Transmission Range Of Sensors Research Articles

On transmission range of sensors in sparse wireless sensor networks

One of the main challenges in the design of wireless ad-hoc and sensor networks is to reduce energy consumption and radio interference and collision, which are strictly and strongly correlated to the transmission range of the nodes. In this article, the transmission ranges of sensors in sparse Wireless Sensor Networks (WSN) is theoretically analyzed and is shown that the transmission ranges of sensors can be significantly reduced in this type of WSNs without losing any connection. Next, a method is presented to calculate reduced transmission ranges. In this method, an iterative transmission range adjustment mechanism is used to select an almost transmission range while no information is needed on the location of nodes or the distance between nodes. This method is very efficient and its communication overhead is low. It can be used as a pre-step of any routing algorithm in order to decrease energy consumption and radio interference in targeted scenarios. The simulation results show the efficiency of the proposed method. We got at least 50% improvement in energy consumption in comparison with full power network, in sparse WSNs.

A deep learning approach to predict the number of [formula omitted]-barriers for intrusion detection over a circular region using wireless sensor networks

Wireless Sensor Networks (WSNs) is a promising technology with enormous applications in almost every walk of life. One of the crucial applications of WSNs is intrusion detection and surveillance at border areas and in the defence establishments. The border areas are stretched over hundreds to thousands of miles, hence, it is not possible to patrol the entire border region. As a result, an enemy may enter from any point absence of surveillance and cause the loss of lives or destroy the military establishments. WSNs can be a feasible solution for the problem of intrusion detection and surveillance at the border areas. Detection of an enemy at the border areas and nearby critical areas such as military cantonments is a time-sensitive task as a delay of a few seconds may have disastrous consequences. Therefore, it becomes imperative to design systems that can identify and detect the enemy as soon as it comes within the range of the deployed system. In this paper, we have proposed a deep learning architecture based on a fully connected feed-forward Artificial Neural Network (ANN) for the accurate prediction of the number of k-barriers for fast intrusion detection and prevention. We have trained and evaluated the feed-forward ANN model using four potential features, namely area of the circular region, sensing range of sensors, transmission range of sensors, and number of sensor for Gaussian and uniform sensor distribution. These features are extracted through Monte Carlo simulation. In doing so, we found that the model accurately predicts the number of k-barriers for both Gaussian and uniform sensor distribution with correlation coefficient (R = 0.78) and Root Mean Square Error (RMSE = 41.15) for the former and R = 0.79 and RMSE = 48.36 for the latter. Further, the proposed approach outperforms the other benchmark algorithms in terms of accuracy and computational time complexity.

Compressive sensing based random walk routing in wireless sensor networks

Random walk (RW) routing for monitoring purposes in Wireless Sensor Networks (WSNs) has been proven to be an energy-efficient method. In this paper, we exploit the integration between Compressive Sensing (CS) and RW to reduce energy consumption for such networks. All the sensory data is reconstructed at the base-station (BS) based on a smaller number of CS measurements compared to the total number of sensor nodes. Each CS measurement is collected through a RW routing with a predefined length. All random CS measurements are forwarded to the BS for the CS recovery process in two either ways: directly or by relaying through intermediate nodes. A trade-off between the sensor transmission range and the length of RWs is investigated for the networks to achieve the smallest energy consumption. We further formulate the average consumed energy of each random walk based on the sensor transmission range. The average consumed energy to send the measurements from RWs to the BS either directly or by relaying in multi-hop are formulated and analyzed. We calculate the total energy consumption in different conditions and suggest the optimal case for the networks to spend the least energy that significantly prolongs the network lifetime.

On the performance evaluation of query-based wireless sensor networks

We present a queueing-theoretic framework to evaluate the performance of large-scale, query-based wireless sensor networks whose nodes detect and advertise significant events that are useful for only a limited time; queries generated by sensor nodes are also time-limited. The main performance parameter is the steady state proportion of generated queries that fail to be answered on time. Using an infinite transmission range model, we first provide an approximation for this parameter that is insensitive to the size of the network. Subsequently, we approximate the proportion of failed queries when the transmission range is limited and show that this proportion converges to its infinite range counterpart as the sensor transmission range tends to infinity. The analytical approximations are shown to be remarkably accurate when compared with benchmark values obtained using a commercial network simulator.

Critical sensor density for partial connectivity in large area wireless sensor networks

In this article, we study the critical sensor density for partial connectivity of a large area sensor network. We assume that sensor deployment follows the Poisson distribution. For a given partial connectivity requirement ρ, 0.5 < ρ < 1, we prove that there exists a critical sensor density λ 0 , around which the probability that at least a fraction ρ of sensors are connected in the network increases sharply from ε to 1-ε within a short interval of sensor density λ. The length of this interval is in the order of O (-log ε/log A ) as A → ∞, where A is the area of the sensor field, and the location of λ 0 is at the sensor density where the aforesaid probability is about 1/2. We prove the preceding theoretical results in the hexagonal model. We also extend our results to the disk model that models transmission range of sensors as disks. Simulations are performed to confirm the analytical results.

Modeling the Energy Performance of Event-Driven Wireless Sensor Network by Using Static Sink and Mobile Sink

Wireless Sensor Networks (WSNs) designed for mission-critical applications suffer from limited sensing capacities, particularly fast energy depletion. Regarding this, mobile sinks can be used to balance the energy consumption in WSNs, but the frequent location updates of the mobile sinks can lead to data collisions and rapid energy consumption for some specific sensors. This paper explores an optimal barrier coverage based sensor deployment for event driven WSNs where a dual-sink model was designed to evaluate the energy performance of not only static sensors, but Static Sink (SS) and Mobile Sinks (MSs) simultaneously, based on parameters such as sensor transmission range r and the velocity of the mobile sink v, etc. Moreover, a MS mobility model was developed to enable SS and MSs to effectively collaborate, while achieving spatiotemporal energy performance efficiency by using the knowledge of the cumulative density function (cdf), Poisson process and M/G/1 queue. The simulation results verified that the improved energy performance of the whole network was demonstrated clearly and our eDSA algorithm is more efficient than the static-sink model, reducing energy consumption approximately in half. Moreover, we demonstrate that our results are robust to realistic sensing models and also validate the correctness of our results through extensive simulations.

An Efficient and Robust Data Dissemination Protocol in Wireless Sensor Networks

In this work we consider the important problem of energy balanced data dissemination in wireless sensor networks (WSN), where data are generated by the sensors and must be routed toward a unique sink. Sensors route data by either sending the data directly to the sink or in a multi-hop fashion by delivering the data to a neighboring sensor. W e decompose the transmission range of sensors into concentric circular bands rings based on a minimum transmission distance between any pair of sensors. We also propose a data dissemination protocol that exploits the above-mentioned decomposition to meet the specific requirements of a sensing application in terms of trade-off between energy and source-to-sink delay. We prove that the use of sensors nodes, which lie on or closely to the shortest path between a source and a sink, as proxy forwarders in data dissemination from sources to a sink, helps simultaneously balance energy consumption and delay. Furthermore, this protocol is based on stochastic estimation methods and is adaptive to environmental changes

A Gaussian Mixture Model-Based Continuous Boundary Detection for 3D Sensor Networks

This paper proposes a high precision Gaussian Mixture Model-based novel Boundary Detection 3D (BD3D) scheme with reasonable implementation cost for 3D cases by selecting a minimum number of Boundary sensor Nodes (BNs) in continuous moving objects. It shows apparent advantages in that two classes of boundary and non-boundary sensor nodes can be efficiently classified using the model selection techniques for finite mixture models; furthermore, the set of sensor readings within each sensor node’s spatial neighbors is formulated using a Gaussian Mixture Model; different from DECOMO [1] and COBOM [2], we also formatted a BN Array with an additional own sensor reading to benefit selecting Event BNs (EBNs) and non-EBNs from the observations of BNs. In particular, we propose a Thick Section Model (TSM) to solve the problem of transition between 2D and 3D. It is verified by simulations that the BD3D 2D model outperforms DECOMO and COBOM in terms of average residual energy and the number of BNs selected, while the BD3D 3D model demonstrates sound performance even for sensor networks with low densities especially when the value of the sensor transmission range (r) is larger than the value of Section Thickness (d) in TSM. We have also rigorously proved its correctness for continuous geometric domains and full robustness for sensor networks over 3D terrains.

Injecting power-awareness into epidemic information dissemination in sensor networks

This paper presents and evaluates a novel approach to decrease the power consumption of epidemic information dissemination in sensor networks. In essence, our strategy consists in modulating the transmission range of sensors before they send messages. Since the range modulation follows a power-law probability distribution, we qualify our approach to information dissemination as being a power-law. An obvious consequence of this strategy is that many nodes can reach few neighbors, while few nodes can reach many neighbors. To evaluate the effects of our approach, we inject the power-law range modulation into four existing epidemic algorithms and we compare their performances with their original versions, based on a fixed transmission range or on a uniform distribution of transmission ranges. This evaluation shows that our power-law approach improves the efficiency of the original algorithms in terms of power consumption, with no negative impact of their effectiveness, measured in terms of how many nodes have been reached after the dissemination.

Algorithms for Fault-Tolerant Topology in Heterogeneous Wireless Sensor Networks

This paper addresses fault-tolerant topology control in a heterogeneous wireless sensor network consisting of several resource-rich supernodes, used for data relaying, and a large number of energy-constrained wireless sensor nodes. We introduce the k-degree anycast topology control (fc-ATC) problem, with the objective of selecting each sensor's transmission range such that each sensor is k-vertex supernode connected and the total power consumed by sensors is minimized. Such topologies are needed for applications that support sensor data reporting, even in the event of failures of up to k - 1 sensor nodes. We propose three solutions for the k-ATC problem: a k-approximation algorithm, a greedy centralized algorithm that minimizes the maximum transmission range between all sensors, and a distributed and localized algorithm that incrementally adjusts sensors' transmission range such that the k-vertex supernode connectivity requirement is met. Extended simulation results are presented to verify our approaches.

A trade-off between energy and delay in data dissemination for wireless sensor networks using transmission range slicing

Data dissemination is an essential function in wireless sensor networks (WSNs). A WSN consists of a large number of unattended sensors with limited storage, battery power, computation, and communication capabilities, where battery power (or energy) is the most crucial resource for sensor nodes. Because delay time is also a critical metric for certain applications, data dissemination between source sensors (or simply sources) and a sink (or central gathering point) should be done in an energy-efficient and timely manner. In this paper, we present an approach that characterizes a trade-off between energy and source-to-sink delay (or simply delay). Specifically, we decompose the transmission range of sensors into concentric circular bands ( CCBs) based on a minimum transmission distance between any pair of sensors. Our decomposition strategy provides a classification of these CCBs that helps a sensor express its degree of interest ( DoI) in minimizing two conflicting metrics, namely energy consumption and delay. We also propose a data dissemination protocol that exploits the above-mentioned decomposition to meet the specific requirements of a sensing application in terms of energy and delay. We prove that the use of sensors nodes, which lie on or closely to the shortest path between a source and a sink, as proxy forwarders in data dissemination from sources to a sink, helps simultaneously minimize energy consumption and delay. Also, we compute theoretical lower and upper bounds on these two metrics. Our simulation results are found to be consistent with our theoretical results, and show that the first CCB minimizes energy consumption; the last CCB minimizes delay; and the middle CCBs trade-off energy consumption with delay in data dissemination in WSNs.

센서 네트워크에서의 안전한 통신을 위한 클러스터 기반 키 분배 구조

We can obtain useful information by deploying large scale sensor networks in various situations. Security is also a major concern in sensor networks, and we need to establish pairwise keys between sensor nodes for secure communication. In this paper, we propose new pairwise key establishment mechanism based on clustering and polynomial sharing. In the mechanism, we divide the network field into clusters, and based on the polynomial-based key distribution mechanism we create bivariate Polynomials and assign unique polynomial to each cluster. Each pair of sensor nodes located in the same cluster can compute their own pairwise keys through assigned polynomial shares from the same polynomial. Also, in our proposed scheme, sensors, which are in each other`s transmission range and located in different clusters, can establish path key through their clusterheads. However, path key establishment can increase the network overhead. The number of the path keys and tine for path key establishment of our scheme depend on the number of sensors, cluster size, sensor density and sensor transmission range. The simulation result indicates that these schemes can achieve better performance if suitable conditions are met.