Dense Wireless Sensor Networks Research Articles

Multipath Grid-Based Enabled Geographic Routing for Wireless Sensor Networks

This work proposes an efficient disjoint multipath geographic routing algorithm for dense wireless sensor networks (WSN), called Multipath Grid-based Enabled Geographic Routing (MGEGR). The proposed algorithm relies on the construction of a 2-D logical grid in the geographical region of deployment. The objective of the proposed scheme is to determine optimal or near-optimal (within a defined constant) multiple disjoint paths (multipath) from a source node to the sink, in order to enhance the reliability of the network. The determined multiple disjoint paths would be used by the source node in a round-robin way to balance the traffic across the disjoint paths, and to avoid discovered paths with cell holes. The proposed scheme limits the use of broadcasting to the process of gateway election within each cell, and the process of maintaining the table of neighbors of each gateway. Our simulation results show the effectiveness and scalability of our routing scheme with increased network size compared to on-demand routing protocols.

Integration and Analysis of Neighbor Discovery and Link Quality Estimation in Wireless Sensor Networks

Network connectivity and link quality information are the fundamental requirements of wireless sensor network protocols to perform their desired functionality. Most of the existing discovery protocols have only focused on the neighbor discovery problem, while a few number of them provide an integrated neighbor search and link estimation. As these protocols require a careful parameter adjustment before network deployment, they cannot provide scalable and accurate network initialization in large-scale dense wireless sensor networks with random topology. Furthermore, performance of these protocols has not entirely been evaluated yet. In this paper, we perform a comprehensive simulation study on the efficiency of employing adaptive protocols compared to the existing nonadaptive protocols for initializing sensor networks with random topology. In this regard, we propose adaptive network initialization protocols which integrate the initial neighbor discovery with link quality estimation process to initialize large-scale dense wireless sensor networks without requiring any parameter adjustment before network deployment. To the best of our knowledge, this work is the first attempt to provide a detailed simulation study on the performance of integrated neighbor discovery and link quality estimation protocols for initializing sensor networks. This study can help system designers to determine the most appropriate approach for different applications.

Cell-based snapshot and continuous data collection in wireless sensor networks

Data collection is a common operation of wireless sensor networks (WSNs). The performance of data collection can be measured by its achievable network capacity. However, most existing works focus on the network capacity of unicast, multicast or/and broadcast. In this article, we study the snapshot/continuous data collection (SDC/CDC) problem under the physical interference model for randomly deployed dense WSNs. For SDC, we propose a Cell-Based Path Scheduling (CBPS) algorithm based on network partitioning. Theoretical analysis shows that its achievable network capacity is order-optimal. For CDC, a novel Segment-Based Pipeline Scheduling (SBPS) algorithm is proposed which combines the pipeline technique and the compressive data gathering technique. Theoretical analysis shows that SBPS significantly speeds up the CDC process and achieves a high network capacity.

DRINA: A Lightweight and Reliable Routing Approach for In-Network Aggregation in Wireless Sensor Networks

Large scale dense Wireless Sensor Networks (WSNs) will be increasingly deployed in different classes of applications for accurate monitoring. Due to the high density of nodes in these networks, it is likely that redundant data will be detected by nearby nodes when sensing an event. Since energy conservation is a key issue in WSNs, data fusion and aggregation should be exploited in order to save energy. In this case, redundant data can be aggregated at intermediate nodes reducing the size and number of exchanged messages and, thus, decreasing communication costs and energy consumption. In this work, we propose a novel Data Routing for In-Network Aggregation, called DRINA, that has some key aspects such as a reduced number of messages for setting up a routing tree, maximized number of overlapping routes, high aggregation rate, and reliable data aggregation and transmission. The proposed DRINA algorithm was extensively compared to two other known solutions: the Information Fusion-based Role Assignment (InFRA) and Shortest Path Tree (SPT) algorithms. Our results indicate clearly that the routing tree built by DRINA provides the best aggregation quality when compared to these other algorithms. The obtained results show that our proposed solution outperforms these solutions in different scenarios and in different key aspects required by WSNs.

An Analytical Study of Variable Preamble Length-Based Broadcasting Scheme for WSNs

In this paper, we present a detailed analytical study of previously proposed variable preamble length-based broadcasting scheme for wireless sensor networks (WSNs). Analytical results show that in dense wireless sensor networks, broadcasting a packet with a small size preamble can significantly improve the energy conservation on already limited battery powered sensor nodes. For fairer comparison with variable preamble length-based broadcasting scheme, a comprehensive analytical study for an existing probability-based broadcasting scheme is also presented. Analytically calculated results show that variable preamble length-based broadcasting scheme is more energy-efficient than the probability-based broadcasting scheme.

An Energy Efficient Color Based Topology Control Algorithm for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are universally being used and deployed to monitor the surrounding physical environments and detail events of interest. In wireless Sensor Networks energy is one of the primary issues and requires energy conservation of the sensor nodes, so that network lifetime can be maximized. To minimize the energy loss in dense WSNs a Color Based Topology Control (CBTC) algorithm is introduced and implemented in Visual Studio 6.0. The results are compared with Traditional dense WSNs. In the evaluation process it was observed that the numbers of CPU ticks required in traditional WSNs are much more than that’s of CBTC Algorithm, both in Normal and Random deployments. So by using CBTC, delay in network can be minimized. Using CBTC algorithm, the energy conservation and removal of coverage holes was also achieved in the present study.

OSERENA: a Coloring Algorithm Optimized for Dense Wireless Networks

The goal of this paper is to present OSERENA, a distributed coloring algorithm optimized for dense wireless sensor networks (WSNs). Network density has an extremely reduced impact on the size of the messages exchanged to color the WSN. Furthermore, the number of colors used to color the network is not impacted by this optimization. We describe in this paper the properties of the algorithm and prove its correctness and termination. Simulation results point out the considerable gains in bandwidth.

Reduced‐frame TDMA protocols for wireless sensor networks

SUMMARYTime‐division multiple‐access (TDMA) is a common medium access control paradigm in wireless sensor networks. However, in its traditional form, the TDMA‐based protocols suffer from low channel utilization and high message delay because of a long frame length needed to provide collision‐free transmissions, which is particularly damaging in dense wireless sensor networks. In this paper, we investigate the performance and the energy efficiency of a class of TDMA‐based protocols, called reduced‐frame TDMA, where every TDMA slot is augmented with a short time period dedicated for carrier sense multiple access‐based contention resolution mechanism. Because of their ability to dynamically resolve collisions caused by conflicting slot assignments, the reduced‐frame TDMA protocols can be configured with any frame length, independently of node density. In addition, we present a distributed heuristic slot assignment algorithm that minimizes interslot interference in the presence of limited number of slots per frame. The simulation results indicate that the reduced‐frame TDMA protocols significantly reduce the message delay and increase the maximum throughput without incurring significant penalty in energy efficiency compared with the traditional TDMA scheme. Copyright © 2012 John Wiley & Sons, Ltd.

An energy-aware spatio-temporal correlation mechanism to perform efficient data collection in wireless sensor networks

Large scale dense wireless sensor networks (WSNs) will be increasingly deployed in different classes of applications for accurate monitoring. Due to their high density of nodes, it is very likely that information that is both spatially and temporally correlated can be detected by several nodes what can be exploited to save energy, a key aspect on these networks. Furthermore, it is important to take advantage of these correlations to decrease communication and data exchange. However, current proposals usually result in high delays and outdated data arriving at the sink node. In this work, we go further and propose a new algorithm, called Efficient Data Collection Aware of Spatio-Temporal Correlation (EAST), which uses shortest routes for forwarding the gathered data toward the sink node and fully exploit both spatial and temporal correlations to perform near real-time data collection in WSNs. Simulation results clearly indicate that our proposal can sense an event with a high accuracy of more than 99.7% while still saving the residual energy of the nodes in more than 14 times when compared to the accurate data collection strategy reported in the literature.

A new medium access control protocol based on perceived data reliability and spatial correlation in wireless sensor network

It is well known that the data of large-scale and dense wireless sensor network has high spatial correlativity. According to the monitoring of a particular event, a new medium access control (MAC) protocol based on perceived data reliability and spatial correlation is proposed in this paper. In this protocol, a ring-shaped space correlation model is established. The nodes close to information source is attributed them high priority in access channel so as to make sure the high quality data send successfully to the sink node and increase the data transferring efficiency. The new protocol can make the received data reflect the real physical phenomenon. At the same time, it can make the data transmission of the nodes far to information source be brought down and the amount of required data nodes be reduced. So the new protocol can prolong lifetime of the whole network. Our simulation results show that the new MAC protocol can supply better network service under low energy consumption and transmission delay. This is very useful for all kinds of mobile services of Internet of Things.

MEA: an energy efficient algorithm for dense sector-based wireless sensor networks

AbstractIn this article, first the energy efficiency of sector-shaped wireless sensor networks is analytically investigated. Based on this study, it is shown that the efficiency of existing data propagation algorithms which consider equal ring width is not optimal and may be improved further. Then, we introduce an energy efficient algorithm for these networks which is called minimum energy algorithm (MEA). The detailed analysis verifies that the proposed algorithm has the minimum energy consumption. Although the main emphasis of the proposed technique is on minimizing the energy, it somehow balances the energy consumption in the sector-shaped network as well. In addition, it is shown that the proposed idea can be applied to all existing energy balancing algorithms. The efficacy of the proposed algorithm is studied for networks with different sizes and node densities. The results show that, for example, for a network with a radius of 440 m and four rings when the MEA algorithm is combined with an efficient full power control algorithm (based on equal ring width), the energy conservation increases 50% more. Finally, the results show that the energy conservation of the proposed algorithm increases with the network size.

Distributed Compressive Sampling for Lifetime Optimization in Dense Wireless Sensor Networks

The problem of data sampling and collection in wireless sensor networks (WSNs) is becoming critical as larger networks are being deployed. Increasing network size poses significant data collection challenges, for what concerns sampling and transmission coordination as well as network lifetime. To tackle these problems, in-network compression techniques without centralized coordination are becoming important solutions to extend lifetime. In this paper, we consider a scenario in which a large WSN, based on ZigBee protocol, is used for monitoring (e.g., building, industry, etc.). We propose a new algorithm for in-network compression aiming at longer network lifetime. Our approach is fully distributed: each node autonomously takes a decision about the compression and forwarding scheme to minimize the number of packets to transmit. Performance is investigated with respect to network size using datasets gathered by a real-life deployment. An enhanced version of the algorithm is also introduced to take into account the energy spent in compression. Experiments demonstrate that the approach helps finding an optimal tradeoff between the energy spent in transmission and data compression.

Experimental analysis of vehicle–bridge interaction using a wireless monitoring system and a two-stage system identification technique

Deterioration of bridges under repeated traffic loading has called attention to the need for improvements in the understanding of vehicle–bridge interaction. While analytical and numerical models have been previously explored to describe the interaction that exists between a sprung mass (i.e., a moving vehicle) and an elastic beam (i.e., bridge), comparatively less research has been focused on the experimental observation of vehicle–bridge interaction. A wireless monitoring system with wireless sensors installed on both the bridge and moving vehicle is proposed to record the dynamic interaction between the bridge and vehicle. Time-synchronized vehicle–bridge response data is used within a two-stage system identification methodology. In the first stage, the free-vibration response of the bridge is used to identify the dynamic characteristics of the bridge. In the second stage, the vehicle–bridge response data is used to identify the time varying load imposed on the bridge from the vehicle. To test the proposed monitoring and system identification strategy, the 180m long Yeondae Bridge (Icheon, Korea) was selected. A dense network of wireless sensors was installed on the bridge while wireless sensors were installed on a multi-axle truck. The truck was driven across the bridge at constant velocity with bridge and vehicle responses measured. Excellent agreement between the measured Yeondae Bridge response and that predicted by an estimated vehicle–bridge interaction model validates the proposed strategy.

Centralized Router Election for IP-WSN Based on Facility Location Model

It is a good alternative that uses reduced IPv6 as network layer protocol of wireless sensor network (WSN). This will generate a new problem: how to choose some nodes as router for homogenous, dense deployment WSN. This paper proposed a centralized router election method, which is based on facility location model, for IP-WSN. This method creates a 0-1 integer programming model by regarding router election as uncapacitated facility location problem firstly. Then genetic algorithm is used for solving the created model. The simulation results show that this method can elect small number of nodes as router with high efficiency.

Energy consumption and lifetime analysis in clustered multi-hop wireless sensor networks using the probabilistic cluster-head selection method

Clustering sensor nodes into groups is an effective way of reducing the transmission of duplicated information in energy-constraint wireless sensor networks (WSNs). The performance of clustering is greatly influenced by the selection of cluster-heads, which are in charge of creating clusters and controlling member nodes. In selecting cluster-heads, a probabilistic method where each sensor node selects itself as a cluster-head with a given probability is often used in large-scale and dense WSNs because it enables all nodes to independently decide their roles while keeping the signaling overhead low. In this method, the probability of being a cluster-head should be optimally chosen to maximize the energy efficiency of the nodes. In this article, we propose a novel energy model to estimate the energy consumed in a multi-hop WSN clustered with probabilistic cluster-head selection. Then, based on our model, we determine optimal probability that maximizes the lifetime of a network. Simulation results achieved by the Monte Carlo method show that our model estimates well in energy consumption from a network and also predicts the optimal clustering probability accurately.

Fuzzy-logic based routing for dense wireless sensor networks

The task of routing data from a source to the sink is a critical issue in ad hoc and wireless sensor networks. In this paper, the use of fuzzy logic to perform role assignment during route establishment and maintenance is proposed. An incremental approach is presented and compared with similar existing routing protocols. Efficient routing approaches provide network load balance to extend network lifetime, efficiency improvements, and data loss avoidance. Experiments show promising results for our proposals and its suitability for operating with dense networks, obtaining quick route creation as well as energy efficiency.

A spatial correlation aware algorithm to perform efficient data collection in wireless sensor networks

Large scale dense wireless sensor networks (WSNs) will be increasingly deployed in different classes of applications for accurate monitoring. Due to this high density of nodes, it is very likely that both spatially correlated information and redundant data can be detected by several nearby nodes, which can be exploited to save energy. In this work we consider the problem of constructing a spatial correlation aware dynamic and scalable routing structure for data collection and aggregation in WSNs. Although there are some solutions for data aggregation in WSNs, most of them build their structures based on the order of event occurrence. This can lead to both low quality routing trees and a lack of load balancing support, since the same tree is used throughout the network lifetime. To tackle these challenges we propose a novel algorithm called dYnamic and scalablE tree Aware of Spatial correlaTion (YEAST). Results show that the routing tree built by YEAST provides the best aggregation quality compared with other evaluated algorithms. With YEAST an event can be sensed with 97% accuracy, and 75% of the nodes’ residual energy can be saved within the phenomena area when compared with the classical approach for data collection.

Collective Information Transmission to Improve Connectivity of Non Uniform Density Wireless Sensor Networks

Collective Information Transmission to Improve Connectivity of Non Uniform Density Wireless Sensor Networks

Characterizing the Traffic Load Distribution in Dense Wireless Sensor Networks

Traffic load is typically not evenly distributed over the sensor nodes in a wireless sensor network (WSN). Understanding the traffic load distribution can guide the network-wide energy allocation, ...

Green modulations in energy-constrained wireless sensor networks

Owing to the unique characteristics of sensor devices, finding the energy-efficient modulation with a low-complexity implementation (refereed to as green modulation) poses significant challenges in the physical layer design of wireless sensor networks (WSNs). Towards this goal, the authors present an in-depth analysis on the energy efficiency of various modulation schemes using realistic models in the IEEE 802.15.4 standard to find the optimum distance-based scheme in a WSN over Rayleigh and Rician fading channels with path loss. The authors describe a proactive system model according to a flexible duty-cycling mechanism utilised in practical sensor apparatus. The present analysis includes the effect of the channel bandwidth and the active mode duration on the energy consumption of popular modulation designs. Path-loss exponent and DC–DC converter efficiency are also taken into consideration. In considering the energy efficiency and complexity, it is demonstrated that among various sinusoidal carrier-based modulations, the optimised non-coherent M-ary frequency shift keying (NC-MFSK) is the most energy-efficient scheme in sparse WSNs for each value of the path-loss exponent, where the optimisation is performed over the modulation parameters. In addition, the authors show that the on–off keying displays a significant energy saving as compared to the optimised NC-MFSK in dense WSNs with small values of path-loss exponent.