Real Wireless Sensor Networks Research Articles

An Improved Cluster Head Selection in Routing for Solar Energy-Harvesting Multi-heterogeneous Wireless Sensor Networks

Wireless sensor network (WSN) is an effective and efficient technology for field information collection in Internet of Things arena. Generally, the lifetime of any WSN is restricted due to the limited battery capacities available with its sensor nodes. Replenishing the nodes’ batteries through energy-harvesting is becoming popular nowadays for improving the lifetime of the WSNs. Wireless communication activities of the sensor nodes take a major chunk of battery’s energy during WSN operations and to optimize the energy dissipation, energy-efficiency is given prime importance in routing decisions. The WSN heterogeneity (e.g., sensor nodes with heterogeneous sensing requirements, nodes with different energies, etc.) has become unavoidable and its effective exploitation further complicates the routing challenges. To fulfill the requirements of a realistic WSN system, this paper considers a multi-heterogeneity WSN scenario with sensors nodes having different initial energies and different traffic requirements along with solar energy-harvesting capabilities. An improved cluster-head selection based routing algorithm is proposed for the scenario, which exploits effectively the WSN heterogeneities in terms of energy, traffic and energy-harvesting. To highlight the performance of the proposed algorithm, the system is considered non energy-neutral, i.e. the energy dissipation of the system is higher than the system’s harvesting energy over a longer time period. The proposed algorithm, Energy-Harvesting, Traffic and Energy Aware Routing, improves the WSN stability period over existing routing algorithms under the scenario, where the stability period signifies the WSN lifetime till all the nodes are alive and represents the most reliable period of an operational WSN.

A Practical Neighbor Discovery Framework for Wireless Sensor Networks.

Neighbor discovery is a crucial operation frequently executed throughout the life cycle of a Wireless Sensor Network (WSN). Various protocols have been proposed to minimize the discovery latency or to prolong the lifetime of sensors. However, none of them have addressed that all the critical concerns stemming from real WSNs, including communication collisions, latency constraints and energy consumption limitations. In this paper, we propose Spear, the first practical neighbor discovery framework to meet all these requirements. Spear offers two new methods to reduce communication collisions, thus boosting the discovery rate of existing neighbor discovery protocols. Spear also takes into consideration latency constraints and facilitates timely adjustments in order to reduce the discovery latency. Spear offers two practical energy management methods that evidently prolong the lifetime of sensor nodes. Most importantly, Spear automatically improves the discovery results of existing discovery protocols, on which no modification is required. Beyond reporting details of different Spear modules, we also present experiment evaluations on several notable neighbor discovery protocols. Results show that Spear greatly improves the discovery rate from to , and prolongs the sensor nodes lifetime up to times.

A Lightweight Anomaly Detection Method Based on SVDD for Wireless Sensor Networks

Limited resources and harsh deployment environments may cause raw observations collected by sensor nodes to have poor data quality and reliability, which will influence the accuracy of the analysis and decision making in wireless sensor networks (WSNs). Therefore, anomaly detection must be implemented on the data collected by nodes. Support vector data description based on spatiotemporal and attribute correlations (STASVDD) can efficiently detect outliers. A novel optimization method based on STASVDD (N-STASVDD) is put forward in this paper. The proposed method considers that outliers can independently occur in each attribute when the collected data vectors are independent and identically distributed in WSNs. The proposed method applies the concept of core-sets to reduce the computational complexity of the quadratic programming problem in STASVDD, consequently reducing the energy consumption of resources-constrained WSNs. In addition, comparing the distributed and centralized detection approach of this method, the results show that the distributed approach has better performance because it relieves the communication burden. Extensive experiments were performed on both synthetic and real WSNs datasets. Results revealed that N-STASVDD achieves low time complexity and high detection accuracy.

A Wake-Up Radio-Based MAC Protocol for Autonomous Wireless Sensor Networks

Wireless sensor networks (WSNs) with energy harvesting capabilities have drawn increasing attention in the last few years, as they enable long-term monitoring applications. However, the level of power harvested is usually limited to few mW. To improve the energy efficiency of WSNs, many power management techniques have been proposed to adjust the quality of service according to the harvested energy fluctuations. As wireless communications consume a major fraction of the available energy, numerous medium access control (MAC) protocols have been proposed to minimize energy consumption, latency, and data collisions. In this paper, we present an innovative MAC protocol for energy-harvesting based WSNs exploiting ultralow-power wake-up radios. To overcome the limited range typical of wake-up radios, a multi-hop wake-up scheme based on a dual radio system is proposed enabling asynchronous communications between a base station and any node of the network while maintaining a low latency and a high energy efficiency. To reduce energy consumption, wake-up calls and data packets are transmitted using two distinct data rates. Combined with destination address decoding, using a higher data rate for data transmission also minimizes the risk of collisions. Our approach has been applied to monitoring applications composed of autonomous sensor nodes powered by indoor light energy. OMNeT++ simulation results demonstrate the benefits of our wake-up radio-based approach in terms of energy, latency, and collisions when compared with the state-of-the-art duty-cycled MAC protocols. Experiments performed with real WSN platforms equipped with a wake-up radio prototype confirm the efficiency of our approach.

IDEG: Integrated Data and Energy Gathering Framework for Practical Wireless Sensor Networks Using Compressive Sensing

Minimizing the energy consumption through efficient data gathering as well as energy harvesting both are open research problems in the context of practical wireless sensor networks (WSNs). Although the number of approaches has been proposed to cater the above, they are limited in terms of computational complexity, energy efficiency, and the ease of implementation. In this paper, we propose an integrated data and energy gathering framework, named hereby as “iDEG,” for practical WSNs. The proposed work is based on compressive sensing and utilizes partial canonical identity (PCI) matrix that samples/picks fewer sensor nodes randomly at any time point instead of picking up a linear combination of data from all sensor nodes at all time points as used in the conventionally popular Gaussian or Bernoulli matrices. This reduces the computation cost, implementation complexity, and energy losses while improving the data recovery. Furthermore, the fewer sensors at any time point selected by PCI contribute for recovering data, while the rest of the nodes harvest energy during those time points. Performance of iDEG has been tested on a real WSN dataset from Intel Lab. Comparative results of iDEG with the conventional approaches highlight its efficacy.

A 3-D geographic location routing protocol based on forward region adaptive

ABSTRACT In the application environment of real wireless sensor networks (WSNs), the sensor node is located in a 3-D space. Therefore, the conical forwarding region-based geographical (CFRG) routing protocol is proposed. In order to reduce the number of redundant data packets and reduce the collision probability of data packages, CFRG will establish a conical forwarding region to limit the number of forwarding nodes. In the case of the void node problem (VNP), the node will adaptively adjust the volume of the conical forwarding region and expand the space of forwarding node selection, namely, solve the VNP by adjusting the conical forwarding region. The simulation result shows that such CFRG proposed is able to solve the VNP. Comparing with a similar 3-D routing protocol, CFRG can improve the performances of end-to-end transmission delay and data packet loss rate.

A new lossless neighborhood indexing sequence (NIS) algorithm for data compression in wireless sensor networks

In the recent years, wireless sensor networks (WSN) has been deployed in different real time applications. Energy efficiency is the critical issue in the design and deployment of WSN since the sensor nodes are powered by batteries with limited capacity. As data transmission is the main power consuming process in WSN, several energy efficient techniques have been proposed. Data compression is a popular energy efficient technique which helps to reduce the amount of data to be transmitted in the network resulting in significant power saving. This paper proposes a new algorithm called neighborhood indexing sequence (NIS) for data compression in WSN. The proposed NIS algorithm dynamically assigns shorter length codewords to each character in the input sequence by exploiting the occurrence of neighboring bits. Using the real world WSN dataset, it is shown that the compression performance of the NIS algorithm is superior to existing compression algorithms. Compared with existing methods, the proposed compression algorithm is not only efficient but also highly robust for different WSN dataset. The proposed algorithm attains a compression ratio of 89.13 with the bit rate of 1.74 per sample. Moreover, it achieved power savings up to 87.57% for the applied WSN dataset.

Characterizing unpredictable patterns in Wireless Sensor Network data

Wireless Sensor Network (WSN) monitoring takes a primary role in many industrial and research processes. Huge amounts of WSN sensor readings are nowadays available and can be analyzed to discover fruitful knowledge.This paper focuses on analyzing historical WSN sensor readings to identify the combinations of sensors whose readings show an unexpected trend. Although significant variations of single sensor readings may be easily detected, discovering correlations between multiple sensor readings is challenging without using advanced data analytics tools. To tackle this issue, we present an itemset-based data mining approach to analyzing WSN data. It identifies the combinations of sensors (of arbitrary size) whose readings are unexpectedly low in a given time period. Since the readings acquired by multiple sensors may decrease in an alternate fashion, the discovered patterns provide new information compared to single sensor analysis. To make the mined patterns manageable by domain experts for manual inspection, the mining algorithm is driven by spatial constraints defined on the WSN topology.The experimental results, achieved on real WSN data, demonstrate the effectiveness of the proposed approach in detecting heating system malfunctioning.

Cooperative localisation over small world WSN using optimal allocation of heterogeneous nodes

Localisation of sensor nodes in a wireless sensor network (WSN) is an important problem in various applications like cyber-physical systems, Internet of things and context aware pervasive systems. Cooperative localisation using multidimensional scaling (MDS) has been used successfully in many localisation applications. A primary requirement in MDS is the computation of accurate distance estimates between pairs of nodes in a WSN. However, the estimated distances are erroneous in MDS especially for node pairs that are connected using multiple hops. This leads to an overall increase in error of location estimates. A recent development in social networks called small world phenomena can be introduced in a WSN leading to small world WSN (SW-WSN). SW-WSN exhibits low average path length and high average clustering coefficient and yields accurate distance estimates between pairs of nodes. In this work, a novel cooperative localisation method that uses heterogeneous nodes (H-nodes) is proposed over SW-WSN. In addition, two optimal H-node allocation methods are also developed for the cooperative localisation method. The significance of the proposed method in reducing localisation error, energy consumption, and bandwidth requirement is illustrated by simulations and extensive experiments on a real WSN testbed.

On Applicability of Network Coding Technique for 6LoWPAN-based Sensor Networks.

In this paper, the applicability of the network coding technique in 6LoWPAN-based sensor multihop networks is examined. The 6LoWPAN is one of the standards proposed for the Internet of Things architecture. Thus, we can expect the significant growth of traffic in such networks, which can lead to overload and decrease in the sensor network lifetime. The authors propose the inter-session network coding mechanism that can be implemented in resource-limited sensor motes. The solution reduces the overall traffic in the network, and in consequence, the energy consumption is decreased. Used procedures take into account deep header compressions of the native 6LoWPAN packets and the hop-by-hop changes of the header structure. Applied simplifications reduce signaling traffic that is typically occurring in network coding deployments, keeping the solution usefulness for the wireless sensor networks with limited resources. The authors validate the proposed procedures in terms of end-to-end packet delay, packet loss ratio, traffic in the air, total energy consumption, and network lifetime. The solution has been tested in a real wireless sensor network. The results confirm the efficiency of the proposed technique, mostly in delay-tolerant sensor networks.

Two energy and time-efficient data dissemination protocols for large-scale wireless sensor networks

In a wireless sensor network (WSN) where positioning information is not assumed or is partially available, efficient data access is a very challenging issue especially in an environment with multiple random sources and sinks. Few years back, an interesting rumor-agent based protocol named rumor routing (RR) was proposed. The key concept RR introduced is that the agents are used to spread request and environmental information in an attempt to not affect the limited bandwidth of WSN. Moreover, in this protocol, the cooperation of agents helps build optimal paths leading to events with low energy cost. In this paper, we first review the major RR extensions classifying them into two categories: location-based and free location-based. Then, based on this classification, we propose two rumor-based data dissemination protocols designed to ensure both energy and time-efficient data access for realistic WSN deployments. The two proposed protocols have been compared with the relevant previous works through simulation campaigns. Performance evaluation results show that our proposed protocols achieve significant improvements in terms of data accessibility, generated traffic, and query latency.

Collaborative Energy and Information Transfer in Green Wireless Sensor Networks for Smart Cities

Smart city is able to make the city source and infrastructure more efficiently utilized, which improves the quality of life for citizens. In this framework, wireless sensor networks (WSNs) play an important role to collect, process, and analyze the corresponding information. However, the massive deployment of WSNs consumes a significant energy consumption, which has raised the growing demand for green WSNs for smart cities. Exploiting the recent advance in collaborative energy and information transfer to power the WSNs and transmit the data has been considered a promising approach to realize the green WSNs for smart cities. We propose an architecture design of the green WSNs for smart cities, by exploiting the collaborative energy and information transfer protocol, and illustrate the challenging issues in this design. To achieve a green system design, the sensor nodes in WSNs harvest the energy simultaneously with the information decoding (ID) from the received radio frequency signals. Specifically, the energy-constrained sensor nodes partition the received signals into two independent groups to perform energy harvesting (EH) and ID. The sensor nodes then use the harvested energy to amplify and forward the information signals. We study the joint optimization of subcarrier grouping, subcarrier pairing, and power allocation such that the transmission rate performance is maximized with the EH constraint. The joint optimization problem is solved via dual decomposition after transforming it into an equivalent convex optimization problem. Simulation results tested with the real WSNs system data indicate that the performance of our proposed protocol can be significantly improved.

LESS: Link Estimation with Sparse Sampling in Intertidal WSNs.

Deploying wireless sensor networks (WSN) in the intertidal area is an effective approach for environmental monitoring. To sustain reliable data delivery in such a dynamic environment, a link quality estimation mechanism is crucial. However, our observations in two real WSN systems deployed in the intertidal areas reveal that link update in routing protocols often suffers from energy and bandwidth waste due to the frequent link quality measurement and updates. In this paper, we carefully investigate the network dynamics using real-world sensor network data and find it feasible to achieve accurate estimation of link quality using sparse sampling. We design and implement a compressive-sensing-based link quality estimation protocol, , which incorporates both spatial and temporal characteristics of the system to aid the link update in routing protocols. We evaluate in both real WSN systems and a large-scale simulation, and the results show that can reduce energy and bandwidth consumption by up to while still achieving more than link quality estimation accuracy.

Joint Application Admission Control and Network Slicing in Virtual Sensor Networks

We focus on the problem of managing a shared physical wireless sensor network (WSN) where a single network infrastructure provider leases the physical resources of the networks to application providers to run/deploy specific applications/services. In this scenario, we solve jointly the problems of application admission control (AAC), that is, whether to admit the application/service to the physical network, and wireless sensor network slicing (SNS), that is, to allocate the required physical resources to the admitted applications in a transparent and effective way. We propose a mathematical programming framework to model the joint AAC-SNS problem which is then leveraged to design effective solution algorithms. The proposed framework is thoroughly evaluated on realistic WSNs infrastructures.

FLOPSYNC-QACS

The development of distributed real-time systems often relies on clock synchronization. However, achieving precise synchronization in the field of Wireless Sensor Networks (WSNs) is hampered by competing design challenges, such as energy consumption and cost constraints, e.g., in Internet of Things applications. For these reasons many WSN hardware platforms rely on a low frequency clock crystal to provide the local timebase. Although this solution is inexpensive and allows for a remarkably low energy consumption, it limits the resolution at which time can be measured. The FLOPSYNC synchronization scheme provides low-energy synchronization that takes into account the quartz crystal imperfections. The main limitation of the approach are the effects of quantization. In this paper we propose a clock synchronization scheme that explicitly takes into account quantization effects caused by low frequency clock crystal, thus addressing the clock synchronization issue in cost-sensitive WSN node platforms. The solution adopts switched control for minimizing the effect of quantization, with minimal overhead. We provide experimental evidence that the approach manages to reach a synchronization error of at most 1 clock tick in a real WSN.

Minimization of delay and collision with cross cube spanning tree in wireless sensor networks

The wireless sensor network (WSN) is a system containing the event detection and the data gathering abilities. The data gathering mechanism is the fundamental but important procedure in the WSN environment. The way of the data gathering majorly affects the efficiency of WSNs on retrieving data at the sink node. It is generally known that the clustering techniques are effective to reduce the energy consumption in the WSNs. However, the research on the packet collision and the transmission delay in the Cluster based routing algorithm still remains limited. The packet loss and the transmission delay will happen more often due to collision and as such it will have negative impact on the WSN performance. In addition, the transmission delay phenomenon in the WSN may cause the inefficient result in the data gathering process. Unfortunately, it is usually neglected in the existing literature. To overcome the drawback of transmission delay and collision, a cluster-based converge cast tree (CCCT) protocol is proposed in this paper. The core of this protocol is to construct a cross cube spanning tree topology control algorithm. The proposed protocol performance is analyzed theoretically, which demonstrate that the protocol is efficient in avoiding packet collision and reducing the transmission delay. Finally, the protocol is examined by the simulations. The simulation results indicate that the proposed CCCT structure and algorithms outperform the existing approaches significantly in the realistic WSN environment.

EEECARP

WSN is a promising approach for variety of different real time applications. Different Routing protocols for WSNs are very effective challenge in present days because of scalability, efficient energy utilization and robustness in large number of wireless sensor networks with consists of more number of sensor nodes. LEATCH is a traditional routing protocol for energy optimization in WSNs. However, LEATCH cannot scale performance for large scale wireless sensor networks and difficulty to apply effective utilization of real time wireless sensor networks. So, in this paper the authors propose to develop a novel Energy Efficiency and Event Clustering Adaptive Routing Protocol (EEECARP) for WSN. The main designing feature of their proposed approach is as follows: Energy Efficiency, Dynamic Event Clustering and multi hop relay configuration with residual energy available on relay nodes in wireless sensor networks. The simulation results show that authors' routing protocol achieves convenient and effective better performance in formation of clusters with relay sensor nodes in wireless sensor networks.

FLOPSYNC-QACS: Quantization-aware clock synchronization for wireless sensor networks

Abstract Distributed real-time systems often rely on clock synchronization. However, the achievement of precise synchronization in Wireless Sensor Networks (WSNs) is hampered by competing design challenges, which finally causes many WSN hardware platforms to rely on low frequency clock crystal for local timebase provision. Although this solution is inexpensive and with a remarkably low energy consumption, it limits the resolution at which time can be measured. The FLOPSYNC synchronization scheme was then introduced to compensate for possible quartz crystal imperfections. The main limitation of FLOPSYNC is that it does not account for the effects of quantization. In this paper we propose a switched control variant of the base FLOPSYNC scheme to address quantization explicitly in the compensator design, providing clock synchronization in cost-sensitive WSN node platforms with a minimal additional overhead. Experimental evidence is given that the approach reaches a synchronization error of at most 1 clock tick in a real WSN.

An efficient lossy link localization approach for wireless sensor networks

Network fault management is crucial for a wireless sensor network (WSN) to maintain a normal running state because faults (e.g., link failures) often occur. The existing lossy link localization (LLL) approach usually infers the most probable failed link set first, and then gives the fault hypothesis set. However, the inferred failed link set contains many possible failures that do not actually occur. That quantity of redundant information in the inferred set can pose a high computational burden on fault hy-pothesis inference, and consequently decreases the evaluation accuracy and increases the failure localization time. To address the issue, we propose the conditional information entropy based redundancy elimination (CIERE), a redundant lossy link elimination approach, which can eliminate most redundant information while reserving the important information. Specifically, we develop a probabilistically correlated failure model that can accurately reflect the correlation between link failures and model the nonde-terministic fault propagation. Through several rounds of mathematical derivations, the LLL problem is transformed to a set-covering problem. A heuristic algorithm is proposed to deduce the failure hypothesis set. We compare the performance of the proposed approach with those of existing LLL methods in simulation and on a real WSN, and validate the efficiency and effec-tiveness of the proposed approach.

A weighted centroid localization algorithm for randomly deployed wireless sensor networks

Localization is one of the important requirements in wireless sensor networks for tracking and analyzing the sensed data/events. In most of the applications of wireless sensor networks, the event information without its location information has no significance. The well known traditional Distance-Vector Hop (DV Hop) algorithm and weighted centroid DV Hop based algorithms can be easily implemented in real wireless sensor networks with low cost and no additional hardware requirement, but it has poor localization accuracy and high power consumption. In order to avoid these limitations, a weighted centroid DV-Hop algorithm is proposed in this paper. The proposed algorithm uses weights that consider the influence of different factors such as number of anchors, communication radius, and nearest anchor to determine location of unknown node. Simulation results and theoretical analysis prove that proposed algorithm outperforms the traditional DV-Hop algorithm in terms of localization error and power consumption.