Framework For Sensor Networks Research Articles

Energy-efficient compressed data aggregation in underwater acoustic sensor networks

In this paper, we propose an energy-efficient compressed data aggregation framework for three-dimensional underwater acoustic sensor networks (UASNs). The proposed framework consists of two layers, where the goal is to minimize the total energy consumption of transmitting the data sensed by nodes. The lower layer is the compressed sampling layer, where nodes are divided into clusters. Nodes are randomly selected to conduct sampling, and then send the data to the cluster heads through random access channels. The upper layer is the data aggregation layer, where full sampling is adopted. We also develop methods to determine the number of clusters and the probability that a node participates in data sampling. Simulation results show that the proposed framework can effectively reduce the amount of sampling nodes, so as to reduce the total energy consumption of the UASNs.

Energy-Neutral Design Framework for Supercapacitor-Based Autonomous Wireless Sensor Networks

To design autonomous wireless sensor networks (WSNs) with a theoretical infinite lifetime, energy harvesting (EH) techniques have been recently considered as promising approaches. Ambient sources can provide everlasting additional energy for WSN nodes and exclude their dependence on battery. In this article, an efficient energy harvesting system which is compatible with various environmental sources, such as light, heat, or wind energy, is proposed. Our platform takes advantage of double-level capacitors not only to prolong system lifetime but also to enable robust booting from the exhausting energy of the system. Simulations and experiments show that our multiple-energy-sources converter (MESC) can achive booting time in order of seconds. Although capacitors have virtual recharge cycles, they suffer higher leakage compared to rechargeable batteries. Increasing their size can decrease the system performance due to leakage energy. Therefore, an energy-neutral design framework providing a methodology to determine the minimum size of those storage devices satisfying energy-neutral operation (ENO) and maximizing system quality-of-service (QoS) in EH nodes, when using a given energy source, is proposed. Experiments validating this framework are performed on a real WSN platform with both photovoltaic cells and thermal generators in an indoor environment. Moreover, simulations on OMNET++ show that the energy storage optimized from our design framework is utilized up to 93.86%.

Flocking, Formation Control, and Path Following for a Group of Mobile Robots

This paper presents a multifunctional control law for a group of mobile robots. The controller concurrently integrates flocking, formation control, and path following, as basic components in a navigation framework for mobile sensor networks tasked with searching and harvesting information. The convergence of the closed loop system is ensured using Ryan’s invariance principle; the particular tool being necessary due to the use of a nonsmooth artificial potential field for formation control. Simulations and experiments corroborate the theoretical convergence results.

Plataforma autoconfigurable de monitoreo remoto para aplicaciones móviles en WSAN

Wireless networks have been evolving toward higher complexity applications. It makes sense not to expect an one-fits-all application framework. These applications require a particular framework profile in order to solve particular issues. In this paper we propose a Wireless Sensor and Actuator Network (WSAN) framework profile, which solves the particular issue of writing and reading memory slots on the sensor node's microcontroller. As simple as it sounds this application opens the framework to a wide set of possibilities, which could be reading a sensed data, or even switching the purpose of the whole node just by changing a pointer value on the remote microcontroller memory. This framework is suitable for a star topology amidst an IEEE802.15.4 standard. It is also able to control, process and communicate successfully among the network nodes.

Traffic Generation Rate Control of Wireless Sensor and Actuator Networks

The traffic generation rate of the network used for cyber-physical systems is a crucial design parameter since it directly affects the stability of physical systems and the congestion level of communication systems. In this paper, we propose a novel modeling framework of the general wireless sensor and actuator networks of cyber-physical systems where the sensor-controller and controller-actuator sides communicate over a lossy network. The performance model is then used to derive the optimal traffic generation rate of sensors and controllers for minimizing the maximum outage probability of the stability constraint of the control systems while guaranteeing a schedulability constraint. The resulting problem is a non-linear optimization problem which allows to obtain the global optimum. An efficient approximation converts the proposed optimization problem into a linear programming problem. One interesting finding is that the optimal solution assigns higher traffic generate rate on the controller-actuator link compared to the one of the sensor-controller link since the actuating-link is more critical to guarantee the stability of the control systems.

Design and Application of a VOC-Monitoring System Based on a ZigBee Wireless Sensor Network

Monitoring volatile organic compound (VOC) pollution levels in indoor environments is of great importance for the health and comfort of individuals, especially considering that people currently spend >80% of their time indoors. The primary aim of this paper is to design a low-power ZigBee sensor network and internode data reception control framework to use in the real-time acquisition and communication of data concerning air pollutant levels from VOCs. The network consists of end device sensors with photoionization detectors, routers that propagate the network over long distances, and a coordinator that communicates with a computer. The design is based on the ATmega16 microcontroller and the Atmel RF230 ZigBee module, which are used to effectively process communication data with low power consumption. Priority is given to power consumption and sensing efficiency, which are achieved by incorporating various smart tasking and power management protocols. The measured data are displayed on a computer monitor through a graphical user interface. The preliminary experimental results demonstrate that the wireless sensor network system can monitor VOC concentrations with a high level of accuracy and is thus suitable for automated environmental monitoring. Both good indoor air quality and energy conservation can be achieved by integrating the VOC monitoring system proposed in this paper with the residential integrated ventilation controller.

A novel authentication and access control framework in wireless sensor networks

Wireless Sensor Networking continues to evolve as one of the most challenging research areas. Considering the insecure nature of these networks and the fact that sensor nodes are distributed in a hostile environment, having a well-implemented security scheme is absolutely essential. Bearing in mind the important security services like authentication and access control, we have proposed a novel security framework for these networks. The new framework is based on Kerberos authentication and access control system. The Kerberos has been adopted for WSNs by utilizing Bloom Filter data structure and Elliptic Curve cryptography. In the proposed scheme, Bloom Filter data structure is used in a novel way; we have used this data structure to get rid of Public Key’s certificates. By combining Bloom Filter data structure and Elliptic Curve cryptography, we achieved a very light robust security framework that offers Authentication, Access Control, and key sharing services. The analysis results showed that our scheme provides more security services and is more robust in the presence of attacks compared to the previous schemes. In contrast, simulation results indicated that our system had significant improvements over the other schemes in many aspects such as power and time expenditure.

Staying Alive

Self-sustainability is a crucial step for modern sensor networks. Here, we offer an original and comprehensive framework for autonomous sensor networks powered by renewable energy sources. We decompose our design into two nested optimization steps: the inner step characterizes the optimal network operating point subject to an average energy consumption constraint, while the outer step provides online energy management policies that make the system energetically self-sufficient in the presence of unpredictable and intermittent energy sources. Our framework sheds new light into the design of pragmatic schemes for the control of energy-harvesting sensor networks and permits to gauge the impact of key sensor network parameters, such as the battery capacity, the harvester size, the information transmission rate, and the radio duty cycle. We analyze the robustness of the obtained energy management policies in the cases where the nodes have differing energy inflow statistics and where topology changes may occur, devising effective heuristics. Our energy management policies are finally evaluated considering real solar radiation traces, validating them against state-of-the-art solutions, and describing the impact of relevant design choices in terms of achievable network throughput and battery-level dynamics.

A data delivery framework for cognitive information-centric sensor networks in smart outdoor monitoring

Cognitive information-centric sensor networks represent a paradigm of wireless sensor networks in which sensory information is identified from the network using named-data, and elements of cognition are used to deliver information to the sink with quality that satisfies the end-user requirements. Specialized nodes called Local Cognitive Nodes (LCNs) implement knowledge representation, reasoning and learning as elements of cognition in the network. These LCNs identify user-requested sensory information, and establish data delivery paths to the sink by prioritizing Quality of Information (QoI) attributes (e.g., latency, reliability, and throughput) at each hop based on the network traffic type. Analytic Hierarchy Processing (AHP) is the reasoning tool used to identify these paths based on QoI-attribute priorities set by the user. From extensive simulations, parameters that can be controlled to improve the values of QoI attributes along each hop were identified, and performance of the AHP-based data-delivery technique was compared with two traditional data-centric techniques in terms of lifetime and QoI attribute performance. It was found that the use of cognition improves the number of successful transmissions to the sink by almost 30%, while closely adapting the data delivery paths to the QoI requirements of the user.

QoS Aware Trust Metric based Framework for Wireless Sensor Networks

Wireless Sensor Networks have gained popularity due to their real time applications and low-cost nature. These networks provide solutions to scenarios that are critical, complicated and sensitive like military fields, habitat monitoring, and disaster management. The nodes in wireless sensor networks are highly resource constrained. Routing protocols are designed to make efficient utilization of the available resources in communicating a message from source to destination. In addition to the resource management, the trustworthiness of neighboring nodes or forwarding nodes and the energy level of the nodes to keep the network alive for longer duration is to be considered. This paper proposes a QoS Aware Trust Metric based Framework for Wireless Sensor Networks. The proposed framework safeguards a wireless sensor network from intruders by considering the trustworthiness of the forwarder node at every stage of multi-hop routing. Increases network lifetime by considering the energy level of the node, prevents the adversary from tracing the route from source to destination by providing path variation. The framework is built on NS2 Simulator. Experimental results show that the framework provides energy balance through establishment of trustworthy paths from the source to the destination. (C) 2015 The Authors. Published by Elsevier B.V.

Monitoring Moving Objects Using Aerial Mobile Sensors

We propose an optimization-based path-planning framework for an aerial mobile sensor network. The purpose of the path planning is to monitor a set of moving surface objects. The algorithm provides collision-free mobile sensor trajectories that are feasible with respect to user-defined vehicle dynamics. The objective of the resulting optimal control problem is to minimize the uncertainty of the objects, represented as the trace of the augmented state and parameter estimation error covariance. The dynamic optimization problem is discretized into a large-scale nonlinear programming problem using the direct transcription method known as simultaneous collocation. The optimization problem is solved with a receding horizon and both a field experiment and a numerical simulation illustrate the approach.

Analysis and control of quality of information (QoI) for wireless sensor networks

The Quality of Information QoI is an important metric that sensor networks provide to applications. In this paper, a hypothesis testing based framework for sensor networks is considered for analysing the QoI performance under the cases of Single Sensor Single Decision-Making SSSD, Multi-Sensor Single Decision-Making MSSD and Multi-Sensor Multiple Decision-Making MSMD detection systems, respectively. The detection probability, false alarm probability and average error probability are treated as the QoI metrics and some explicit solutions are derived, which can capture the impact of various parameters on the QoI metrics. Owing to the dynamic and time-varying characteristics of the target signal of interest, a rate-based QoI control scheme is proposed to effectively control the QoI requirements by adaptively adjusting the data sampling rates of sensors. Numerical results are presented for evaluating the performance of the SSSD, MSSD and MSMD systems and validating the effectiveness of the QoI control scheme.

A hybrid wireless sensor network framework for range-free event localization

In event localization, wireless sensors try to locate the source of an event from its emitted power. This is more challenging than sensor localization as the power level at the source of an event is neither predictable with precision nor can be controlled. Considering the emerging trend of long sensing range for cost-effective sensor deployment, locating events within a region much smaller than the sensing area of a single sensor has gained research interest. This paper proposes the first range-free event localization framework, which avoids expensive hardware needed by the range-based counterparts. Our approach first develops a sensing range model from the statistical information on the emitted power of a type of events so that user-defined event-detection quality can be provisioned using a minimal network of static sensors. Then an accurate event location boundary estimation technique is developed from the sensing feedbacks, which also facilitates guided expansion of the area of possible event location (APEL) to deal with sensing errors. Finally, user-defined event-localization quality guarantee is provisioned cost-effectively by inviting mobile sensors on-demand to target positions. Analytical solutions are provided whenever appropriate and comprehensive simulations are carried out to evaluate localization performance. The proposed event localization technique outperforms the state-of-the-art range-based counterpart (Xu et al., 2011) in realistic environment with path loss, shadow fading, and sensor positioning errors.

A New Pluggable Framework for Centralized Routing in Wireless Sensor Network

This paper presents a novel energy aware centralized dynamic clustering routing framework for large-scale Wireless Sensor Network (WSN). The main advantage of the proposed method is pluggability of clustering algorithms in the framework. It uses some clustering algorithms that some of their usages are new in this field. The clustering algorithms are K-means, FCM, UPC, GA, IGA and FGKA that run at base station used to identify cluster of sensors. Six clustering algorithms are evaluated in the framework and results of them are compared in three models named unicast, multicast and broadcast.

Hybrid Re-Clustering Algorithm for Enhancement of Network Lifetime in Wireless Sensor Networks

In this paper, we propose a re-clustering framework for wireless sensor networks to undergo global re-clustering and local delegation in order to enhance the lifetime of the network. The variation in energy distribution across the CH network can be characterized by a mapping function using a metric, based on energy cost, defined on a metric space. The mapping characterizes the change in link cost in terms of energy distribution. Supremum of the change in link costs is defined as the distortion of the map. Distortion exceeding a given threshold is used to decide on re-clustering / local delegation. The global monitoring of energy distribution in the CH network afforded by the metric space and the distortion concept helps to provide a energy management framework for WSN which is proved to be effective. Simulation carried out in MAT LAB on a typical WSN showed favourable comparison in terms of average network life time with respect to established results.

A novel multiple-level trust management framework for wireless sensor networks

The distributed deployment nature of wireless sensor networks (WSNs) poses a challenge to the security of node cooperation in them as it is difficult for WSN to ensure that all nodes can recognise a huge number of other individual nodes and select appropriate and trustworthy nodes for cooperation. Node cooperation may therefore be launched in an unreliable environment and might be vulnerable to attacks. Consequently, the security of nodes is of paramount importance for the proper operation of WSNs. The distributed trust management scheme is a feasible solution. With a view to making improvement on the existing trust management mechanisms, we in this paper propose ML-TRUST, a multiple-level trust management framework for trust management in WSN in which three levels of trust are used to establish trustworthy relationships among nodes for their cooperation, namely, (1) a subjective trust, which is defined as belief and is proposed with respect to three aspects: past judgements, witness evidence, and capacity evaluation; (2) an objective trust, which is defined as reputation and is proposed with two factors, number of functioning communities and weighted judgements by rating nodes’ reputations, being introduced in reputation rating, and with several rules and fraud factor tests being given to prevent reputation rating from malicious attacks, and (3) the recommended trust method, which is proposed to obtain trustable impressions from strange recommendations with, in connection, several consistency factors being presented to determine the trustworthiness of a recommendation. Besides using a set of lemmas and theorems to back up our ML-TRUST framework, we also list the results of a series of simulation tests to further verify the performance of our mechanism.

Ensemble based traffic light control for city zones using a reduced number of sensors

Rapid advances in computing, sensing and telecommunication technology offer unprecedented opportunities for artificial intelligence concepts to expand their applications in the field of traffic management and control. Our methodology gravitates around a powerful decision-making method: ensemble-based systems. This technique is used to accurately classify the near future traffic conditions and to make efficient decisions for adapting the traffic lights sequences within an urban area to optimize the traffic flows. The proposed approach requires only measurements provided by traffic sensors located along the principal roads entering the zone. This reduced number of sensors are considered to be enough relevant for classifying the near future state of the traffic and moreover, their measurements can be validated through analytical/hardware redundancy. Our methodology is meant to be implemented within the framework of a wireless sensor and actuator network and is confirmed by computer simulation, including normal or abnormal traffic conditions, for the central part of the city of Timisoara-Romania.

Top-k query framework in wireless sensor networks for smart grid

The smart grid has caught great attentions in recent years, which is poised to transform a centralized, producer-controlled network to a decentralized, consumer-interactive network that's supported by fine-grained monitoring. Large-scale WSNs (Wireless Sensor Networks) have been considered one of the very promising technologies to support the implementation of smart grid. WSNs are applied in almost every aspect of smart grid, including power generation, power transmission, power distribution, power utilization and power dispatch, and the data query processing of `WSNs in power grid? become an hotspot issue due to the amount of data of power grid is very large and the requirement of response time is very high. To meet the demands, top-k query processing is a good choice, which performs the cooperative query by aggregating the database objects' degree of match for each different query predicate and returning the best k matching objects. In this paper, a framework that can effectively apply top-k query to wireless sensor network in smart grid is proposed, which is based on the cluster-topology sensor network. In the new method, local indices are used to optimize the necessary query routing and process intermediate results inside the cluster to cut down the data traffic, and the hierarchical join query is executed based on the local results. Besides, top-k query results are verified by the clean-up process, and two schemes are taken to deal with the problem of node's dynamicity, which further reduce communication cost. Case studies and experimental results show that our algorithm has outperformed the current existing one with higher quality results and better efficiently.

A Distributed Fault Detection System Based on IWSN for Machine Condition Monitoring

This paper introduces a novel framework for industrial wireless sensor networks (IWSNs) used for machine condition monitoring (MCM). Our approach enables the use of state-of-the-art computationally intensive classifiers in computationally weak sensor network nodes. The key idea is to split data acquisition, classifier building and training, and the operation phase, between different units. Computationally demanding processing is carried out in the central unit, while other tasks are distributed to the sensor nodes using over-the-air programming. The system is autonomously trained on the healthy state of a machine and then monitors a change in behavior which indicates a faulty state. Thanks to one-class classification, there is no need to introduce the faulty state of the machine in the training phase. We extend the diagnostic capability of the system using dynamic changes in the data acquisition and classification parts of the program in the sensor nodes. This enables the system to react to ambiguous machine states by temporarily changing the diagnostic focus. Compressing the information in the individual sensor nodes provided by in-node classification allows us to transmit only the classification result, instead of full signal waveforms. This enables the MCM system to be deployed with a large number of nodes, even with high sampling rates. The proposed concept was evaluated in IRIS IWSN by means of a rotary machine simulator.

Efficient Solutions Framework for Optimal Multitask Resource Assignments for Data Fusion in Wireless Sensor Networks

Motivated by the need to judiciously allocate scarce sensing resources to attain the highest benefit for the applications that sensor networks serve, in this article we develop a flexible solutions methodology for maximizing the overall reward attained, subject to constraints on the resource demands under fairly general reward or demand functions. We map a broad class of related problems for data fusion in wireless sensor networks into an integer programming problem and provide an iterative Lagrangian relaxation technique to solve it. Each iteration step involves solving for a maximum-weight independent set of an appropriately constructed graph, which, in many cases, can be obtained in polynomial time. We apply our methodology to the problem of tracking targets moving over a period of time through a nonhomogeneous, energy-constrained sensor field. With rewards represented by the quality of information attained in tracking, we study its trade-offs and relationship with energy consumption and periodic measurement taking. We finally illustrate other applications of our framework in sensor networks.