Deployed Wireless Sensor Research Articles

Wireless sensor network energy efficient coverage method based on intelligent optimization algorithm

As a basic and fundamental problem in wireless sensor network (WSN), the network coverage greatly reflects the performance of information transmission in WSN. In order to achieve a good balance between target coverage and energy consumption, in this paper, we propose a novel wireless sensor network energy efficient coverage method based on genetic algorithm. Particularly, the goal of this work is cover a 2D sensing area via selecting a minimum number of sensors. Moreover, the deployed wireless sensors should be connected to let each sensor be connected a path to the base station. Afterwards, genetic algorithm is used to compute the minimum number of potential position to let all target be k-covered and all sensor nodes be m-connected, and each chromosome is set to be the number of potential positions. Finally, we provide a simulation to test the performance of the proposed method, and simulation results demonstrate that the proposed method can achieve high degree of target coverage without wasting extra energy.

Feasibility for Utilizing IEEE 802.15.4 Compliant Radios Inside Rotating Electrical Machines for Wireless Condition Monitoring Applications

The conventional condition monitoring systems for rotating electrical machines are predominantly based on externally available signals, while the internal signals, especially on the rotor, remain relatively unexplored. To access these internal signals, wireless sensors offer an effective solution in terms of cost and ease of deployment. However, the environment inside an electrical machine deviates from a typical wireless RF application and communication link reliability under such scenarios needs to be assessed. This paper explores the feasibility of utilizing the ubiquitous IEEE 802.15.4-2.4 GHz compliant radios for wireless sensor deployment inside an electrical machine. A series of measurements are made to experimentally investigate the link quality characteristics under the prominent environmental factors such as the multipath effect, Doppler shift, magnetic field interference, and shielding effect of the enclosure. Packet reception rate, received signal strength and link quality indicator are used as the metrics to quantify the link performance. It is observed that contrary to popular belief, the link quality between nodes inside the machine is not deteriorated and in fact is found to be enhanced. A poor link quality is observed for the connection between nodes placed inside and outside the machine. Moreover, the link quality is highly dependent on spatial position and prone to interference. However, a sufficiently stable link can be established at the expense of high transmit power. These experimental findings indicate that wireless RF-based sensor nodes can be deployed inside an electrical machine and it opens an opportunity to explore invasive condition monitoring systems.

Underwater Self Deployment of Wireless Sensors for Maximum Coverage and Connectivity using K-Means Clustering

By virtue of abundant research being executed in terrestrial wireless sensor networks, we get enlightened about the different advantages of using wireless sensors and implemented these wireless sensors in underwater to probe the area below the sea for its various applications. The most crucial task in underwater 3D space is the node deployment such that sensors can cover the maximum area while simultaneously maintaining the connectivity with the base station. Due to the advantages of self deployment where no pre calculations and no human interventions are needed, we proposed a distributed move restricted self deployment underwater wireless sensor network. The main emphasis is on maximizing the coverage area by the sensor nodes and simultaneously maintaining the connectivity with the base station. Firstly, the maximum coverage has achieved by removing the interference between the sensor nodes which minimize the coverage overlap and thus more area can be covered which improves the network coverage. Then by using K-Means clustering, all the sensors nodes were divided into clusters, having one centroid for each cluster. Further all these centroids have been interconnected and then by finding the centroid which is nearest to the sink by Euclidean distance formula, we connect it to the sink.

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.

Encapsulation of real‐time IoT CoAP traffic

AbstractThe Constrained Application Protocol (CoAP) has been instrumental to the deployment of wireless sensors and actuators in Internet of Things low‐power low‐rate networks. Typically, a large number of such devices interact with a single gateway that routes traffic to servers where complex processing is performed. CoAP provides a highly efficient end‐to‐end mechanism that relies on the User Datagram Protocol for transport, and it is characterized by both low throughput and low latency. Because Internet firewalls typically filter User Datagram Protocol traffic as it traverses from gateways to servers, the use of Transmission Control Protocol (TCP) encapsulation becomes a viable alternative. This solution, however, is negatively affected by network packet loss that, due to TCP inherent retransmissions, severely degrades latency, reducing system responsibility. In this paper, we analyze the effect of TCP‐encapsulated CoAP and propose a mechanism that overcomes these limitations without having to change network topologies or modifying protocol functionality.

Building Scalable Cyber-Physical-Social Networking Infrastructure Using IoT and Low Power Sensors

Wireless sensors are an important component to develop the Internet of Things (IoT) Sensing infrastructure. There are enormous numbers of sensors connected with each other to form a network (well known as wireless sensor networks) to complete the IoT Infrastructure. These deployed wireless sensors are with limited energy and processing capabilities. The IoT infrastructure becomes a key factor to building cyber-physical-social networking infrastructure, where all these sensing devices transmit data toward the cloud data center. Data routing toward cloud data center using such low power sensor is still a challenging task. In order to prolong the lifetime of the IoT sensing infrastructure and building scalable cyber infrastructure, there is the requirement of sensing optimization and energy efficient data routing. Toward addressing these issues of IoT sensing, this paper proposes a novel rendezvous data routing protocol for low-power sensors. The proposed method divides the sensing area into a number of clusters to lessen the energy consumption with data accumulation and aggregation. As a result, there will be less amount of data stream to the network. Another major reason to select cluster-based data routing is to reduce the control overhead. Finally, the simulation of the proposed method is done in the Castalia simulator to observe the performance. It has been concluded that the proposed method is energy efficient and it prolongs the networks lifetime for scalable IoT infrastructure.

Design of Probability Density Function Targeting Energy Efficient Network for Coalition Based WSNs

Energy consumption is one of the important issues in wireless sensor network that rely on non chargeable batteries for power. Also, the sensor network has to maintain a desired sensing coverage area along with periodically sending of the sensed data to the base station. Therefore, coverage and the lifetime are the two important issues that need to be addressed. Effective deployment of wireless sensors is a major concern as the coverage and lifetime of any wireless sensor network depends on it. In this paper, we propose the design of a Probability Density Function (PDF) targeting the desired coverage, and energy efficient node deployment scheme. The suitability of the proposed PDF based node distribution to model the network architecture considered in this work has been analyzed. The PDF divides the deployment area into concentric coronas and provides a probability of occurrence of a node within any corona. Further, the performance of the proposed PDF is evaluated in terms of the coverage, the number of transmissions of packets and the lifetime of the network. The scheme is compared with the existing node deployment schemes based on various distributions. The percentage gain of the proposed PDF based node deployment is 32\(\%\) more than that when compared with the existing schemes. Thus, the simulation results obtained confirm the schemes superiority over the other existing schemes.

Performance evaluation of linked stream data processing engines for situational awareness applications

SummaryThe easy deployment of wireless sensors allows the development of context‐aware applications that could react to the environment changes and users preferences. For example, data extracted from mobile phones, IoT devices, and embedded computers in buses and taxis could be processed in real time to understand city dynamics. However, gathering and real‐time processing of relevant data are still a challenging task. For instance, the high volume of streaming data requires real‐time processing in order to generate immediate mitigation actions. Complex‐event processing (CEP) techniques and predictive analytics have been recently proposed for analyzing streaming data in order to generate fast insights and then take suitable actions according to the situations' changes. These techniques could be also used to predict future situations and react to them properly before happening. The work presented in this paper focuses mainly on the performance evaluation of three CEP engines, CQELS, C‐SPARQL, and ETALIS, that are widely used by researchers for linked stream data processing. Experiments have been conducted using two existing benchmarks, CityBench and SP2Bench. Several performance metrics have been evaluated to assess on their ability to process high streaming data using complex queries. Results are reported to show the efficiency and scalability of these CEP engines for both social‐based data (SP2Bench) and physical‐based data (CityBench). Reported results show that ETALIS outperforms CQELS and C‐SPARQL in terms of throughput and memory utilization. ETALIS was integrated as a use case scenario for occupancy and air quality comfort in energy‐efficient buildings. Results show the usefulness of CEP for real‐time data processing and reasoning.

Optimized Charging Scheduling with Single Mobile Charger for Wireless Rechargeable Sensor Networks

Due to the rapid development of wireless charging technology, the recharging issue in wireless rechargeable sensor network (WRSN) has been a popular research problem in the past few years. The weakness of previous work is that charging route planning is not reasonable. In this work, a dynamic optimal scheduling scheme aiming to maximize the vacation time ratio of a single mobile changer for WRSN is proposed. In the proposed scheme, the wireless sensor network is divided into several sub-networks according to the initial topology of deployed sensor networks. After comprehensive analysis of energy states, working state and constraints for different sensor nodes in WRSN, we transform the optimized charging path problem of the whole network into the local optimization problem of the sub networks. The optimized charging path with respect to dynamic network topology in each sub-network is obtained by solving an optimization problem, and the lifetime of the deployed wireless sensor network can be prolonged. Simulation results show that the proposed scheme has good and reliable performance for a small wireless rechargeable sensor network.

NDSC based methods for maximizing the lifespan of randomly deployed wireless sensor networks for infrastructures monitoring

Abstract This paper addresses the problem of wireless sensor network (WSN) lifetime maximization, under limited available energy constraint. The investigations have shown that the valid amount of energy was not used by the existing disjoint set covers (DSC) based scheduling method for WSNs lifetime maximization, because of the DSC constraints. Instead, we suggest in this paper to schedule non-disjoint sets covers (NDSC) for maximizing WSNs lifetime. Thus, we have formulated this problem, using the integer linear programming (ILP) mathematical model, then we developed an approach based on genetic algorithm GA to find the maximal lifespan. As main contributions, we investigated and designed a new method using the NDSC instead of the DSC. This approach removes the latter’s constraint and gives the opportunity to a sensor to participate in more than one cover, and thereby improves significantly the WSNs’ lifetime. We proposed an exact method and a genetic algorithms (GA) for the NDSC efficient scheduling for the WSN lifetime maximization. The exact method lies on the integer linear programming (ILP). For the GA based heuristics, we used a specific arrangement of chromosomes combining several crossover and mutation strategies for encoding the solutions. We provided experimental results for different instances involving sensors with non-identical amount of initial energy and power consumption. In addition, we provided comparative analysis results between the solutions obtained by our both methods and the existing methods based on DSC. The comparisons of the run times and the solutions’ quality revealed the dominance of the solutions yielded by our methods based on the NDSC compared to those based on the DSC.

On Efficient Deployment of Wireless Sensors for Coverage and Connectivity in Constrained 3D Space.

Sensor networks have been used in a rapidly increasing number of applications in many fields. This work generalizes a sensor deployment problem to place a minimum set of wireless sensors at candidate locations in constrained 3D space to k-cover a given set of target objects. By exhausting the combinations of discreteness/continuousness constraints on either sensor locations or target objects, we formulate four classes of sensor deployment problems in 3D space: deploy sensors at Discrete/Continuous Locations (D/CL) to cover Discrete/Continuous Targets (D/CT). We begin with the design of an approximate algorithm for DLDT and then reduce DLCT, CLDT, and CLCT to DLDT by discretizing continuous sensor locations or target objects into a set of divisions without sacrificing sensing precision. Furthermore, we consider a connected version of each problem where the deployed sensors must form a connected network, and design an approximation algorithm to minimize the number of deployed sensors with connectivity guarantee. For performance comparison, we design and implement an optimal solution and a genetic algorithm (GA)-based approach. Extensive simulation results show that the proposed deployment algorithms consistently outperform the GA-based heuristic and achieve a close-to-optimal performance in small-scale problem instances and a significantly superior overall performance than the theoretical upper bound.

Optimization and Design of Wireless Systems for the Implementation of Context Aware Scenarios in Railway Passenger Vehicles

In this paper, intra-wagon wireless communication performance is analyzed, in order to account for inherent scenario complexity in the deployment phase of wireless systems toward the implementation of a context-aware environment. A real commercial passenger wagon has been simulated by means of an in-house-developed 3-D ray launching code, accounting for embedded wagon elements as well as variable user densities within the passenger wagon. Onboard measurements of a designed and deployed wireless sensor network are obtained, showing good agreement with wireless channel estimations for two different frequencies of operation. Energy consumption behavior and user density impact have also been analyzed and estimated as a function of network topology and the operational mode. These results can aid in wireless transceivers deployment configurations, in order to minimize power consumption, optimize interference levels, and increase overall service performance.

Novel Algorithms for Reliability Evaluation of Remotely Deployed Wireless Sensor Networks

Wireless sensor networks can be deployed in remote areas for monitoring rainforest, bio-diversity, detecting forest fire or even surveillance. In such remote monitoring applications, sensor nodes a...

Development of a Wireless Sensors Network for Greenhouse Monitoring and Control

Wireless sensor networks (WSN) could be used to monitor and control many parameters of environment such as temperature, humidity, and radiation leakage. In greenhouse the weather and soil should be independent of the natural agents. To achieve this condition a wireless sensor nodes could be deployed and communicate with a central base station to measure and transmit the sensed required environment factors. In this paper a WSN was implemented by deployed wireless sensor nodes in a greenhouse with temperature, humidity, moisture light, and CO2 sensors. The proposed model was built and tested, and the result shows an excellent improvement in the sensed parameters. To control the environmental factors, the used microcontroller programmed to control the parameters according to preset values, or manually through a user interface panel.

Development of a Wireless Sensors Network for Greenhouse Monitoring and Control

Wireless sensor networks (WSN) could be used to monitor and control many parameters of environment such as temperature, humidity, and radiation leakage. In greenhouse the weather and soil should be independent of the natural agents. To achieve this condition a wireless sensor nodes could be deployed and communicate with a central base station to measure and transmit the sensed required environment factors. In this paper a WSN was implemented by deployed wireless sensor nodes in a greenhouse with temperature, humidity, moisture light, and CO2 sensors. The proposed model was built and tested, and the result shows an excellent improvement in the sensed parameters. To control the environmental factors, the used microcontroller programmed to control the parameters according to preset values, or manually through a user interface panel.

Measurement Based As-You-Go Deployment of Two-Connected Wireless Relay Networks

Motivated by the need for impromptu or as-you-go deployment of wireless sensor networks in some situations, we study the problem of optimal sequential deployment of wireless sensors and relays along a line (e.g., a forest trail) of unknown length. Starting from the sink node (e.g., a base station), a ”deployment agent„ walks along the line, stops at equally spaced points (”potential„ relay locations), placing relays at some of these points, until he reaches a location at which the source node (i.e., the sensor) needs to be placed, the objective being to create a multihop wireless relay network between the source and the sink. The deployment agent decides whether to place a relay or not at each of the potential locations, depending upon the link quality measurements to the previously placed relays. In this article, we seek to design efficient deployment algorithms for this class of problems, to achieve the objective of 2-connectivity in the deployed network. We ensure multi-connectivity by allowing each node to communicate with more than one neighbouring node. By proposing a network cost objective that is additive over the deployed relays, we formulate the relay placement problem as a Markov decision process. We provide structural results for the optimal policy and evaluate the performance of the optimal policy via numerical exploration. Computation of such an optimal deployment policy requires a statistical model for radio propagation; we extract this model from the raw data collected via measurements in a forestlike environment. To validate the results obtained from the numerical study, we provide an experimental study of algorithms for 2-connected network deployment.

Hybrid subnet-based node failure recovery formal procedure in wireless sensor and actor networks

An unattended deployment of wireless sensor and actor networks in a harsh and inhospitable environment may cause its failure by partitioning it into the disjoint segments. Although many variants of this problem are addressed using different approaches, it still needs to be investigated due to its various applications. In this article, an efficient, localized, hybrid failure detection and recovery algorithm is proposed which assumes planned deployment of nodes. The algorithm is approximate and distributed as its topology is partitioned into subnets localizing failure recovery procedure and efficient as the time complexity is reduced from nondeterministic polynomial-time–hard to polynomial time. The algorithm is hybrid as pre-failure planning and post-failure recovery is assumed for the critical nodes. Graph-based model is designed to represent static part which is then transformed into a formal model using Vienna Development Method—specification language. The static model consists of subnets, circular topology, sensors, actors, and gateways as composite objects in Vienna Development Method—specification language. The dynamic model is developed by defining its state space, functions, and possible operations to describe the failure recovery procedure. Invariants are defined on static model to assure correctness, and pre/post conditions are used in the dynamic model to control the behavior preventing system to enter into an unwanted situation. The formal specification is analyzed using Vienna Development Method—specification language Toolbox to visualize the model.

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.

Cluster-based optimal wireless sensor deployment for structural health monitoring

A well-designed wireless sensor deployment method not only directly influences the number of deployed sensors and data accuracy, but also influences on network topology. As most of the energy cost comes from the transmission and receiving of data packets, clustering optimization in wireless sensor network becomes an important issue for energy-efficient coordination among the densely deployed nodes for data communication. In a typical hierarchical wireless sensor network, total intra-cluster communication distance and total distance of cluster heads to base station depend on number of cluster heads. This work presents a novel approach by selecting the number of clusters in hierarchical wireless sensor network. We analyze and demonstrate the validity of the cluster optimization for wireless sensor deployment using an example of a numerically simulated simply supported truss, in terms of efficient use of the constrained wireless sensor network resources. Followed by a cluster-based optimization framework, we show how to adopt our approach to achieve scalable and efficient deployment, through a comprehensive optimization study of a realistic wireless structural health monitoring system. Finally, we suggest optimal deployment scheme based on the comparative performance evaluation results in the case study.

Mean Territorial Energy Based Clustering Protocol for Randomly Deployed Wireless Sensor Networks

Clustering algorithms can balance the power consumption of energy constraint wireless sensor networks. This paper proposes a new clustering protocol called Mean Territorial Energy Based Clustering Protocol (MTEP) for randomly deployed wireless sensor networks. In MTEP, cluster heads are selected according to residual energy and location information of a node in current round as well as mean territorial energy and total base station distance of node’s corresponding cluster territory in previous round. Energy consumption in conventional protocols becomes unbalanced because of clusters having different lengths. Proposed MTEP protocol addresses this problem by setting thresholds on cluster length and node to cluster head distance for producing equal length clusters. Simulation results show that MTEP protocol extends network lifetime and stability with reduction in energy dissipation compared to other clustering protocols such as LEACH and REAC.