an optimal reinforcement learning scheme for efficient spectrum utilization that enables efficient sensors’ data collection, while sustainability issues are satisfied. Software Defined Wireless Sensor Network dynamically adapts to the spectrum and interference conditions on per flow basis and predicts Primary Users’ traffic to totally avoid collision with the licensed users. The paper is concentrated on sustainable solutions for sensors’ data collection by the cluster heads leveraging the Cognitive Radio Network facilities and taking into account the demands of the applications running on the sensors. The Cognitive Radio Sensor Network is considered as large organized on a local basis to extend networks lifetime and allow resource reuse.

Energy Balanced Two-level Clustering for Large-scale Wireless Sensor Networks based on the Gravitational Search Algorithm

Organizing sensor nodes in clusters is an effective method for energy preservation in a Wireless Sensor Network (WSN). Throughout this research work we present a novel hybrid clustering scheme that combines a typical gradient clustering protocol with an evolutionary optimization method that is mainly based on the Gravitational Search Algorithm (GSA). The proposed scheme aims at improved performance over large in size networks, where classical schemes in most cases lead to non-efficient solutions. It first creates suitably balanced multihop clusters, in which the sensors energy gets larger as coming closer to the cluster head (CH). In the next phase of the proposed scheme a suitable protocol based on the GSA runs to associate sets of cluster heads to specific gateway nodes for the eventual relaying of data to the base station (BS). The fitness function was appropriately chosen considering both the distance from the cluster heads to the gateway nodes and the remaining energy of the gateway nodes, and it was further optimized in order to gain more accurate results for large instances. Extended experimental measurements demonstrate the efficiency and scalability of the presented approach over very large WSNs, as well as its superiority over other known clustering approaches presented in the literature.

Moth Flame Optimization Algorithm Range-Based for Node Localization Challenge in Decentralized Wireless Sensor Network

Recently developments in wireless sensor networks (WSNs) have raised numerous challenges, node localization is one of these issues. The main goal in of node localization is to find accurate position of sensors with low cost. Moreover, very few works in the literature addressed this issue. Recent approaches for localization issues rely on swarm intelligence techniques for optimization in a multi-dimensional space. In this article, we propose an algorithm for node localization, namely Moth Flame Optimization Algorithm (MFOA). Nodes are located using Euclidean distance, thus set as a fitness function in the optimization algorithm. Deploying this algorithm on a large WSN with hundreds of sensors shows pretty good performance in terms of node localization. Computer simulations show that MFOA converge rapidly to an optimal node position. Moreover, compared to other swarm intelligence techniques such as Bat algorithm (BAT), particle swarm optimization (PSO), Differential Evolution (DE) and Flower Pollination Algorithm (FPA), MFOA is shown to perform much better in node localization task.

Spreading Aggregation: A distributed collision-free approach for data aggregation in large-scale wireless sensor networks

Abstract Recently, numerous works have shown that serial aggregation in large wireless sensor networks is scalable and very efficient, in terms of avoiding collisions and conserving energy and, more importantly, in terms of reducing response time. In this paper, a novel serial data aggregation approach, called Spreading Aggregation (SA), is proposed with the aim of shortening the traversal path and further reducing communications. First, given the fact that it is not based on a pre-established itinerary, SA is data structure maintenance-free and does not require any communications in this regard. Each time an aggregation process is launched, a new path is built, which decreases vulnerability to failure in links and nodes and allows the approach to handle topology changes. Second, SA is a localized approach that relies only on the one-hop neighbors table of each node to gradually construct the path, which makes it very scalable. A third interesting feature of SA is the merge of path construction and data processing. While the path is progressively constructed, data is simultaneously aggregated, saving a considerable amount of time and energy. In addition to all that, SA also saves energy and time due to its collision-free nature. In fact, in SA, only one packet is present in the entire network at any given time. In this paper, we formally prove the correctness of SA (i.e., free of looping and ensures the traversal of all connected nodes). Furthermore, the extensive OMNeT++ simulations, we performed, confirm that the proposed approach reduces communications, scales well in large networks, and conserves time and energy. The obtained results also show that SA outperforms state-of-the-art serial approaches.

Machine Learning Based Localization in Large-Scale Wireless Sensor Networks.

The rapid proliferation of wireless sensor networks over the past few years has posed some serious technical challenges to researchers. The primary function of a multi-hop wireless sensor network (WSN) is to collect and forward sensor data towards the destination node. However, for many applications, the knowledge of the location of sensor nodes is crucial for meaningful interpretation of the sensor data. Localization refers to the process of estimating the location of sensor nodes in a WSN. Self-localization is required in large wireless sensor networks where these nodes cannot be manually positioned. Traditional methods iteratively localize these nodes by using triangulation. However, the inherent instability in wireless signals introduces an error, however minute it might be, in the estimated position of the target node. This results in the embedded error propagating and magnifying rapidly. Machine learning based localizing algorithms for large wireless sensor networks do not function in an iterative manner. In this paper, we investigate the suitability of some of these algorithms while exploring different trade-offs. Specifically, we first formulate a novel way of defining multiple feature vectors for mapping the localizing problem onto different machine learning models. As opposed to treating the localization as a classification problem, as done in the most of the reported work, we treat it as a regression problem. We have studied the impact of varying network parameters, such as network size, anchor population, transmitted signal power, and wireless channel quality, on the localizing accuracy of these models. We have also studied the impact of deploying the anchor nodes in a grid rather than placing these nodes randomly in the deployment area. Our results have revealed interesting insights while using the multivariate regression model and support vector machine (SVM) regression model with radial basis function (RBF) kernel.

Efficient information gathering from large wireless sensor networks

Abstract Considering large WSNs and comparing data aggregation approaches in terms of scalability, robustness, completeness, and time/energy effectiveness, recent research has asserted the superiority of serial structure-free approaches over serial structure-based ones, and over both parallel structure-free and parallel structure-based techniques. But, in spite of the fact that serial structure-free approaches excel in large and medium-scale networks, their underlying path-construction algorithms are not optimal and can be improved by reducing the involved communications and further shortening the visiting path. To respond to this need, this paper presents Geometric Serial Search (GSS); a new serial structure-free algorithm specifically designed to efficiently gather information from large wireless resource-constrained networks. In addition to its completeness (i.e., visiting all nodes), collision-free nature, high scalability, energy/time efficiency, and robustness against topology changes (failures in links/nodes, …), the main advantage distinguishing GSS is that it considerably reduces communications and always approaches the optimal number of hops. More precisely, in GSS, no control packets or complex data structures are required, instead, one packet hops from node to node and explores the entire network. While gradually finding its way through the network, this packet interrogates nodes and collects their responses at the same time. The followed path is not established in advance, can stem from any node in the network, and requires only the one-hop neighborhood information of each traversed node to be gradually drawn. The obtained OMNeT++ simulation results presented in this paper demonstrate the efficiency of GSS and confirm all the previously cited claims.

Designing Human Assisted Wireless Sensor and Robot Networks Using Probabilistic Model Checking

Wireless sensor networks (WSNs) have a wide variety of applications in environment monitoring (such as air pollution and fire detection), industrial operations (such as machine surveillance), and precision agriculture. It is an arduous task to manage a large WSN as constant monitoring is required to keep it operational. Mobile robots are used to deploy, manage, and perform various application specific tasks in WSNs. However, a fully autonomous robot lacks the ability of proper decision-making in complex situations such as network coverage in disastrous areas. A remote human operator can assist the robot in improved decision-making, specially in odd situations that arise due to either inherent application needs or changes in the environment. In addition to the complexity of WSN managed by a robot, analyzing the effect of human operator in managing WSN poses further challenge. This is due to the fact that the performance of a human operator is also influenced by internal (such as fatigue) as well as external (such as workload conditions) factors. In this paper, we use probabilistic model checking to analyze the performance of robot assisted WSN. This study enables WSN administrators to analyze and plan WSN management before the actual deployment of robot and sensors in the field. Given specific application requirements, we are able to examine key parameters such as size of the network, number of sensors needed to keep the network operational, and time to service the farthest location. With the help of remote human operator, we introduce several degrees of autonomy to the mobile robot managing WSN. Markov decision process is used to capture uncertainties and imperfections in the human−robot interactions. We demonstrate the benefits obtained due to intelligent decision-making by a realistic human operator whose performance is affected by both external and internal factors. We demonstrate the applicability of our approach via detailed case studies in planning and managing WSNs.

An effective Bat algorithm for node localization in distributed wireless sensor network

Wireless sensor networks (WSNs) have recently been extensively investigated due to their numerous applications in processes that have to be spread over a large area. One of the important issues of WSN is node localization; node localization capability is highly desirable for the performance evaluation in monitoring applications. Localization is defined as estimating the locations of sensors with initially unknown location information as most sensors do not know their locations due to the cost and size of sensors. The main objective of localization is to find the nodes' positions in a short time with a low energy cost; for this reason, recent approaches relying on swarm intelligence techniques are utilized, and node localization is considered an optimization problem in a multidimensional space. Recently, the meta‐heuristic Bat algorithm was proposed as a solution for the node localization problem. This paper proposes an effective Bat algorithm for the node localization problem, the effectiveness of which is based on the adaptation of velocity of the Bats by hybridization, with Doppler effect for improving the performance, aptly termed Dopeffbat. Hence, Dopeffbat computes (through evolution) the nodes' positions iteratively through the Euclidian distance as fitness. Deploying this algorithm on a large WSN with hundreds of sensors demonstrates decent performance in terms of node localization. Moreover, the Dopeffbat parameters are simulated and interpreted in different scenarios of simulation; in addition, a comparative study was performed to further demonstrate the performance of the proposed algorithm, and the simulation results prove that Dopeffbat has a high convergence rate and greater precision compared with the original Bat algorithm and particle swarm optimization (PSO) algorithms.

Recovery from simultaneous failures in a large scale wireless sensor network

Abstract The Wireless sensor networks (WSNs) become more and more recognized these recent years and their applications spread in several domains. In general, these applications require that the network presents a minimum degree of reliability, effectiveness and robustness. However, the specificity of the nodes used in this type of networks makes them prone to failures. Indeed, the multichannel communications are generally privileged in the interference context which is very frequent in several WSNs’ applications due to the density of sensors and the harsh environment in which they are deployed. Moreover, in real applications, the occurrence of some faults (for example fire) may alter an entire zone of the network especially in the case of large scale deployment. Therefore, in this paper, we propose a network fault recovery approach from simultaneous failures in a large scale multichannel WSN. To make our solution as realistic as possible, we consider the case of the precision agriculture application and propose a detailed deployment of the WSN for that application. The choice of precision agriculture application is motivated by the fact that this application require large scale WSN (thousands and thousands of sensors) to supervise such a large area. Based on such precision agriculture scenario, we propose our fault recovery approach, called Simultaneous Failure Recovery based on Relay Node Relocation (SFR-RNR), that aims to restore the connectivity and partially the coverage in the network. The performance of the proposed approach is evaluated by simulation.

Awa: Using water distribution systems to transmit data

AbstractThis article presents Awa, a real‐time wireless monitoring solution for urban water distribution systems (WDSs). The Awa system is composed of a distributed network of low‐cost sensor nodes that can be quickly installed on water pipes. Rather than using conventional radio signals, which are attenuated in underground environments, the nodes use the actual water‐filled pipe as the transmission medium to communicate with one another. This permits the use of an already existing water infrastructure in the deployment of large wireless sensor networks. We describe the development and evaluation of novel and battery‐powered sensor nodes that can be magnetically clipped to valves and hard‐to‐access points in WDSs without requiring digging or cutting of pipes. A channel characterization is carried out and a number of data modulation schemes are evaluated across a 110‐m section of pipe in an operational WDSs. We identify a near‐optimal frequency (near 500 Hz) for transmitting data. We demonstrate communication at 100 bps across a real‐world water pipe using amplitude modulation. Not unlike radio‐frequency wireless, we also measure the highly nonstationary effects of multipath fading. The article also contains an in‐depth discussion about the opportunities that this emerging communication technology offers in the context of city‐wide leak detection and flow monitoring.

Load Balanced Coverage with Graded Node Deployment in Wireless Sensor Networks

In this paper, to gather streams of data in static wireless sensor networks, a novel graded node deployment strategy is proposed that generates minimum traffic, just sufficient for coverage. Based on this node distribution, a distributed, nearly load-balanced data gathering algorithm is developed to deliver packets to the sink node via minimum-hop paths that also in turn helps to limit the network traffic. An average case probabilistic analysis is done based on perfect matching of random bipartite graphs to establish a theoretical lower bound on the number of nodes to be deployed. Analysis and simulation studies show that the proposed model results huge enhancement in network lifetime that significantly overrides the cost due to over deployment. Hence, this technique offers an excellent cost-effective and energy-efficient solution for node deployment and routing in large wireless sensor networks to operate with prolonged lifetime.

A Formal Methodology to Design and Deploy Dependable Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) are being increasingly adopted in critical applications, where verifying the correct operation of sensor nodes is a major concern. Undesired events may undermine the mission of the WSNs. Hence, their effects need to be properly assessed before deployment, to obtain a good level of expected performance; and during the operation, in order to avoid dangerous unexpected results. In this paper, we propose a methodology that aims at assessing and improving the dependability level of WSNs by means of an event-based formal verification technique. The methodology includes a process to guide designers towards the realization of a dependable WSN and a tool (“ADVISES”) to simplify its adoption. The tool is applicable to homogeneous WSNs with static routing topologies. It allows the automatic generation of formal specifications used to check correctness properties and evaluate dependability metrics at design time and at runtime for WSNs where an acceptable percentage of faults can be defined. During the runtime, we can check the behavior of the WSN accordingly to the results obtained at design time and we can detect sudden and unexpected failures, in order to trigger recovery procedures. The effectiveness of the methodology is shown in the context of two case studies, as proof-of-concept, aiming to illustrate how the tool is helpful to drive design choices and to check the correctness properties of the WSN at runtime. Although the method scales up to very large WSNs, the applicability of the methodology may be compromised by the state space explosion of the reasoning model, which must be faced by partitioning large topologies into sub-topologies.

A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks.

Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude.

Information Driven Data Gathering for Energy Efficient Wireless Sensor Network

Large scale dense Wireless Sensor Networks (WSNs) have been progressively employed for different classes of applications for the resolve of precise monitoring. As a result of high density of nodes, both spatially and temporally correlated information can be detected by several nodes. Hence, energy can be saved which is a major aspect of these networks. Moreover, by using these advantages of correlations, communication and data exchange can be reduced. In this paper, a novel algorithm that selects the data based on their contextual importance is proposed. The data, which are contextually important, are only transmitted to the upper layer and the remains are ignored. In this way, the proposed method achieves significant data reduction and in turn improves the energy conservation of data gathering.

Energy-efficient strategies for wireless sensor networks

Design of large scale wireless sensor networks WSN has become cost effective with the advances in inexpensive sensor technology and wireless communications and attracts the attention of a wide range of applications such as health and environmental monitoring and battlefields surveillance. Wireless Sensor Networks (WSNs) consists of huge number of Sensor Nodes (SNs) with sensing, communication and processing capabilities. SNs have limited energy supply, storage and computational capacity. In recent years energy efficient computation is a major concern in WSN. The critical aspects include reduction in the energy consumption of SNs so that the network lifetime can be extended to reasonable times. For this purpose many novel innovative techniques based on energy efficient computation have been proposed. In this paper, we present a brief analysis on energy efficient computation protocols. We have also presented a comparison of these protocols The main challenge in the design of wireless sensor networks is the limited battery power of the sensors and the difficulty of replacing and/or recharging these batteries due to the nature of the monitoring field and cost to ease this problem it is necessary that the sensors be densely deployed and appropriate protocols be designed in order to identify this redundancy while maximizing the lifetime of the network. Several protocols have been proposed in the literature with a goal to save the sensors energy. All those proposed algorithms aim to design energy efficient protocol for WSN. However these protocols consider the various issues of WSN separately.