Connectivity In Sensor Networks Research Articles

Distribution of Cell Area in Bounded Poisson Voronoi Tessellations with Application to Secure Local Connectivity

Poisson Voronoi tessellations have been used in modeling many types of systems across different sciences, from geography and astronomy to telecommunications. The existing literature on the statistical properties of Poisson Voronoi cells is vast, however, little is known about the properties of Voronoi cells located close to the boundaries of a compact domain. In a domain with boundaries, some Voronoi cells would be naturally clipped by the boundary, and the cell area falling inside the deployment domain would have different statistical properties as compared to those of non-clipped Voronoi cells located in the bulk of the domain. In this paper, we consider the planar Voronoi tessellation induced by a homogeneous Poisson point process of intensity $\lambda\!>\!0$ in a quadrant, where the two half-axes represent boundaries. We show that the mean cell area is less than $\lambda^{-1}$ when the seed is located exactly at the boundary, and it can be larger than $\lambda^{-1}$ when the seed lies close to the boundary. In addition, we calculate the second moment of cell area at two locations for the seed: (i) at the corner of a quadrant, and (ii) at the boundary of the half-plane. We illustrate that the two-parameter Gamma distribution, with location-dependent parameters calculated using the method of moments, can be of use in approximating the distribution of cell area. As a potential application, we use the Gamma approximations to study the degree distribution for secure connectivity in wireless sensor networks deployed over a domain with boundaries.

Reliable network connectivity in wireless sensor networks for remote monitoring of landslides

Providing reliable communications in a remotely monitored large-scale deployment of Wireless Sensor Networks is a challenging task. In this paper, we deal with such a deployment to monitor a landslide prone zone where the nodes sense geological attributes essential for early warning. The deployment area is a hilly mountain with different demographic characteristics. Establishing network connectivity in this deployment site for real-time streaming of sensor data involves dealing with site specific challenges such as asymmetric links, dynamic network conditions due to rough weather, inadequate solar power, network fail-over and re-connection problem etc. Our deployment makes use of only a few number of relay nodes for connecting the wireless sensor network to a field management center through an IoT gateway. The field management center makes use of multiple fault-tolerant WAN networks to relay the data to a remote central data management center for deep data analysis and for generating early warnings prior to a catastrophic event. This paper provides insights into our experiences from successful deployment of a WSN system and reports real-time measurements taken in the field for ensuring network reliability. Evaluation of our system reflects that the network is able to provide reliable communication by providing a fail-over for streaming data with less than a minute to re-establish the connection and the gateway software is capable of handling heterogeneous sensor readings at a rate up-to 1700/s within a latency of 10 s while delivering the data to the data center. The system also achieves highly accurate time synchronization in the order of microseconds throughout the network.

Optimizing Maximum Monitoring Frequency and Guaranteeing Target Coverage and Connectivity in Energy Harvesting Wireless Sensor Networks

Improving the quality of monitoring and guaranteeing target coverage and connectivity in energy harvesting wireless sensor networks (EH-WSNs) are important issues in near-perpetual environmental monitoring. Existing solutions only focus on the utility of coverage or energy efficient coverage by considering target connectivity for battery-powered WSNs. This paper focuses on optimizing the maximum monitoring frequency with guaranteed target coverage and connectivity in EH-WSNs. We first analyzed the factors affecting monitoring quality and the energy harvesting model. Thereafter, we presented the problem formulation and proposed the algorithm for maximizing monitoring frequency and guaranteeing target coverage and connectivity (MFTCC) that is based on graph theory. Furthermore, we presented the corresponding distributed implementation approach. On the basis of the existing energy harvesting prediction model, expensive simulations show that the proposed MFTCC algorithm achieves high average maximum monitoring frequency and energy usage ratio. Moreover, it obtains a higher throughput than existing target monitoring methods.

Firefly Algorithm for Intelligent Context-Aware Sensor Deployment Problem in Wireless Sensor Network

Wireless sensor networks (WSNs) provide acceptable low cost and efficient deployable solutions to execute the target tracking, checking and identification of substantial measures. The primary step necessary for WSN is to organize all the sensor nodes in their positions to build up an effective network. In the sensor deployment model, Target COVerage (TCOV) and Network CONnectivity (NCON) are the basic issues in WSNs that have obtained significant consideration in sensor deployment. This paper intends to develop an intelligent context awareness algorithm for sensor deployment process in WSN. Accordingly, the process is divided into two phases. In the first phase, the TCOV process is performed, whereas the second phase of the algorithm establishes NCON among the sensors. An objective model to meet both TCOV and NCON is formulated as a minimization problem. The problem is solved using FireFly (FF) optimization to determine the optimal locations for sensors. It leads to an intelligent sensor deployment model that can determine the optimal locations for the sensors in the WSN. Further, the proposed FF-TCOV and FF-NCON models are compared against the conventional algorithms, namely, genetic algorithm, particle swarm optimization, artificial bee colony, differential evolution and evolutionary algorithm-based TCOV and NCON models. The results achieved from the simulation show the improved performance of the proposed technique.

$k$ -Connectivity of Inhomogeneous Random Key Graphs With Unreliable Links

We consider secure and reliable connectivity in wireless sensor networks that utilize the heterogeneous random key predistribution scheme. We model the unreliability of wireless links by an on/off channel model that induces an Erdős-Renyi graph, while the heterogeneous scheme induces an inhomogeneous random key graph. The overall network can thus be modeled by the intersection of both graphs. We present conditions (in the form of zero-one laws) on how to scale the parameters of the intersection model, so that with high probability: i) all of its nodes are connected to at least ${k}$ other nodes, i.e., the minimum node degree of the graph is no less than $ {k}$ , and ii) the graph is $ {k}$ -connected, i.e., the graph remains connected even if any $ {k} - {1}$ nodes leave the network. These results are shown to complement and generalize several previous results in the literature. We also present numerical results to support our findings in the finite-node regime. Finally, we demonstrate via simulations that our results are also useful when the on/off channel model is replaced with the more realistic disk communication model .

On Connectivity and Energy Efficiency for Sleeping-Schedule-Based Wireless Sensor Networks.

Based on the connectivity and energy consumption problems in wireless sensor networks, this paper proposes a kind of new network algorithm called the connectivity and energy efficiency (CEE) algorithm to guarantee the connectivity and connectivity probability, and also to reduce the network energy consumption as much as possible. Under the premise that all sensors can communicate with each other in a specific communication radius, we obtained the relationship among the connectivity, the number of sensor nodes, and the communication radius because of the theory of probability and statistics. The innovation of the paper is to maximize the network connectivity and connectivity probability, by choosing which types of sleeping nodes to wake up. According to the node’s residual energy and the relative value of distance, the algorithm reduces the energy consumption of the whole network as much as possible, and wakes up the number of neighbor nodes as little as possible, to improve the service life of the whole network. Simulation results show that this algorithm combines the connectivity and the energy efficiency, provides a useful reference value for the normal operation of the sensors networks.

Hybrid Artificial Bee Colony Algorithm for Improving the Coverage and Connectivity of Wireless Sensor Networks

In this paper, we studied the wireless sensor network coverage and connectivity problem. Considering the artificial bee colony algorithm existed slow convergence, easily falling into the most superior faults, then used the free search algorithm pheromone sensitivity model instead of the traditional roulette wheel selection model. We also proposed a wireless sensor network coverage and connectivity based on improved artificial bee colony algorithm. Analytical proofs and simulation experiments show that compared the random distribution of nodes, genetic algorithms with the proposed algorithm, from the coverage and the number of nodes perceived relationship, the number of nodes connectivity rate and perceived relationship, covering connectivity efficiency, the number of nodes in the network lifetime and perceived relationship, achieving the same coverage and connectivity required time-consuming aspects of WSNs coverage and connectivity issues related to study, the proposed algorithm has the higher of the coverage and connectivity, and reduce data redundancy and network traffic, improve network efficiency.

Compliances of Connectivity and Communication strategies of Wireless Sensor Network (WSN) over IoT

Compliances of Connectivity and Communication strategies of Wireless Sensor Network (WSN) over IoT

A Spectrum-Aware Clustering Algorithm Based on Weighted Clustering Metric in Cognitive Radio Sensor Networks

Clustering organizes nodes into groups in order to enhance the connectivity and stability of cognitive radio sensor networks. Mainly depending on the channel availability, many existing spectrum-aware clustering algorithms may not achieve the most satisfactory clustering. Taking into account the various influence factors to establish the optimal clustering is a challenge to enhance the network performance. This paper proposes a novel spectrum-aware clustering algorithm based on weighted clustering metric to obtain the optimal clustering by solving an optimization model. The new weighted clustering metric, simultaneously evaluating temporal-spatial correlation, confidence level and residual energy, is used to elect clusterheads and ally member nodes. After clustering, the clusterheads sensing spectrum instead of all member nodes greatly reduces the energy consumption of spectrum sensing and increases the opportunity of data transmission. The performance comparison between the traditional spectrum-aware clustering algorithms and our proposed algorithm has been highlighted with the experiments.

The complexity of cluster-connectivity of wireless sensor networks

Wireless sensor networks consist of sensor devices with limited computational capabilities and memory operating in bounded energy resources; hence, network optimisation and algorithmic development in minimising the total energy or power while maintaining the connectivity of the underlying network are crucial for their design and maintenance. We consider a generalised system model of wireless sensor networks whose node set is decomposed into multiple clusters, and show that the decision and the associated minimisation problems of the connectivity of clustered wireless sensor networks appear to be computationally intractable - completeness and hardness, respectively, for the non-deterministic polynomial-time complexity class. An approximation algorithm is devised to minimise the number of end nodes of inter-cluster edges within a factor of 2 of the optimum for the cluster-connectivity.

Distributed Detection in Ad Hoc Networks Through Quantized Consensus

We study asymptotic performance of distributed detection in large scale connected sensor networks. Contrasting to the canonical parallel network where a single node has access to local decisions from all other nodes, each node can only exchange information with its direct neighbors in the present setting. We establish that, with each node employing an identical one-bit quantizer for local information exchange, a novel consensus reaching approach can achieve the optimal asymptotic performance of centralized detection as the network size scales. The statement is true under three different detection frameworks: the Bayesian criterion where the maximum a posteriori detector is optimal, the Neyman-Pearson criterion with a constant type-I error probability constraint, and the Neyman-Pearson criterion with an exponential type-I error probability constraint. Leveraging recent development in distributed consensus reaching using bounded quantizers with possibly unbounded data (which are log-likelihood ratios of local observations in the context of distributed detection), we design a one-bit deterministic quantizer with controllable threshold that leads to desirable consensus error bounds. The obtained bounds are key to establishing the optimal asymptotic detection performance. In addition, we examine non-asymptotic performance of the proposed approach, and show that the type-I and type-II error probabilities at each node can be made arbitrarily close to the centralized ones simultaneously when a continuity condition is satisfied.

Estimating distances via received signal strength and connectivity in wireless sensor networks

Distance estimation is vital for localization and many other applications in wireless sensor networks (WSNs). Particularly, it is desirable to implement distance estimation as well as localization without using specific hardware in low-cost WSNs. As such, both the received signal strength (RSS) based approach and the connectivity based approach have gained much attention. The RSS based approach is suitable for estimating short distances, whereas the connectivity based approach obtains relatively good performance for estimating long distances. Considering the complementary features of these two approaches, we propose a fusion method based on the maximum-likelihood estimator to estimate the distance between any pair of neighboring nodes in a WSN through efficiently fusing the information from the RSS and local connectivity. Additionally, the method is reported under the practical log-normal shadowing model, and the associated Cramer–Rao lower bound (CRLB) is also derived for performance analysis. Both simulations and experiments based on practical measurements are carried out, and demonstrate that the proposed method outperforms any single approach and approaches to the CRLB as well.

Three‐dimensional sensor network connectivity considering border effects and channel randomness with application to underwater networks

Connectivity is an important measure that determines the quality of service of a network. The sensor networks that find applications in underwater, terrain and space communication requires the nodes to be three-dimensional (3D), and hence, 3D connectivity is a challenging issue to be addressed. Also, the networks are prone to fading and shadowing effects due to the barriers in the communication path. Hence, the analysis of deterministic connectivity is not valid as the range of connectivity is a randomly varying parameter in the presence of fading and shadowing. In this study, the authors provide an analysis of 3D connectivity under channel randomness considering the border effects, with application to the underwater networks, where the existence of network border is significant due to the presence of natural boundaries at bottom and surface of the ocean. The connectivity analysis in the presence of log-normal shadowing is performed, and closed-form expressions are derived. Also, the 3D connectivity under the combined effects of Rayleigh fading and log-normal shadowing is analysed, which can be approximated to the well-known K-distribution used to characterise underwater sensor networks. The analysis of the proposed work matches almost exactly with the simulation, and thus, the results are validated.

Round-table negotiation for fast restoration of connectivity in partitioned wireless sensor networks

This paper addresses the problem of restoration of connectivity in wireless sensor networks after multiple simultaneous node failures. Such failures of multiple nodes may split the network into several clusters. These clusters are unaware of their own size, surviving nodes and links as well as size and location of other survivor clusters. A distributed and autonomous approach of reconnecting disjoint clusters in a short time is proposed, in which each survivor cluster undergoes a self-discovery process where it compiles information of connected survivors and then sends a negotiator to participate in a round table negotiation and decision-making process. All such negotiators exchange information and decide upon reconnection paths between clusters through known dead node locations and then assign nodes to be deployed on those paths, using available nodes. The negotiators then return to their respective clusters, convey the decision and the reconnection process is carried out. Analytical results of the self-discovery process have been obtained and simulation results on a large network are presented to illustrate the process. It is shown through a detailed comparison with existing methods that the proposed approach achieves reconnection in significantly lower time and compares favorably with respect to other performance metrics as well.

Connectivity and Coverage in Hybrid Wireless Sensor Networks using Dynamic Random Geometric Graph Model

Connectivity and Coverage in Hybrid Wireless Sensor Networks using Dynamic Random Geometric Graph Model

Dynamically Optimized Sensor Deployment Based on Game Theory

Sensor network deployment is the key for sensors to play an important performance. Based on game theory, first, the authors propose a multi-type sensor target allocation method for the autonomous deployment of sensors, considering exploration cost, target detection value, exploration ability and other factors. Then, aiming at the unfavorable environment, e.g., obstacles and enemy interference, the authors design a method to maintain the connectivity of sensor network, under the conditions of effective detection of the targets. Simulation result shows that the proposed deployment strategy can achieve the dynamic optimization deployment under complex conditions.

Minimizing movement for network connectivity in mobile sensor networks: an adaptive approach

Nowadays the most important key point in the design of wireless sensor networks (WSNs) is the sensor coverage point and network connectivity. The main practical issue in designing the essential WSN is the mobility of mobile sensors which consumes more power thereby reduces the network lifetime significantly. In order to avoid these problems, we have investigated the mobile sensor deployment (MSD) problem comprises of network connectivity and target coverage is resolved using Euclidean spanning tree model (ECST) and ECST-adaptive VABC (ECST-AVABC) method respectively. Besides, we proposed an AVABC optimization algorithm by obtaining minimum movement of mobile sensors over the network. Furthermore, the extensive simulation experiments have offered the optimal promising solutions of NCON, to the MSD problem with minimum movement and providing the extended lifetime of WSN. Finally, the experimental results states that the movement distance shown by the proposed ECST-AVABC become 4.2, 10, and 20% lesser than the standard ECST-VABC method.

The Complexity of Cluster-Connectivity of Wireless Sensor Networks

Wireless sensor networks consist of sensor devices with limited computational capabilities and memory operating in bounded energy resources; hence, network optimisation and algorithmic development in minimising the total energy or power while maintaining the connectivity of the underlying network are crucial for their design and maintenance. We consider a generalised system model of wireless sensor networks whose node set is decomposed into multiple clusters, and show that the decision and the associated minimisation problems of the connectivity of clustered wireless sensor networks appear to be computationally intractable - completeness and hardness, respectively, for the non-deterministic polynomial-time complexity class. An approximation algorithm is devised to minimise the number of end nodes of inter-cluster edges within a factor of 2 of the optimum for the cluster-connectivity.

Probabilistic Key Predistribution in Mobile Networks Resilient to Node-Capture Attacks

We present a comprehensive analysis on connectivity and resilience of secure sensor networks under the widely studied q-composite key predistribution scheme. For network connectivity, which ensures that any two sensors can find a path in between for secure communication, we derive the conditions to guarantee connectivity in consideration of: 1) node-capture attacks, where the adversary may capture a set of sensors and compromise keys in their memory; 2) sensor mobility, meaning that sensors can move around so that the network topology may change over time; 3) physical transmission constraints, under which two sensors have to be within each other's transmission range for communication; 4) the boundary effect of network fields; and 5) link unreliability, meaning that links are allowed to be unreliable. In contrast, many prior connectivity analyses of secure sensor networks often ignore the above issues. For resilience, although limited studies have presented formal analysis, it is often assumed that the adversary captures a random set of sensors, whereas this paper allows the adversary to capture an arbitrary set of sensors. We present conditions to ensure unassailability and unsplittability in secure sensor networks under the q-composite scheme. Unassailability ensures that an adversary capturing any set consisting of a negligible fraction of sensors can compromise only a negligible fraction of communication links although the adversary may compromise communications between non-captured nodes, which happen to use keys that are shared by captured nodes. Unsplittability means that when a negligible fraction of sensors are captured, almost all of the remaining nodes are still securely connected. Based on the results of connectivity, unassailability, and unsplittability, we provide useful guidelines for the design of secure sensor networks.

Predictive Retransmissions for Intermittently Connected Sensor Networks with Transmission Diversity

Batteryless wireless sensor networks that rely on energy harvested from the environment often exhibit random power outages due to limitations of energy resources, which give rise to intermittent connectivity and long transmission delays. To improve the delay performance in such networks, we consider a design strategy that uses predictive retransmissions to maximize the probability of success for each transmission. This is applied to two different transmission diversity schemes: cooperative relaying over unicast routes and opportunistic routing. Performance evaluations from theoretical models and simulations are presented that show that significant gains can be achieved using the proposed approach in such networks.