High Density Wireless Sensor Networks Research Articles

Energy-Efficient Algorithm for Data Path Selection in High-Density Wireless Sensor Networks

Relevance. Increasing the number of sensor devices per unit area consequently reduces the physical distance between the devices in the sensor network. Such networks are usually deployed over a large area and the sensor device that wants to transmit a data packet is located far away from the base station. In such a case, the source device is challenged to choose a transmission path that consumes the least amount of energy resources and satisfies the delivery time requirements. The objective of this study is to develop and validate the effectiveness of an empirical algorithm for selecting a transmission path that reduces the energy consumption of high-density wireless sensor networks. Methods of system analysis, analytical modeling, geometry and probability theories are used. Solution. It is assumed that the sensor network is deployed in a limited area and is a set of devices that are connected to each other informationally and energetically. When building data transmission routes, any sensor devices can be used as repeaters. At the same time, the increase in the number of repeaters leads to an increase in the time of data delivery. Novelty. It is assumed that the sensor network is deployed in a limited area and is a set of devices that are connected to each other informationally and energetically. Any sensor devices may be used as repeaters when constructing data transmission routes.Significance (theoretical). Dependences of power consumption level on various system parameters affecting the processes of functioning of high-density wireless sensor networks have been obtained. Significance (practical). The proposed empirical algorithm for selecting a rational data transmission route in a wireless sensor network allows us to determine, among all alternatives, the route to the coordinator that requires the least power. The effectiveness of the proposed empirical power-saving algorithm is confirmed by simulation modeling.

Minimization of Energy Consumption for Routing in High-Density Wireless Sensor Networks Based on Adaptive Clone Elite Genetic Algorithm

High-density wireless sensor networks (HDWSNs) perceive environmental information through sensor nodes, connects data to the network, and is widely used in environmental detection, intelligent control, and military fields. Since sensor nodes are usually randomly distributed in the environment and have limited energy, ensuring the energy supply of these nodes is still a difficult problem to solve at present. Therefore, how to effectively reduce network energy consumption, improve algorithm efficiency, and extend network life time is the main problem to be solved by QoS routing in HDWSNs. This paper proposes an adaptive clone elite genetic algorithm (ACEGA) to reduce the energy consumption of HDWSNs routing. The algorithm uses clone operators and elite operators to speed up the convergence speed, and uses adaptive operators to enhance the global search capability of the algorithm. To verify the effectiveness of ACEGA, we compare ACEGA with particle swarm optimization (PSO) and simulated annealing (GA). Simulation results demonstrate that the execution performance of the ACEGA outperforms SA and PSO. In addition, the system energy consumption of HDWSNs using ACEGA is also lower than that of SA and PSO. Therefore, the algorithm we proposed effectively reduces the routing consumption of the entire HDWSNs system.

Clone Chaotic Parallel Evolutionary Algorithm for Low-Energy Clustering in High-Density Wireless Sensor Networks

Because the sensors are constrained in energy capabilities, low-energy clustering has become a challenging problem in high-density wireless sensor networks (HDWSNs). Usually, sensor nodes tend to be tiny devices along with constrained clustering abilities. To have a low communication energy consumption, a low-energy clustering scheme should be designed properly. In this work, a new cloned chaotic parallel evolution algorithm (CCPEA) is proposed, and a low-energy clustering model is established to lower the communication energy consumption of HDWSNs. By introducing CCPEA into the low-energy clustering, an objective function is designed for evaluating the communication energy consumption. For this problem, we define a clone operator to minimize the communication energy consumption of HDWSNs, use the chaotic operator to randomly generate the initial population to expand the search range to avoid local optimization, and find the parallel operator to speed up the convergence speed. In the experiment, the effect of CCPEA is compared to heuristic approaches of particle swarm optimization (PSO) and simulated annealing (SA) for the HDWSNs with different numbers of sensors. Simulation experiments demonstrate that the presented CCPEA method achieves a lower communication energy consumption and faster convergence speed than PSO and SA.

A High Density WSN Cluster Positioning Approach

Node self-positioning is one of the supporting technologies for wireless sensor network applications. In this paper, a clustering localization algorithm is proposed for large-scale high-density wireless sensor networks. Firstly, the potential of the node is defined as the basis for the election of the cluster head. The distance between the nodes in the network is calculated indirectly by the relationship between the received signal strength and the communication radius. The topology information in each cluster is saved by the cluster head, and the linear programming method is used in the cluster head to implement the cluster internal relative positioning. Then, from the sink node, the inter-cluster location fusion is gradually implemented, and finally the absolute positioning of the whole network is realized. Compared with the centralized convex programming algorithm, the proposed algorithm has low computational complexity, small traffic, high positioning accuracy, and does not need to know the signal attenuation factor in the environment in advance, and there is anti-noise ability.

A Novel Adjacent Sensors-Based Mechanism to Increase Performance of Wireless Sensor Networks

Wireless Sensor Networks are widely used nowadays to support the decision-makers in different applications by monitoring and collecting the environmental parameters in specific areas. Sensors are deployed in such areas either randomly or formally. In a high-density Wireless Sensor Network, several sensors are randomly deployed in a small area. This will make the adjacent sensors collect same data and send them to the sink, which will increase the power consumption in those sensors. Adjacent sensors are considered critical because of their effect on the network performance. In this paper, the effect of the adjacent sensors is minimized because of the above-mentioned criticality and performance influence of these sensors. The proposed mechanism is evaluated by using MATLAB simulator and is then compared with the low-energy adaptive clustering hierarchy (LEACH) protocol. Results prove that the proposed mechanism outperforms the LEACH protocol by 21% in terms of the network lifetime and by 18% in terms of the number of the transmitted packets to the cluster heads and reduces the number of the transmitted packets to the base station by approximately 3% by avoiding the duplicated packets.

Minimization of Energy Consumption for Routing in High‐Density Wireless Sensor Networks Based on Adaptive Elite Ant Colony Optimization

High‐density wireless sensor networks (HDWSNs) are usually deployed randomly, and each node of the network collects data from complex environments. Because the energy of sensor nodes is powered by batteries, it is basically impossible to replace batteries or charge in the complex surroundings. In this paper, a QoS routing energy consumption model is designed, and an improved adaptive elite ant colony optimization (AEACO) is proposed to reduce HDWSN routing energy consumption. This algorithm uses the adaptive operator and the elite operator to accelerate the convergence speed. So, as to validate the efficiency of AEACO, the AEACO is contrast with particle swarm optimization (PSO) and genetic algorithm (GA). The simulation outcomes show that the convergence speed of AEACO is sooner than PSO and GA. Moreover, the energy consumption of HDWSNs using AEACO is reduced by 30.7% compared with GA and 22.5% compared with PSO. Therefore, AEACO can successfully decrease energy consumption of the whole HDWSNs.

MOONGA: Multi-Objective Optimization of Wireless Network Approach Based on Genetic Algorithm

In high-density wireless sensor networks, the quality of service in terms of sensing coverage, connectivity, lifetime, energy consumption and cost is closely linked to the position of the nodes in the network. Consequently, the placement of a large number of nodes while simultaneously optimizing several measurements is considered to be an NP-difficult problem. In this article, we propose a new approach to optimizing the problem of node placement. To achieve this objective, we started by studying the main approaches existing in the literature in order to identify their limits. In order to have accurate solutions, existing physical models are studied, improved, and validated with real measurements. Then, we proposed a new formulation of the deployment optimization problem as a constrained multi-objective optimization problem. This allowed us to develop an optimizer, based on the multi-objective genetic algorithm and the weighted sum optimization method, which we called MOONGA (multi-objective wireless network optimization using the genetic algorithm). This optimizer makes it possible to generate an optimal deployment according to the topology, the environment, the specifications of different applications and the preferences of the network designer users. The algorithms that we have developed and implemented within the framework of experiments carried out on test data in order to prove the effectiveness of our approach. The analysis of the results found confirm well the interest and the superiority of our proposed approach compared to main studied approaches.

Elite Adaptive Particle Swarm Optimization for Target Coverage Problem in High-density Wireless Sensor Networks

The rapid development of intelligent sensing, micro electro-mechanical systems and communication has made it feasible to equip low computational complexity, low energy consumption, autonomous, and intelligent sensor nodes. High-density wireless sensor networks (HDWSNs) have information acquisition and communication abilities. HDWSNs are widely used in a number of areas including traffic avoidance, homelands security, target monitoring and so on. One of the major challenges in HDWSNs is to maximize the point coverage percentage. It is important since it is known that obtaining an optimal coverage target for HDWSNs is an NP-hard problem. In this paper, we use an elite adaptive particle swarm optimization (EAPSO) to solve the issue of target coverage in HDWSNs. In order to improve the effectiveness of system, a system model is provided to evaluate the monitored rate for HDWSNs. The proposed EAPSO with an efficient particle swarm optimization in discrete mode has the advantages of both adaptive as well as elite strategy. Numerical simulations are conducted with a number of nodes and targets using EAPSO, evolutionary algorithm (EA) and simulated annealing (SA). In the simulations, a better performance of EAPSO is given when it is compared with EA and SA with the same computational complexity.

Transfer-Efficient Face Routing Using the Planar Graphs of Neighbors in High Density WSNs.

Face routing has been adopted in wireless sensor networks (WSNs) where topological changes occur frequently or maintaining full network information is difficult. For message forwarding in networks, a planar graph is used to prevent looping, and because long edges are removed by planarization and the resulting planar graph is composed of short edges, and messages are forwarded along multiple nodes connected by them even though they can be forwarded directly. To solve this, face routing using information on all nodes within 2-hop range was adopted to forward messages directly to the farthest node within radio range. However, as the density of the nodes increases, network performance plunges because message transfer nodes receive and process increased node information. To deal with this problem, we propose a new face routing using the planar graphs of neighboring nodes to improve transfer efficiency. It forwards a message directly to the farthest neighbor and reduces loads and processing time by distributing network graph construction and planarization to the neighbors. It also decreases the amount of location information to be transmitted by sending information on the planar graph nodes rather than on all neighboring nodes. Simulation results show that it significantly improves transfer efficiency.

Low Cost Recursive Localization scheme for High Density Wireless Sensor Networks

While existing localization approaches mainly focus on enhancing the accuracy, particular attention has recently been given to reducing the localization algorithm implementation costs. To obtain a tradeoff between location accuracy and implementation cost, recursive localization approaches are being pursued as a cost-effective alternative to the more expensive localization approaches. In the recursive approach, localization information increases progressively as new nodes compute their positions and become themselves reference nodes. A strategy is then required to control and maintain the distribution of these new reference nodes. The lack of such a strategy leads, especially in high density networks, to wasted energy, important communication overhead and even impacts the localization accuracy. In this paper, the authors propose an efficient recursive localization approach that reduces the energy consumption, the execution time, and the communication overhead, yet it increases the localization accuracy through an adequate distribution of reference nodes within the network.

FM-UWB: Towards a Robust, Low-Power Radio for Body Area Networks.

The Frequency Modulated Ultra-Wideband (FM-UWB) is known as a low-power, low-complexity modulation scheme targeting low to moderate data rates in applications such as wireless body area networks. In this paper, a thorough review of all FM-UWB receivers and transmitters reported in literature is presented. The emphasis is on trends in power reduction that exhibit an improvement by a factor 20 over the past eight years, showing the high potential of FM-UWB. The main architectural and circuit techniques that have led to this improvement are highlighted. Seldom explored potential of using higher data rates and more complex modulations is demonstrated as a way to increase energy efficiency of FM-UWB. Multi-user communication over a single Radio Frequency (RF) channel is explored in more depth and multi-channel transmission is proposed as an extension of standard FM-UWB. The two techniques provide means of decreasing network latency, improving performance, and allow the FM-UWB to accommodate the increasing number of sensor nodes in the emerging applications such as High-Density Wireless Sensor Networks.

An Approximate Zero IF FM-UWB Receiver for High Density Wireless Sensor Networks

A low-power frequency modulated-ultra-wideband (FM-UWB) receiver for short-range communications, capable of simultaneously demodulating multiple FM-UWB signals located at the same frequency, is presented in this paper. The proposed receiver utilizes an “approximate zero IF” architecture that uses a low-power, free-running ring oscillator as the RF LO to first convert the input signal into baseband, where it is then amplified and demodulated. Moving the most power-hungry blocks from RF to IF results in reduction of power consumption by more than one order of magnitude compared to previous implementations using the delay-line demodulator. Integrated in a 65-nm CMOS technology, the whole receiver chain consumes 423 $\mu \text{W}$ from a 1-V supply while achieving −70-dBm sensitivity at a data rate of 100 kb/s at 4 GHz. Communication with up to four FM-UWB users, operating in the same band, is demonstrated, making this receiver suitable for high-density wireless sensor networks.

Distributed Data Aggregation Scheduling in Multi-Channel and Multi-Power Wireless Sensor Networks

Large amounts of sensor data are frequently generated and streamed from sensors deployed on various buildings, in forests or in other application areas. In many of these areas, one difficulty is managing the velocity and volume of the big sensor data while still providing low time latency support for data analysis. Data aggregation can reduce the volume of big sensor data. However, data aggregation is a fundamental yet time-consuming operation in wireless sensor networks (WSNs), particularly in high-density WSNs. Therefore, researchers have started focusing on minimizing the latency of data aggregation, which has been proven to be an NP-hard problem. This paper proposes a cluster-based distributed data aggregation scheduling algorithm, distributed multi-power and multi-channel (DMPMC), that can minimize the data aggregation latency in multi-channel and multi-power WSNs. To save energy, low transmission power is used for packet transmissions inside a cluster, and high power is used for packet transmissions among clusters. Simulations are conducted to compare DMPMC with the best centralized algorithm in a single channel, named E-PAS, the best distributed algorithm in a single channel, named CLU-DDAS, and the best algorithm in multi-channels, named multi-channel. The results show that the DMPMC algorithm proposed in this paper achieves the lowest average latency.

A Time Difference of Arrival-based Localization Algorithm for Wireless Sensor Networks

Aimed at solving the problem of local divergence and low data accuracy, this paper introduces a new Time Difference of Arrival(TDOA)-based localization algorithm (TBL) for the large-scale, high-density wireless sensor networks which are designed for real-time surveillance and unexpected incidents management. In particular, several means to improve the accuracy of distance measurement are investigated, and the TDOA method, based on the sound wave and electromagnetic wave to locate in the large-scale WSN, is discussed. Also, the well-designed circular location process has the advantage of better positioning accuracy and coverage percentage. Simulation results have confirmed the effectiveness of the formed TBL algorithm.

Partial synchronization and controllability of asymmetric coupling wireless sensor networks

For a uniform complex dynamics networks, a synchronization criterion of this model is given by constructing a Lyapunov function. In this paper, we present a new asymmetric coupling wireless sensor networks (WSNs) synchronization control algorithm combining with clusters, it aimed at the application of a large scale, high-density WSNs. It is proved that this network can achieve synchronization dynamically under the control scheme. This model avoids the research under the specific network topology of network synchronization problems, instead of researching how to improve the synchronization performance of the network from the perspective of cybernetics. Particularly, this controller has strong robustness against the coupling strength and topological structure of the network. Numerical simulations show this model can significantly improve the performance of synchronization for WSNs. Our results offer important references on the performance issues depending on specific application scenarios of WSNs.

Full Coverage Deployment in Wireless Sensor Network

In wireless sensor network very less work has been done on deployment. As we know in random deployment we can’t achieve the maximum coverage in communication which result in short life time of the battery. So in this paper we discuss few benefit of the Uniform deployment in which we can achieve the full coverage with uniformity. In this paper we can achieve the maximum efficiency and increase lifetime time of the sensors which result in the long battery life which is the back bone of any Wireless Sensor Network for Remote Sensing like in Oceanographic, Glacier study and Metrological Department for weather study which are nearly inaccessible regions. Wireless sensor networks (WSNs) are composed of large number of sensor nodes that have capability of sensing, data processing and communication functionalities. The nodes are usually accoutered with power-strained batteries, which are often difficult, extravagant and even impossible to be restored once the nodes are deployed. Therefore energy aliveness becomes the key research challenge for sensor network protocols. The energy consumed by a node depends on its state. Each node could be in one of four states: transmit, receive, idle (when the node keeps listening to the medium even when no messages are being transmitted) and finally sleep state (where the radio module is switched off: no communication is possible). Recent research showed that accurate energy savings can be achieved by scheduling node’s activities in high-density WSNs. Specifically, some nodes are scheduled to sleep whereas the remaining ones provide continuous monitoring. The main issue here is how to minimize the number of active nodes in order to maximize the network lifetime. Sensing coverage is also a significant issue for sensor networks and is viewed as one of the critical measures of the supervision quality provided by a WSN. Sensor deployment is important in WSNs, as it not only determines the cost for creating the network but also affects how well a region is supervised by sensors. One frequently used method to decrease the undesirable energy consumption is to implement an energy-efficient configuration protocol where sensors operate under a well-defined activating schedule, that is, some unnecessary sensors switch to off mode or low-power listening mode. The coverage problem in WSNs is how one can be sure that the deployment of active sensors provides the necessary coverage level and how to determine the minimum number of active sensors required and their locations in the interested area to ensure desirable coverage. Regarding sensors activating or deployment control problem, some work have been investigated how some sensors can be arranged to go to sleeping modes to elongate the network lifetime while maintaining desirable coverage of sensing field. On the other hand how these active sensors can be selected to maintain the desirable coverage of sensing field. And provide a near-optimal grid-based sensor placement algorithm to achieve the complete coverage. In this method, the interested area is divided into grid points uniformly. If one grid point can be detected by at least one sensor, the grid point is covered. The coverage of the area is defined as the ratio of detected grid points to the total

Selected RSSI-based DV-Hop Localization for Wireless Sensor Networks

With the increasing demand on wireless sensor networks (WSNs) applications, acquiring the information of sensor node locations becomes one of the most important issues. Up to now, available localization approaches can be categorized into range-free and range-based methods. Range-free localizations are being pursued as a more cost-effective method. However, range-based schemes have better localization accuracy. This paper proposes the selected RSSI-based DV-Hop localization, which improves localization accuracy from the existing schemes by applying a combined technique that inherits the benefits from both methods. Our proposed technique firstly employs the DV-Hop approach of range-free algorithms, then uses the received signal strength indicator (RSSI) estimation technique of range-based algorithms to estimate the distances of selected hops. This paper also includes basic studies, which have been performed via computer simulations as well as testbed experiments, for distance calculation from RSSI measurement and location estimation in order to prove the credibility of our simulator. The proposed technique is implemented and tested via our developed WSN simulation model. Results in terms of distance error in comparison with traditional DV-Hop, RDV-Hop, and weighted RSSI algorithms show significant performance improvement by using our proposed method for both low-density and high-density wireless sensor network test scenarios.

Keeping Desired QoS by a Partial Coverage Algorithm for Cluster-Based Wireless Sensor Networks

Many studies were done on design algorithms to completely cover an area. We can call the issue as a complete coverage problem. Although there were many researches about proposing a partial coverage issue; almost all of the studies only focus on lengthening the lifespan of wireless sensor networks (WSNs) but ignore the truth that a wireless sensor network will inevitably lose its coverage because of exhausted energy during the operating time. Lifespan refers to wireless sensor network offering its service to meet the required coverage ratio. In this work, we propose a topology control algorithm for cluster-based wireless sensor networks to keep predefined Quality of Service (QoS) of coverage as long as the sensor nodes can stand. The algorithm forms a multi-hop cluster network with a required connectivity by using a novel cluster head competition scheme and proper transmission of power settings. It can organize sensor nodes into clusters actively to achieve required coverage ratio as long as possible. The algorithm is suitable for practical applications of large-scale or high-density wireless sensor networks due to its distributed processing and scalable cluster topology

A trajectory-based selective broadcast query protocol for large-scale, high-density wireless sensor networks

We present a small-footprint search protocol designed to facilitate any-type queries for data content and services in large population, high-density wireless sensor networks. Our protocol, termed Trajectory-based Selective Broadcast Query (TSBQ), works in conjunction with time division multiple access- or schedule-based medium access control protocols to reduce per-query energy expenditure. We compare the performance of TSBQ to unicast- and local broadcast-based search algorithms and also determine a critical node density based on the energy expended by nodes to transmit and receive. Minimal energy expenditure is achieved by determining the optimal number of data/service replicates and the number of nodes designated to receive each query transmission. Numerical results indicate that TSBQ significantly reduces the total energy expenditure of a network as compared to unicast and local broadcast-based search protocols.

A Location-free Algorithm of Energy-Efficient Connected Coverage for High Density Wireless Sensor Networks

One of the most serious concerns for wireless sensor networks (WSN) is energy. To obtain long lifetime, one potential method is deploying redundant sensors in the WSN and let each sensor switch its state between ACTIVE and OFF. At the same time, the WSN should meet various requirements of quality of service (QoS). This paper focuses on two important measurements of OoS: sensing coverage and network connectivity. Existing researches have provided many algorithms to schedule the nodes' states for coverage preserving, including location-free ones. Our work differs from existing location-free algorithms in several key ways: (1). We propose a novel principle on coverage preserving that is suitable for location-free schemes; (2). Following the principle, a distributed, localized and location-free node scheduling algorithm, Stand Guard Algorithm (StanGA), is proposed for treating coverage and connectivity in a unified scheme. Under certain conditions, StanGA can guarantee network connectivity and any degree of sensing coverage. Simulation results show that StanGA is scalable and robust, and also show that StanGA outperforms existing location-free algorithms even when only coverage issue is considered.