A self-healing clustering algorithm for underwater sensor networks

Underwater wireless sensor networks (UWSNs) is a novel networking paradigm to explore aqueous environments. The characteristics of mobile UWSNs, such as low communication bandwidth, large propagation delay, floating node mobility, and high error probability, are significantly different from terrestrial wireless sensor networks. Energy-efficient communication protocols are thus urgently demanded in mobile UWSNs. In this paper, we develop a novel clustering algorithm that combines the ideas of energy-efficient cluster-based routing and application-specific data aggregation to achieve good performance in terms of system lifetime, and application-perceived quality. The proposed clustering technique organizes sensor nodes into direction-sensitive clusters, with one node acting as the head of each cluster, in order to fit the unique characteristic of up/down transmission direction in UWSNs. Meanwhile, the concept of self-healing is adopted to avoid excessively frequent re-clustering owing to the disruption of individual clusters. The self-healing mechanism significantly enhances the robustness of clustered UWSNs. The experimental results verify the effectiveness and feasibility of the proposed algorithm.     

Traffic-aware routing protocol for wireless sensor networks

In wireless sensor networks, when a sensor node detects events in the surrounding environment, the sensing period for learning detailed information is likely to be short. However, the short sensing cycle increases the data traffic of the sensor nodes in a routing path. Since the high traffic load causes a data queue overflow in the sensor nodes, important information about urgent events could be lost. In addition, since the battery energy of the sensor nodes is quickly exhausted, the entire lifetime of wireless sensor networks would be shortened. In this paper, to address these problem issues, a new routing protocol is proposed based on a lightweight genetic algorithm. In the proposed method, the sensor nodes are aware of the data traffic rate to monitor the network congestion. In addition, the fitness function is designed from both the average and the standard deviation of the traffic rates of sensor nodes. Based on dominant gene sets in a genetic algorithm, the proposed method selects suitable data forwarding sensor nodes to avoid heavy traffic congestion. In experiments, the proposed method demonstrates efficient data transmission due to much less queue overflow and supports fair data transmission for all sensor nodes. From the results, it is evident that the proposed method not only enhances the reliability of data transmission but also distributes the energy consumption across wireless sensor networks.     

Self-adapting maximum flow routing for autonomous wireless sensor networks

Autonomous wireless sensor networks are subject to power, bandwidth, and resource limitations that can be represented as capacity constraints imposed to their equivalent flow networks. The maximum sustainable workload (i.e., the maximum data flow from the sensor nodes to the collection point which is compatible with the capacity constraints) is the maxflow of the flow network. Although a large number of energy-aware routing algorithms for ad-hoc networks have been proposed, they usually aim at maximizing the lifetime of the network rather than the steady-state sustainability of the workload. Energy harvesting techniques, providing renewable supply to sensor nodes, prompt for a paradigm shift from energy-constrained lifetime optimization to power-constrained workload optimization.     

An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network

Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish–Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection.     

Ecoinformatics using wireless sensor networks: An overview

Wireless sensor networks have the potential to become significant subsystems of ecological experiment. Sensor networks consist of large number of tiny sensor nodes, all of which have sensing capabilities. These networks allow coordinated signal detection, monitoring, and tracking to enable sensor nodes to simultaneously capture geographically distinct measurements. Sensor nodes do not require predetermined positioning making such networks especially useful for applications in remote, inhospitable environments. In this paper we have tried to see the various ecological experimental scenarios, and how wireless sensor networks can be used in that field. One of the most challenging bottlenecks in the usage of wireless sensor networks in large scale experiments is the energy constraint. Various routing protocols which have tried to optimize the energy usage are also studied in the paper.     

On an improved clustering algorithm based on node density for WSN routing protocol

To better collect data in context to balance energy consumption, wireless sensor networks (WSN) need to be divided into clusters. The division of clusters makes the network become a hierarchical organizational structure, which plays the role of balancing the network load and prolonging the life cycle of the system. In clustering routing algorithm, the pros and cons of clustering algorithm directly affect the result of cluster division. In this paper, an algorithm for selecting cluster heads based on node distribution density and allocating remaining nodes is proposed for the defects of cluster head random election and uneven clustering in the traditional LEACH protocol clustering algorithm in WSN. Experiments show that the algorithm can realize the rapid selection of cluster heads and division of clusters, which is effective for node clustering and is conducive to equalizing energy consumption.

     

Optimal Transmission Radius for Flooding in Large Scale Sensor Networks

One of the principal characteristics of large scale wireless sensor networks is their distributed, multi-hop nature. Due to this characteristic, applications such as query propagation rely regularly on network-wide flooding for information dissemination. If the transmission radius is not set optimally, the flooded packet may be holding the transmission medium for longer periods than are necessary, reducing overall network throughput. We analyze the impact of the transmission radius on the average settling time—the time at which all nodes in the network finish transmitting the flooded packet. Our analytical model takes into account the behavior of the underlying contention-based MAC protocol, as well as edge effects and the size of the network. We show that for large wireless networks there exists an intermediate transmission radius which minimizes the settling time, corresponding to an optimal tradeoff between reception and contention times. We also explain how physical propagation models affect small wireless networks and why there is no intermediate optimal transmission radius observed in these cases. The mathematical analysis is supported and validated through extensive simulations.Marco Zuniga is currently a PhD student in the Department of Electrical Engineering at the University of Southern California. He received his Bachelors degree in Electrical Engineering from the Pontificia Universidad Catolica del Peru in 1998, and his Masters degree in Electrical Engineering from the University of Southern California in 2002. His interests are in the area of Wireless Sensor Networks in general, and more specifically in studying the interaction amongst different layers to improve the performance of these networks. He is a member of IEEE and the Phi Kappa Phi Honor society.Bhaskar Krishnamachari is an Assistant Professor in the Department of Electrical Engineering at the University of Southern California (USC), where he also holds a joint appointment in the Department of Computer Science. He received his Bachelors degree in Electrical Engineering with a four-year full-tuition scholarship from The Cooper Union for the Advancement of Science and Art in 1998. He received his Masters degree and his Ph.D. in Electrical Engineering from Cornell University in 1999 and 2002, under a four-year university graduate fellowship. Dr. Krishnamacharis previous research has included work on critical density thresholds in wireless networks, data centric routing in sensor networks, mobility management in cellular telephone systems, multicast flow control, heuristic global optimization, and constraint satisfaction. His current research is focused on the discovery of fundamental principles and the analysis and design of protocols for next generation wireless sensor networks. He is a member of IEEE, ACM and the Tau Beta Pi and Eta Kappa Nu Engineering Honor Societies     

A Service Approach for Architecting Application Independent Wireless Sensor Networks

The current sensor networks are assumed to be designed for specific applications, having data communication protocols strongly coupled to applications. The future sensor networks are envisioned as comprising heterogeneous devices assisting to a large range of applications. To achieve this goal, a new architecture approach is needed, having application specific features separated from the data communication protocol, while influencing its behavior. We propose a Web Services approach for the design of sensor network, in which sensor nodes are service providers and applications are clients of such services. Our main goal is to enable a flexible architecture in which sensor networks data can be accessed by users spread all over the world.     

Distance estimation by mining characteristics in anisotropic sensor networks

Localization is useful for many position-dependent applications in wireless sensor networks, where distance estimation from sensor nodes to beacon nodes plays a fundamental role. Most current ranging methods rely on an assumption that deployed WSNs are isotropic. Hence, adjustments on measured distances are the same in all directions. Unfortunately, this assumption does not hold in practice. Present methods introduce such great ranging errors that they are not feasible for real applications. In order to obtain better distance estimation in anisotropic WSNs, we propose a new metric, Dominating Degree, to describe the local deployment characteristics of sensor nodes, and to identify turning nodes along paths. We further propose a method to scale deployment irregularities of WSNs as global characteristics. Finally, appropriate adjustments to distance measurements are performed by synthesizing both local and global characteristics. Simulation results show that the proposed method outperforms PDM and DV-distance especially when beacon nodes are not deployed uniformly.     

Using Trust to Secure Geographic and Energy Aware Routing against Multiple Attacks

To address the vulnerability of geographic routing to multiple security threats such as false routing information, selective forwarding and the Sybil attack in wireless sensor networks, this paper proposes a trust-based defending model against above-mentioned multiple attacks. Considering the characteristics of resource-constrained sensor nodes, trust values of neighboring nodes on the routing path can be calculated through the Dirichlet distribution function, which is based on data packets'' acknowledgements in a certain period instead of energy-consuming monitoring. Trust is combined with the cost of geographic and energy aware routing for selecting the next hop of routing. At the same time, the initial trust is dynamically determined, service requests are restricted for malicious nodes in accordance with trust values, and the impact of node mobility is weakened by the trust evolution. The simulation results and analysis show that the proposed model under multiple attacks has advantages in packet delivery ratio and network lifetime over the existing models.     

Localization algorithm in wireless sensor networks based on semi-supervised manifold learning and its application

Localization of mobile nodes in wireless sensor network gets more and more important, because many applications need to locate the source of incoming measurements as precise as possible. Many previous approaches to the location-estimation problem need know the theories and experiential signal propagation model and collect a large number of labeled samples. So, these approaches are coarse localization because of the inaccurate model, and to obtain such data requires great effort. In this paper, a semi-supervised manifold learning is used to estimate the locations of mobile nodes in a wireless sensor network. The algorithm is used to compute a subspace mapping function between the signal space and the physical space by using a small amount of labeled data and a large amount of unlabeled data. This mapping function can be used online to determine the location of mobile nodes in a sensor network based on the signals received. We use independent development nodes to setup the network in metallurgical industry environment, outdoor and indoor. Experimental results show that we can achieve a higher accuracy with much less calibration effort as compared with RADAR localization systems.     

Lifetime extension based on residual energy for receiver-driven multi-hop wireless network

An important research topic in wireless sensor networking is the extension of operating time by controlling the power consumption of individual nodes. In a receiver-driven communication protocol, a receiver node periodically transmits its ID to the sender node, and in response the sender node sends an acknowledgment, after which data transmission starts. By applying such a receiver-driven protocol to wireless sensor networks, the average power consumption of the network can be controlled, but there still remains the problem of unbalanced load distribution among nodes. Therefore, part of the network shuts down when the battery of the node that consumes the most power is completely discharged. To extend the network lifetime, we propose a method where information about the residual energy level is exchanged through ID packets in order to balance power consumption. Simulation results show that the network lifetime can be extended by about 70–100 % while maintaining high network performance in terms of packet collection ratio and delay.     

Bayes Node Energy Polynomial Distribution to Improve Routing in Wireless Sensor Network

Wireless Sensor Network monitor and control the physical world via large number of small, low-priced sensor nodes. Existing method on Wireless Sensor Network (WSN) presented sensed data communication through continuous data collection resulting in higher delay and energy consumption. To conquer the routing issue and reduce energy drain rate, Bayes Node Energy and Polynomial Distribution (BNEPD) technique is introduced with energy aware routing in the wireless sensor network. The Bayes Node Energy Distribution initially distributes the sensor nodes that detect an object of similar event (i.e., temperature, pressure, flow) into specific regions with the application of Bayes rule. The object detection of similar events is accomplished based on the bayes probabilities and is sent to the sink node resulting in minimizing the energy consumption. Next, the Polynomial Regression Function is applied to the target object of similar events considered for different sensors are combined. They are based on the minimum and maximum value of object events and are transferred to the sink node. Finally, the Poly Distribute algorithm effectively distributes the sensor nodes. The energy efficient routing path for each sensor nodes are created by data aggregation at the sink based on polynomial regression function which reduces the energy drain rate with minimum communication overhead. Experimental performance is evaluated using Dodgers Loop Sensor Data Set from UCI repository. Simulation results show that the proposed distribution algorithm significantly reduce the node energy drain rate and ensure fairness among different users reducing the communication overhead.     

Energy and QoS Aware Routing in Wireless Sensor Networks

Many new routing protocols have been proposed for wireless sensor networks in recent years. Almost all of the routing protocols considered energy efficiency as the ultimate objective since energy is a very scarce resource for sensor nodes. However, the introduction imaging sensors has posed additional challenges. Transmission of imaging data requires both energy and QoS aware routing in order to ensure efficient usage of the sensors and effective access to the gathered measurements. In this paper, we propose an energy-aware QoS routing protocol for sensor networks which can also run efficiently with best-effort traffic. The protocol finds a least-cost, delay-constrained path for real-time data in terms of link cost that captures nodes energy reserve, transmission energy, error rate and other communication parameters. Moreover, the throughput for non-real-time data is maximized by adjusting the service rate for both real-time and non-real-time data at the sensor nodes. Such adjustment of service rate is done by using two different mechanisms. Simulation results have demonstrated the effectiveness of our approach for different metrics with respect to the baseline approach where same link cost function is used without any service differentiation mechanism.Kemal Akkaya received his B.S. degree in Computer Science from Bilkent University, Ankara, Turkey in 1997 and MS degree in Computer Science from Ortadogu Technical University (ODTU), Ankara, Turkey in 1999. He worked as a software developer at an automation project of Siemens and World Bank in Ankara, Turkey in 2000. He is currently a Ph.D. candidate at University of Maryland, Baltimore County (UMBC), Baltimore, MD. His research interests include energy aware routing, security and quality of service issues in ad hoc wireless networks.Mohamed F. Younis received B.S. degree in computer science and M.S. in engineering mathematics from Alexandria University in Egypt in 1987 and 1992, respectively. In 1996, he received his Ph.D. in computer science from New Jersey Institute of Technology. He is currently an assistant professor in the department of computer science and electrical engineering at the university of Maryland Baltimore County (UMBC). Before joining UMBC, he was with the Advanced Systems Technology Group, an Aerospace Electronic Systems R&D organization of Honeywell International Inc. While at Honeywell he led multiple projects for building integrated fault tolerant avionics, in which a novel architecture and an operating system were developed. This new technology has been incorporated by Honeywell in multiple products and has received worldwide recognition by both the research and the engineering communities. He also participated in the development the Redundancy Management System, which is a key component of the Vehicle and Mission Computer for NASAs X-33 space launch vehicle. Dr. Younis technical interest includes network architectures and protocols, embedded systems, fault tolerant computing and distributed real-time systems. Dr. Younis has four granted and three pending patents. He served on multiple technical committees and published over 40 technical papers in refereed conferences and journals.     

Using ant-based agents for congestion control in ad-hoc wireless sensor networks

Ad-hoc wireless sensor networks suffer from problems of congestion, which lead to packet loss and excessive energy consumption. In this paper, we address the issue of congestion in these networks. We propose a new routing protocol for wireless sensor networks namely Ant-based Routing with Congestion Control (ARCC), which takes into account the congestion of the network at a given instant and proposes to reduce it and then finds the optimum paths between the source and the sink nodes. Simulation results show that ARCC performs better with respect to the throughput, the number of packets lost and the priority performance.     

Sensor scheduling for <Emphasis Type="Italic">p</Emphasis>-percent coverage in wireless sensor networks

We study sensor scheduling problems of p-percent coverage in this paper and propose two scheduling algorithms to prolong network lifetime due to the fact that for some applications full coverage is not necessary and different subareas of the monitored area may have different coverage requirements. Centralized p-Percent Coverage Algorithm (CPCA) we proposed is a centralized algorithm which selects the least number of nodes to monitor p-percent of the monitored area. Distributed p-Percent Coverage Protocol (DPCP) we represented is a distributed algorithm which can determine a set of nodes in a distributed manner to cover p-percent of the monitored area. Both of the algorithms can guarantee network connectivity. The simulation results show that our algorithms can remarkably prolong network lifetime, have less than 5% un-required coverage for large networks, and employ nodes fairly for most cases.     

Investigations into the relationship between feedback loops and functional importance of a signal transduction network based on Boolean network modeling

Background  

A number of studies on biological networks have been carried out to unravel the topological characteristics that can explain the functional importance of network nodes. For instance, connectivity, clustering coefficient, and shortest path length were previously proposed for this purpose. However, there is still a pressing need to investigate another topological measure that can better describe the functional importance of network nodes. In this respect, we considered a feedback loop which is ubiquitously found in various biological networks.     

Interdependent Multi-Layer Networks: Modeling and Survivability Analysis with Applications to Space-Based Networks

This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs) allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other''s Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats) of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also examined, and the results highlight the importance of the reliability of the wireless links between spacecraft (nodes) to enable any survivability improvements for space-based networks.     

Scrutiny of unruly and abuse in wireless networks to mitigate physical layer threats using discriminate based misbehavior prevention

The open nature of communication medium in wireless networks becomes easy for the attackers to indulge in jamming attack. Jamming blocks the communication channel with the intent of preventing the flow of useful information. Jammers effectively and stealthy corrupts the packet by injecting high level of noise thereby keeping the channel busy so that the legitimate traffic gets completely blocked, resulting in packet loss at the receiver side. Securing the information from the jammers has become very important. Therefore an effective approach is needed to prevent this attack and this paper proposes Discriminate based Misbehavior Prevention DMP scheme to identify and to detach the jammer that corrupts the packet in wireless network. The proposed scheme consists of three modules. In module one, the log files are analyzed and using the trusting mechanism the suspected traces are identified. In module two, three reshuffling algorithms has been developed for reshuffling and identifying doubtful traces. In module three, the jammer nodes are identified and detached from the network. By simulation studies, it is observed that the proposed scheme attains higher throughput and packet delivery ratio while attaining lower delay.     

Distributed Gaussian mixture model-based particle filter method for chemical pollution source localization with sensor network

Chemical pollution source localization with statistical estimation algorithm in sensor networks, which was also known as source parameters estimation, has an important significance in fields such as pollution environmental monitoring and control. In this paper, a distributed Gaussian mixture dispersion model based particle filter method was proposed for the chemical pollution source localization problem. At the same time, we designed a composite information objective function for sensor scheduling scheme, which comprised of information utility measurement and energy consumption measurement. At last, in order to balance the source localization accuracy and energy consumption, a dynamical sensor radius adjusting method was given for sensor nodes scheduling. Simulation and experiment results show that the proposed method could determine the position of chemical pollution source, compared to UKF, the distributed Gaussian mixture particle filter method was suggested because it could get a significant reduction in the required numbers of sensor nodes and less energy to achieve the desired performance with less time.