Dense Wireless Sensor Networks Research Articles

Evaluation of Clustering Parameters in WSN Fields Using Distributed Zone-Based Approach

Abstract Wireless sensor network (WSN) is a low-powered network formed by the sensor nodes that finds application in civilian, military, visual sense models, and many others. Improved network lifetime is an important task to be achieved by these sensor networks. In this paper, a methodology for evaluation of clustering efficiency, routing efficiency, energy efficiency, and the lifetime of two dense wireless sensor network fields—using a distributed clustering approach, the hybrid energy efficient clustering algorithm (HEECA)—was proposed, which mainly targeted effectively prolonging the lifetime of wireless sensor networks. It is a well-distributed and energy-efficient clustering algorithm that employs three novel techniques: zone based transmission power (ZBTP), routing using distributed relay nodes (DRNs), and rapid cluster formation (RCF). The proposed scheme was compared with two well-evaluated existing distributed clustering algorithms, O-LEACH and HEED. Simulation results clearly showed an excellent improvement in remaining energy, throughput, routing efficiency, and energy efficiency of the entire wireless sensor system. The clustering process was effectively controlled, thereby the number of cluster heads selection and the number of packets delivered to the base station was also found to be effective. Ultimately, the overall lifetime of the wireless sensor network was improved when compared to the two existing distributed clustering algorithms.

The self-configuration of nodes using RSSI in a dense wireless sensor network

Wireless sensor networks (WSNs) may be made of a large amount of small devices that are able to sense changes in the environment, and communicate these changes throughout the network. An example of a similar network is a photo voltaic (PV) power plant, where there is a sensor connected to each solar panel. The task of each sensor is to sense the output of the panel which is then sent to a central node for processing. As the network grows, it becomes impractical and even impossible to configure all these nodes manually. And so, the use of self-organization and auto-configuration algorithms becomes essential. In this paper, three algorithms are proposed that allow nodes in the network to automatically identify their closest neighbors, relative location in the network, and select which frequency channel to operate in. This is done using the value of the Received Signal Strength Indicator (RSSI) of the messages sent and received during the setup phase. The performance of these algorithms is tested by means of both simulations and testbed experiments. Results show that the error in the performance of the algorithms decreases as we increase the number of RSSI values used for decision making. Additionally, the number of nodes in the network affects the setup error. However, the value of the error is still acceptable even with a high number of nodes.

Building structural health monitoring using dense and sparse topology wireless sensor network

Wireless sensor technology has been opened up numerous opportunities to advanced health and maintenance monitoring of civil infrastructure. Compare to the traditional tactics, it offers a better way of providing relevant information regarding the condition of building structure health at a lower price. Numerous domestic buildings, especially longer-span buildings have a low frequency response and challenging to measure using deployed numbers of sensors. The way the sensor nodes are connected plays an important role in providing the signals with required strengths. Out of many topologies, the dense and sparse topologies wireless sensor network were extensively used in sensor network applications for collecting health information. However, it is still unclear which topology is better for obtaining health information in terms of greatest components, node\'s size and degree. Theoretical and computational issues arising in the selection of the optimum topology sensor network for estimating coverage area with sensor placement in building structural monitoring are addressed. This work is an attempt to fill this gap in high-rise building structural health monitoring application. The result shows that, the sparse topology sensor network provides better performance compared with the dense topology network and would be a good choice for monitoring high-rise building structural health damage.

Analytical Event Based Investigations Over Delphi Random Generator Distributions for Data Dissemination Routing Protocols in Highly Dense Wireless Sensor Network

This paper focuses on the event based investigations over different data dissemination routing protocols for highly dense wireless sensor networks. Initially for the wireless sensor network domain, we analyzed data dissemination flooding and gossiping routing protocols. We etude and exemplify our proposed model for data dissemination based evaluation with Delphi random generator distribution strategy. We calculated performance metrics as sense count, transmit count, receive count and receive redundant count. At the end, simulations analysis has been carried out to prove the validity of our designed scenario.

Estimating the Spatiotemporal Evolution Characteristics of Diffusive Hazards Using Wireless Sensor Networks

There is a fast growing interest in exploiting wireless sensor networks (WSNs) for tracking the boundaries and predicting the evolution properties of diffusive hazardous phenomena (e.g. wildfires, oil slicks etc.) often modeled as “continuous objects”. We present a novel distributed algorithm for estimating and tracking the local evolution characteristics of continuous objects. The hazard’s front line is approximated as a set of line segments, and the spatiotemporal evolution of each segment is modeled by a small number of parameters (orientation, direction and speed of motion). As the hazard approaches, these parameters are re-estimated using ad-hoc clusters (triplets) of collaborating sensor nodes. Parameters updating is based on algebraic closed-form expressions resulting from the analytical solution of a Bayesian estimation problem. Therefore, it can be implemented by microprocessors of the WSN nodes, while respecting their limited processing capabilities and strict energy constraints. Extensive computer simulations demonstrate the ability of the proposed distributed algorithm to estimate accurately the evolution characteristics of complex hazard fronts under different conditions by using reasonably dense WSNs. The proposed in-network processing scheme does not require sensor node clocks synchronization and is shown to be robust to sensor node failures and communication link failures, which are expected in harsh environments.

Dense Clustered Multi-Channel Wireless Sensor Cloud

Dense Wireless Sensor Network Clouds have an inherent issue of latency and packet drops with regards to data collection. Though there is extensive literature that tries to address these issues through either scheduling, channel contention or a combination of the two, the problem still largely exists. In this paper, a Clustered Multi-Channel Scheduling Protocol (CMSP) is designed that creates a Voronoi partition of a dense network. Each partition is assigned a channel, and a scheduling scheme is adopted to collect data within the Voronoi partitions. This scheme collects data from the partitions concurrently and then passes it to the base station. CMSP is compared using simulation with other multi-channel protocols like Tree-based Multi-Channel, Multi-Channel MAC and Multi-frequency Media Access Control for wireless sensor networks. Results indicate CMSP has higher throughput and data delivery ratio at a lower power consumption due to network partitioning and hierarchical scheduling that minimizes load on the network.

Fuzzy-Based Adaptive Countering Method against False Endorsement Insertion Attacks in Wireless Sensor Networks

Wireless sensor networks (WSNs) are vulnerable to false endorsement insertion attacks (FEIAs), where a malicious adversary intentionally inserts incorrect endorsements into legitimate sensing reports in order to block notifications of real events. A centralized solution can detect and adaptively counter FEIAs while conserving the energy of the forwarding nodes because it does not make the nodes verify reports using cryptographic operations. However, to apply this solution to a WSN, the users must carefully select 10 or more security parameters, which are used to determine the occurrences of FEIAs. Thus, an inappropriate choice of a single parameter might result in the misinterpretation of or misdetection of FEIAs. Therefore, the present study proposes a fuzzy-based centralized method for detecting and adaptively countering FEIAs in dense WSNs, where two fuzzy rule-based systems are used to detect an FEIA and to select the most effective countermeasure against the FEIA. A major benefit of the proposed method is that the fuzzy systems can be optimized automatically by combining a genetic algorithm and a simulation. Thus, users only need to write a model of the WSN to apply the proposed method to a WSN. The improved performance with this method is demonstrated by simulation results.

Application of Harmony Search Optimization Algorithm to Improve Connectivity in Wireless Sensor Network with Non-uniform Density

Low connectivity is a challenging problem in wireless communication sensor networks with non-uniform sensor node densities. Reliable information transmission requires the full connectivity of the network because of the multi-hop communication of wireless sensor networks. A network is fully connected if each pair of nodes can communicate with each other, either directly or through intermediate relay nodes. The current study presents a Harmony Search (HS) algorithm to improve the connectivity of the non-uniform density wireless sensor networks. HS is a meta-heuristic algorithm, which mimics the actions of harmonizing musical instruments, including memory-based, random, and pitch-adjusted play, during improvisation. In the present work, HS is used to optimize the phase shift parameters of sensors node inside the cluster to ensure maximum radiation field at the reception point. This process results in a significant increase in the coverage area of cluster nodes, consequently increasing network connectivity.

Connected k-Coverage Topology Control for Area Monitoring in Wireless Sensor Networks

One of the design challenges in Wireless sensor network (WSN) is to save limited energy resource to prolong the lifetime of the network without affecting their communication connectivity or sensing coverage . In dense WSN, the sensing areas of sensor node may overlap with each other. In general, the larger the overlap of the sensing range, the more redundant data will be generated with more energy consumption. In WSN, connected dominating set (CDS) has been used for energy efficient topology control, where a virtual backbone is formed, which allows communication between any arbitrary pair of nodes in the network. Nodes which are not in CDS can go to sleep state for energy conservation. Applications related to security and reliability require a certain amount of active nodes to ensure desired level of coverage at all time. These active nodes should be connected to reduce the delay during the communication. Therefore, it is necessary to construct a fault-tolerant CDS that continues to function during node or link failure, for both coverage and connectivity requirements of WSN. In this paper, we propose k-coverage connected dominating set (k-CCDS) for connected area coverage. We also propose a weight metric named weighted coverage cost (WCC), which consists of energy of sensing neighbors. The proposed protocol selects a CDS based on WCC to ensure that every non-dominating node is connected with the CDS even if $$k-1$$k-1 dominating nodes are dead. This CDS is named k-CCDS, in which every non-dominating node is covered by atleast k dominating nodes. Simulation results show that it performs better when comparing with A3Cov in terms of CDS size, coverage, energy consumption and lifetime of the network.

Cross-Layer Design of Distributed Sensing-Estimation With Quality Feedback— Part I: Optimal Schemes

This two-part paper presents a feedback-based cross-layer framework for distributed sensing and estimation of a dynamic process by a wireless sensor network (WSN). Sensor nodes wirelessly communicate measurements to the fusion center (FC). Cross-layer factors such as packet collisions and the sensing-transmission costs are considered. Each SN adapts its sensing-transmission action based on its own local observation quality and the estimation quality feedback from the FC under cost constraints for each SN. In this first part, the optimization complexity is reduced by exploiting the statistical symmetry and large network approximation of the WSN. Structural properties of the optimal policy are derived for a coordinated and a decentralized scheme. It is proved that a dense WSN provides sensing diversity, so that only a few SNs with the best local observation quality need to be activated, despite the fluctuations of the WSN. The optimal policy dictates that, when the estimation quality is poor, only the best SNs activate, otherwise all SNs remain idle to preserve energy. The costs of coordination and feedback are evaluated, revealing the scalability of the decentralized scheme to large WSNs, at the cost of performance degradation. Simulation results demonstrate cost savings from 30% to 70% over a non-adaptive scheme, and significant gains over a previously proposed estimator which does not consider these cross-layer factors.

Autonomous Deployment for Load Balancing <inline-formula> <tex-math notation="TeX">$k$</tex-math></inline-formula>-Surface Coverage in Sensor Networks

Although the problem of $k$ -area coverage has been intensively investigated for dense wireless sensor networks (WSNs), how to arrive at a $k$ -coverage sensor deployment that optimizes certain objectives in relatively sparse WSNs still faces both theoretical and practical difficulties. Moreover, only a handful of centralized algorithms have been proposed to elevate 2-D area coverage to 3-D surface coverage. In this paper, we present a practical algorithm, i.e., the Autonomous dePlOyment for Load baLancing $k$ -surface cOverage (APOLLO), to move sensor nodes toward $k$ -surface coverage, aiming at minimizing the maximum sensing range required by the nodes. APOLLO enables purely autonomous node deployment as it only entails localized computations. We prove the termination of the algorithm and the (local) optimality of the output. We also show that our optimization objective is closely related to other frequently considered objectives for 2-D area coverage. Therefore, our practical algorithm design also contributes to the theoretical understanding of the 2-D $k$ -area coverage problem. Finally, we use extensive simulation results to both confirm our theoretical claims and demonstrate the efficacy of APOLLO.

Aggregation Capacity of Wireless Sensor Networks: Extended Network Case

A critical function of wireless sensor networks (WSNs) is data gathering. One is often only interested in collecting a specific function of the sensor measurements at a sink node, rather than downloading all the raw data from all the sensors. In this paper, we study the capacity of computing and transporting the specific functions of sensor measurements to the sink node, called aggregation capacity, for WSNs. We focus on random WSNs that can be classified into two types: random extended WSN and random dense WSN. All existing results about aggregation capacity are studied for dense WSNs, including random cases and arbitrary cases, under the protocol model (ProM) or physical model (PhyM). In this paper, we propose the first aggregation capacity scaling laws for random extended WSNs. We point out that unlike random dense WSNs, for random extended WSNs, the assumption made in ProM and PhyM that each successful transmission can sustain a constant rate is over-optimistic and unpractical due to transmit power limitation. We derive the first result on aggregation capacity for random extended WSNs under the generalized physical model. Particularly, we prove that, for the type-sensitive divisible perfectly compressible functions and type-threshold divisible perfectly compressible functions, the aggregation capacities for random extended WSNs with ${\mbi {n}}$ nodes are of order $\Thetab ({{{({\bf log} {\mbi {n}})}^{ - {\alphab \over {\bf 2}} - {\bf 1}}}})$ and $\Theta ({{{{{({\bf log} {\mbi {n}})}^{ - \alphab /{\bf 2}}}} \over {{\bf log}{\bf log} {\mbi {n}}}}})$ , respectively, where $\alphab \gt {\bf 2}$ denotes the power attenuation exponent in the generalized physical model. Furthermore, we improve the aggregation throughput for general divisible perfectly compressible functions to $\Omegab ({{{({\bf log} {\mbi {n}})}^{ - {\alphab \over {\bf 2}}}}})$ by choosing $\Thetab ({\bf log}\; {\mbi {n}})$ sensors from a small region (relative to the whole region) as sink nodes.

USING A WIRELESS SENSOR NETWORK TO DETERMINE CLIMATE HETEROGENEITY OF A GREENHOUSE ENVIRONMENT

One of the main targets of a greenhouse control system is to generate a uniform climate environment, which has obvious economic advantages, due to homogenous crop, lesser diseases and the potential energy saving. By using a dense wireless sensor network, the spatial distribution of climate parameters and thus the uniformity of greenhouse environment can be determined. This information can be used by the growers to detect cold and wet spots in greenhouse environment and to adjust their climate control system. In this study, the horizontal climate heterogeneity of the greenhouse environment is analysed. Six experiments were performed in commercial greenhouses observing the micro-climate with 100 wireless sensors under different conditions and for different crops. From the data the spatial and temporal distribution of air temperature T and relative humidity RH was obtained. Instantaneous spatial differences varied up to ±5°C for T and ±20% for RH, depending on location, time of day and season. Significant differences of ±2°C for T and ±12.5% for RH, while averaging data over longer periods (5 days), have been observed. Further research is needed to investigate the way the information regarding the spatial and temporal distribution of climatic parameters can be integrated in the control systems and finally to quantify the energy saving potential.

Accuracy vs. Resolution in Radio Tomography

Radio tomographic imaging (RTI) has recently been proposed for tracking object location via radio waves without requiring the objects to transmit or receive radio signals. The position is extracted by inferring which voxels are obstructing the various radio links in a dense wireless sensor network. This paper derives an analytic expression for the image accuracy (error per voxel) for each of 5 published RTI system models. The formulae show the effects of weight model choice, voxel size, number of sensors, and degree of regularization on the Cramer-Rao Lower Bound (CRLB). This enables analysis of the tradeoffs between these parameters in system design, particularly between image accuracy and image resolution. The theoretical results agree well with simulations, and the new theory is used to interpret an experimental scenario.

Energy-Efficient Node Selection Algorithms with Correlation Optimization in Wireless Sensor Networks

The sensing data of nodes is generally correlated in dense wireless sensor networks, and the active node selection problem aims at selecting a minimum number of nodes to provide required data services within error threshold so as to efficiently extend the network lifetime. In this paper, we firstly propose a new Cover Sets Balance (CSB) algorithm to choose a set of active nodes with the partially ordered tuple (data coverage range, residual energy). Then, we introduce a new Correlated Node Set Computing (CNSC) algorithm to find the correlated node set for a given node. Finally, we propose a High Residual Energy First (HREF) node selection algorithm to further reduce the number of active nodes. Extensive experiments demonstrate that HREF significantly reduces the number of active nodes, and CSB and HREF effectively increase the lifetime of wireless sensor networks compared with related works.

Cluster-Based Localisation Method for Dense WSN: A Distributed Balance between Accuracy and Complexity Fixed by Cluster Size

Localisation is a fundamental requirement for a monitoring and tracking system based on wireless sensor networks (WSN). In order to build an accurate set of measurements, sensor nodes must have information regarding their own position within a system of coordinates. When a considerable number of nodes are randomly scattered over a monitoring area, sensor nodes must be part of a self-organised system which provides a set of local position estimates. Nodes participate under very stringent conditions, for example, limited power supply and reduced computational capabilities. This work presents a GPS-free localisation method consisting of four stages that are executed only once during the network initialisation process. These stages are aimed to increase the overall system lifetime by reducing the signalling overhead commonly involved in distributed localisation procedures. The proposed localisation method turns the initial and complex node deployment to several smaller instances by dividing the network into clusters, which can be solved simultaneously based on local resources only. Simulation results show that this approach produces important savings in the involved overall complexity, which can translate into a trade-off between computational cost and localisation accuracy.

DYNAMIC SELECTION OF CLUSTER HEAD IN IN-NETWORKS FOR ENERGY MANAGEMENT

In this project, we presented Multipath Region Routing (MRR) protocol for energy conservation in Wireless Sensor Networks (WSNs). Large scale dense WSNs are used in different types of applications for accurate monitoring. Energy conservation is an important issue in WSNs. In order to save energy, Multipath Region Routing protocol is used which provides balance in energy consumption and sustains the network life-span. By using this method, we can reduce the number of energy dissipation because the cluster head will collect data directly from other nodes. Hence, the energy can be preserved and network life time is extended to reasonable time.

Radio Tomography for Roadside Surveillance

Radio tomographic imaging (RTI) has recently been proposed for tracking object location via radio waves without requiring the objects to transmit or receive radio signals. The position is extracted by inferring which voxels are obstructing a subset of radio links in a dense wireless sensor network. This paper proposes a variety of modeling and algorithmic improvements to RTI for the scenario of roadside surveillance. These include the use of a more physically motivated weight matrix, a method for mitigating negative (aphysical) data due to noisy observations, and a method for combining frames of a moving vehicle into a single image. The proposed approaches are used to show improvement in both imaging (useful for human-in-the-loop target recognition) and automatic target recognition in a measured data set.

A Bayesian Approach to Device-Free Localization: Modeling and Experimental Assessment

Device-free positioning allows to localize and track passive targets (i.e., not carrying any electronic device) moving in an area monitored by a dense network of low-power and battery-operated wireless sensors. The technology is promising for a wide number of applications, ranging from ambient intelligence in smart spaces, intrusion detection, emergency and rescue operations in critical areas. In this paper, a new approach is proposed where both the average path-loss and the fluctuations of the received signal strength induced by the moving target are jointly modeled based on the theory of diffraction. A novel stochastic model is derived and used for the evaluation of fundamental performance limits. The model is proved to be tight enough to be adopted for real-time estimation of the target location. The proposed localization system is validated by extensive experimental studies in both indoor and outdoor environments. The model calibration is addressed in practical scenarios to compare the performance of different Bayesian online localization methods. The test-bed system supports efficient and flexible target tracking, without requiring any action from the end-users. In addition, the technology is proven to be readily applicable over the existing IEEE 802.15.4 compliant PHY layer standard, by adapting the low-level MAC firmware.

An Empirical Evaluation of Lightweight Random Walk Based Routing Protocol in Duty Cycle Aware Wireless Sensor Networks

Energy efficiency is an important design paradigm in Wireless Sensor Networks (WSNs) and its consumption in dynamic environment is even more critical. Duty cycling of sensor nodes is used to address the energy consumption problem. However, along with advantages, duty cycle aware networks introduce some complexities like synchronization and latency. Due to their inherent characteristics, many traditional routing protocols show low performance in densely deployed WSNs with duty cycle awareness, when sensor nodes are supposed to have high mobility. In this paper we first present a three messages exchange Lightweight Random Walk Routing (LRWR) protocol and then evaluate its performance in WSNs for routing low data rate packets. Through NS-2 based simulations, we examine the LRWR protocol by comparing it with DYMO, a widely used WSN protocol, in both static and dynamic environments with varying duty cycles, assuming the standard IEEE 802.15.4 in lower layers. Results for the three metrics, that is, reliability, end-to-end delay, and energy consumption, show that LRWR protocol outperforms DYMO in scalability, mobility, and robustness, showing this protocol as a suitable choice in low duty cycle and dense WSNs.