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.     

Algorithms for energy efficient mobile object tracking in wireless sensor networks

Wireless sensor networks have found more and more applications in a variety of pervasive computing environments, in their functions as data acquisition in pervasive applications. However, how to get better performance to support data acquisition of pervasive applications over WSNs remains to be a nontrivial and challenging task. The network lifetime and application requirement are two fundamental, yet conflicting, design objectives in wireless sensor networks for tracking mobile objects. The application requirement is often correlated to the delay time within which the application can send its sensing data back to the users in tracking networks. In this paper we study the network lifetime maximization problem and the delay time minimization problem together. To make both problems tractable, we have the assumption that each sensor node keeps working since it turns on. And we formulate the network lifetime maximization problem as maximizing the number of sensor nodes who don’t turn on, and the delay time minimization problem as minimizing the routing path length, after achieving the required tracking tasks. Since we prove the problems are NP-complete and APX-complete, we propose three heuristic algorithms to solve them. And we present several experiments to show the advantages and disadvantages referring to the network lifetime and the delay time among these three algorithms on three models, random graphs, grids and hypercubes. Furthermore, we implement the distributed version of these algorithms.     

On Explicit Congestion Notification for Stream Control Transmission Protocol in Lossy Networks

As a congestion avoidance mechanism, Explicit Congestion Notification (ECN) is designed to inform a data source to react to potential congestion early. Currently, the new transport protocol, Stream Control Transmission Protocol (SCTP), is not ECN-capable. An ECN-capable SCTP is proposed in this paper, which is bandwidth-efficient and robust to non-congestion losses. An SCTP source needs to adjust its congestion window when receiving ECN messages. We find the optimal value of the congestion window for an SCTP source in response to ECN messages, and develop a simple and practical method to achieve this optimal congestion window. Both simulation results and analysis are provided to support the effectiveness of the proposed ECN mechanism for SCTP. The simplified method in achieving the optimal congestion window is attractive because the total goodput performance of SCTP associations or the bottleneck link utilization is not sensitive to the window reduction policies when the network load is heavy. Using complicated methods to fine-tune SCTP or TCPs congestion window in response to congestion indications may not be worth the increase in complexity of the protocol.Prepared through collaborative participation in the Communications and Networks Consortium sponsored by the U.S. Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-2-0011. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon.Guanhua Ye received the B.E. degree in Information & Electronic Technology from Zhejiang University, China, in 1997 and M.E. degree in Communication & Information Systems from China Academy of Telecommunications Technology in 2000. He is currently working toward the Ph.D. degree in Electrical Engineering at City College and Graduate Center of City University of New York.His research interests are in computer networks, congestion control, ad hoc networks, voice over IP and multimedia communications.Tarek N. Saadawi received the B.Sc. and the M.Sc. from Cairo University Egypt and the Ph.D from the University of Maryland, College Park (all in Electrical Engineering). Since 1980 he has been with the Electrical Engineering Department, The City University of New York, City College. His current research interests are telecommunications network, high-speed networks, multimedia networks, AD-HOC networks and packet radio networks. He has published extensively in the area of telecommunications networks. He is a Co-author of the book, Fundamentals of Telecommunication Networks, John Wiley & Sons, 1994. He is also the lead author of Egypt Telecommunications Infrastructure Master Plan covering the fiber network, IP/ATM, DSL and the wireless local loop. Dr. Saadawi is a Former Chairman of IEEE Computer Society of New York City (1986–87). He has received IEEE Region 1 Award, 1987, and the Nippon Telegraph and Telephone (NTT) of America for research on Broadband Telecommunication Networks.Dr. Myung Jong Lee received the B.S from Seoul National University in Korea and M.S and Ph.D degrees in electrical engineering from Columbia University, 1986 and 1990 respectively. He joined the Department of Electrical Engineering, City College and Graduate Center of City University of New York, where he is currently an associate professor.His recent researches focus on various aspects of wireless ad hoc networks, sensor networks, and personal area networks. He has published over 50 refereed journal and conference papers. He is the Director of Samsung-CUNY Joint Laboratory on Sensor Networks. Dr. Lee received CUNYs Excellence Performance Award in 1999. Dr. Lee is a senior member of IEEE, and served many IEEE and other conferences as program committee member and session chair, and also actively participates in ZigBee Alliance and IEEE1451 Smart Sensor WG.     

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.     

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     

Multi-Parametric Clustering for Sensor Node Coordination in Cognitive Wireless Sensor Networks

The deployment of wireless sensor networks for healthcare applications have been motivated and driven by the increasing demand for real-time monitoring of patients in hospital and large disaster response environments. A major challenge in developing such sensor networks is the need for coordinating a large number of randomly deployed sensor nodes. In this study, we propose a multi-parametric clustering scheme designed to aid in the coordination of sensor nodes within cognitive wireless sensor networks. In the proposed scheme, sensor nodes are clustered together based on similar network behaviour across multiple network parameters, such as channel availability, interference characteristics, and topological characteristics, followed by mechanisms for forming, joining and switching clusters. Extensive performance evaluation is conducted to study the impact on important factors such as clustering overhead, cluster joining estimation error, interference probability, as well as probability of reclustering. Results show that the proposed clustering scheme can be an excellent candidate for use in large scale cognitive wireless sensor network deployments with high dynamics.     

Distributed sensor activation algorithm for target tracking with binary sensor networks

Target tracking with wireless sensor networks (WSNs) has been a hot research topic recently. Many works have been done to improve the algorithms for localization and prediction of a moving target with smart sensors. However, the results are frequently difficult to implement because of hardware limitations. In this paper, we propose a practical distributed sensor activation algorithm (DSA²) that enables reliable tracking with the simplest binary-detection sensors. In this algorithm, all sensors in the field are activated with a probability to detect targets or sleep to save energy, the schedule of which depends on their neighbor sensors’ behaviors. Extensive simulations are also shown to demonstrate the effectiveness of the proposed algorithm. Great improvement in terms of energy-quality tradeoff and excellent robustness of the algorithm are also emphasized in the simulations.     

A new traffic congestion prediction strategy (TCPS) based on edge computing

Real-time accurate traffic congestion prediction can enable Intelligent traffic management systems (ITMSs) that replace traditional systems to improve the efficiency of traffic and reduce traffic congestion. The ITMS consists of three main layers, which are: Internet of Things (IoT), edge, and cloud layers. Edge can collect real-time data from different routes through IoT devices such as wireless sensors, and then it can compute and store this collected data before transmitting them to the cloud for further processing. Thus, an edge is an intermediate layer between IoT and cloud layers that can receive the transmitted data through IoT to overcome cloud challenges such as high latency. In this paper, a novel real-time traffic congestion prediction strategy (TCPS) is proposed based on the collected data in the edge’s cache server at the edge layer. The proposed TCPS contains three stages, which are: (i) real-time congestion prediction (RCP) stage, (ii) congestion direction detection (CD²) stage, and (iii) width change decision (WCD) stage. The RCP aims to predict traffic congestion based on the causes of congestion in the hotspot using a fuzzy inference system. If there is congestion, the CD² stage is used to detect the congestion direction based on the predictions from the RCP by using the Optimal Weighted Naïve Bayes (OWNB) method. The WCD stage aims to prevent the congestion occurrence in which it is used to change the width of changeable routes (CR) after detecting the direction of congestion in CD². The experimental results have shown that the proposed TCPS outperforms other recent methodologies. TCPS provides the highest accuracy, precision, and recall. Besides, it provides the lowest error, with values equal to 95%, 74%, 75%, and 5% respectively.

     

Ant colony optimization for multicasting in static wireless ad-hoc networks

Nodes of wireless ad-hoc networks are generally equipped with batteries. This makes energy a scarce resource. Therefore, power consumption of network operations is critical and subject to optimization. One of the fundamental problems in ad-hoc networks is multicasting. In this work, we consider the so-called minimum energy multicast (MEM) problem in static ad-hoc networks. This problem can be stated as a combinatorial optimization problem. We develop an ant colony optimization algorithm for networks with omni-directional as well as directional antennas. The results show that our algorithm consistently outperforms existing techniques. This work was supported by grant TIN2007-66523 (FORMALISM) of the Spanish Government, and by the EU project FRONTS (FP7-ICT-2007-1) funded by the European Commission under the FET Proactive Initiative Pervasive Adaptation. In addition, Christian Blum acknowledges support from the Ramón y Cajal program of the Spanish Ministry of Science and Innovation, and Hugo Hernández acknowledges support from the Catalan Government through an FI grant.     

Multi objective-based incremental clustering by fast search technique for dynamically creating and updating clusters in large data

Cluster Computing - With the prevailing advancements in sensor technologies such as the Internet of Things (IoTs), cyber–physical-systems (CPSs), wireless sensor networks (WSNs), and many...     

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.     

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.

     

New ecological insights through the Global Lake Ecological Observatory Network (GLEON)

Sensor networks are playing an increasingly important role in ecology. Continual advances in affordable sensors and wireless communication are making the development of automated sensing systems with remote communication (i.e., sensor networks) affordable for many ecological research programs (Porter et al. 2005)[1].     

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.     

Healthcare Worker Contact Networks and the Prevention of Hospital-Acquired Infections

We present a comprehensive approach to using electronic medical records (EMR) for constructing contact networks of healthcare workers in a hospital. This approach is applied at the University of Iowa Hospitals and Clinics (UIHC) – a 3.2 million square foot facility with 700 beds and about 8,000 healthcare workers – by obtaining 19.8 million EMR data points, spread over more than 21 months. We use these data to construct 9,000 different healthcare worker contact networks, which serve as proxies for patterns of actual healthcare worker contacts. Unlike earlier approaches, our methods are based on large-scale data and do not make any a priori assumptions about edges (contacts) between healthcare workers, degree distributions of healthcare workers, their assignment to wards, etc. Preliminary validation using data gathered from a 10-day long deployment of a wireless sensor network in the Medical Intensive Care Unit suggests that EMR logins can serve as realistic proxies for hospital-wide healthcare worker movement and contact patterns. Despite spatial and job-related constraints on healthcare worker movement and interactions, analysis reveals a strong structural similarity between the healthcare worker contact networks we generate and social networks that arise in other (e.g., online) settings. Furthermore, our analysis shows that disease can spread much more rapidly within the constructed contact networks as compared to random networks of similar size and density. Using the generated contact networks, we evaluate several alternate vaccination policies and conclude that a simple policy that vaccinates the most mobile healthcare workers first, is robust and quite effective relative to a random vaccination policy.     

Techniques for Universal Evaluation of Astringency of Green Tea Infusion by the Use of a Taste Sensor System

A practical method for universal evaluation of the astringency of green tea infusion by a taste sensor system was established. The use of EGCg aqueous solution as a standard enabled analysis with high accuracy and reproducibility. The sensor output was converted into taste-intensity on the basis of Weber’s and Weber-Fechner laws, which was named the “EIT _ast ” value (“EIT” and “ast” are abbreviations for “Estimated Intensity of Taste” and “astringency” respectively). It was clarified that green tea infusion is to be classified into eight grades on the EIT _ast scale. Furthermore, the high correlation of the EIT _ast value with the human gustatory sense and the high stability of the taste sensor were proved.     

Marine hydrocarbonoclastic bacteria as whole‐cell biosensors for n‐alkanes

Whole‐cell biosensors offer potentially useful, cost‐effective systems for the in‐situ monitoring of seawater for hydrocarbons derived from accidental spills. The present work compares the performance of a biosensor system for the detection of alkanes in seawater, hosted in either Escherichia coli (commonly employed in whole‐cell biosensors but not optimized for alkane assimilation) or different marine bacteria specialized in assimilating alkanes. The sensor system was based on the Pseudomonas putida AlkS regulatory protein and the PalkB promoter fused to a gene encoding the green fluorescent protein. While the E. coli sensor provided the fastest response to pure alkanes (25‐fold induction after 2 h under the conditions used), a sensor based on Alcanivorax borkumensis was slower, requiring 3–4 h to reach similar induction values. However, the A. borkumensis sensor showed a fourfold lower detection threshold for octane (0.5 μM), and was also better at sensing the alkanes present in petrol. At petrol concentrations of 0.0125%, the A. borkumensis sensor rendered a sevenfold induction, while E. coli sensor showed no response. We discuss possible explanations to this behaviour in terms of the cellular adaptations to alkane uptake and the basal fluorescence produced by each bacterial strain, which was lowest for A. borkumensis.     

Software sensors for fermentation processes

Four software sensors based on standard on-line data from fermentation processes and simple mathematical models were used to monitor a number of state variables in Escherichia coli fed-batch processes: the biomass concentration, the specific growth rate, the oxygen transfer capacity of the bioreactor, and the new R _O/S sensor which is the ratio between oxygen and energy substrate consumption. The R _O/S variable grows continuously in a fed-batch culture with constant glucose feed, which reflects the increasing maintenance demand at declining specific growth rate. The R _O/S sensor also responded to rapid pH shift-downs reflecting the increasing demand for maintenance energy. It is suggested that this sensor may be used to monitor the extent of physiological stress that demands energy for survival.