490 research outputs found

    An optimal clustering algorithm based distance-aware routing protocol for wireless sensor networks

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    Wireless Sensors Networks (WSN) consist of low power devices that are deployed at different geographical isolated areas to monitor physical event. Sensors are arranged in clusters. Each cluster assigns a specific and vital node which is known as a cluster head (CH). Each CH collects the useful information from its sensor member to be transmitted to a sink or Base Station (BS). Sensor have implemented with limited batteries (1.5V) that cannot have replaced. To resolve this issue and improve network stability, the proposed scheme adjust the transmission range between CHs and their members. The proposed approach is evaluated via simulation experiments and compared with some references existing algorithms. Our protocol seemed improved performance in terms of extended lifetime and achieved more than 35% improvements in terms of energy consumptio

    A new Itinerary planning approach among multiple mobile agents in wireless sensor networks (WSN) to reduce energy consumption

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    one of the important challenges in wireless sensors networks (WSN) resides in energy consumption. In order to resolve this limitation, several solutions were proposed. Recently, the exploitation of mobile agent technologies in wireless sensor networks to optimize energy consumption attracts researchers. Despite their advantage as an ambitious solution, the itineraries followed by migrating mobile agents can surcharge the network and so have an impact on energy consumption. Many researches have dealt with itinerary planning in WSNs through the use of a single agent (SIP: Single agent Itinerary Planning) or multiple mobile agents (MIP: Multiple agents Itinerary Planning). However, the use of multi-agents causes the emergence of the data load unbalancing problem among mobile agents, where the geographical distance is the unique factor motivating to plan the itinerary of the agents. The data balancing factor has an important role especially in Wireless sensor networks multimedia that owns a considerable volume of data size. It helps to optimize the tasks duration and thus optimizes the overall answer time of the network.  In this paper, we provide a new MIP solution (GIGM-MIP) which is based not only on geographic information but also on the amount of data provided by each node to reduce the energy consumption of the network. The simulation experiments show that our approach is more efficient than other approaches in terms of task duration and the amount of energy consumption

    Optimal energy balanced data gathering in wireless sensor networks

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    Unbalanced energy consumption is an inherent problem in wireless sensor networks where some nodes may be overused and die out early, resulting in a short network lifetime. In this paper, we investigate the problem of balancing energy consumption for data gathering sensor networks. Our key idea is to exploit the tradeoff between hop-by-hop transmission and direct transmission to balance energy dissipation among sensor nodes. By assigning each node a transmission probability which controls the ratio between hop-by-hop transmission and direct transmission, we formulate the energy consumption balancing problem as an optimal transmission probability allocation problem. We discuss this problem for both chain networks and general networks. Moreover, we present the solution to compute the optimal number of sections in terms of maximizing the network lifetime. Numerical results demonstrate that our methods outperform the traditional hop-by-hop and direct transmission schemes and achieve significant lifetime extension especially for dense sensor networks.Haibo Zhang, Hong Shen, Yasuo Ta

    USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

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    Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people

    Cloud-Assisted Device Clustering for Lifetime Prolongation in Wireless IoT Networks

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    One of the crucial challenges in the recently emerging Internet of Things (IoT) applications is how to handle the massive heterogeneous data generated from a large number of resource-constrained sensors. In this context, cloud computing has emerged as a promising paradigm due to its enormous storage and computing capabilities, thus leading to the IoT-Cloud convergence. In such a framework, IoT devices can be grouped into several clusters and each cluster head can send the aggregated information to the cloud via a gateway for further processing. Although a number of clustering methods have been proposed for the conventional Wireless Sensor Networks (WSNs), it is important to consider specific IoT characteristics while adapting these techniques for wireless IoT networks. One of the important features of IoT networks that can be exploited while developing clustering techniques is the collaborations among heterogeneous IoT devices. In this regard, the network-wide knowledge at the cloud center can be useful to provide information about the device relations to the IoT gateway. Motivated by this, we propose and evaluate a cloud-assisted device interaction-aware clustering scheme for heterogeneous IoT networks. The proposed method considers the joint impact of residual energy and device closeness factor for the effective selection of cluster heads. Our results show that the proposed clustering scheme can significantly prolong the network lifetime, and enhance the overall throughput of a wireless IoT network

    Distributed Transforms for Efficient Data Gathering in Sensor Networks

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    Devices, systems, and techniques for data collecting network such as wireless sensors are disclosed. A described technique includes detecting one or more remote nodes included in the wireless sensor network using a local power level that controls a radio range of the local node. The technique includes transmitting a local outdegree. The local outdegree can be based on a quantity of the one or more remote nodes. The technique includes receiving one or more remote outdegrees from the one or more remote nodes. The technique includes determining a local node type of the local node based on detecting a node type of the one or more remote nodes, using the one or more remote outdegrees, and using the local outdegree. The technique includes adjusting characteristics, including an energy usage characteristic and a data compression characteristic, of the wireless sensor network by selectively modifying the local power level and selectively changing the local node type

    Spectrum Map and its Application in Cognitive Radio Networks

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    Recent measurements on radio spectrum usage have revealed the abundance of underutilized bands of spectrum that belong to licensed users. This necessitated the paradigm shift from static to dynamic spectrum access. Cognitive radio based secondary networks that utilize such unused spectrum holes in the licensed band, have been proposed as a possible solution to the spectrum crisis. The idea is to detect times when a particular licensed band is unused and use it for transmission without causing interference to the licensed user. We argue that prior knowledge about occupancy of such bands and the corresponding achievable performance metrics can potentially help secondary networks to devise effective strategies to improve utilization. In this work, we use Shepard\u27s method of interpolation to create a spectrum map that provides a spatial distribution of spectrum usage over a region of interest. It is achieved by intelligently fusing the spectrum usage reports shared by the secondary nodes at various locations. The obtained spectrum map is a continuous and differentiable 2-dimension distribution function in space. With the spectrum usage distribution known, we show how different radio spectrum and network performance metrics like channel capacity, secondary network throughput, spectral efficiency, and bit error rate can be estimated. We show the applicability of the spectrum map in solving the intra-cell channel allocation problem in centralized cognitive radio networks, such as IEEE 802.22. We propose a channel allocation scheme where the base station allocates interference free channels to the consumer premise equipments (CPE) using the spectrum map that it creates by fusing the spectrum usage information shared by some CPEs. The most suitable CPEs for information sharing are chosen on a dynamic basis using an iterative clustering algorithm. Next, we present a contention based media access control (MAC) protocol for distributed cognitive radio network. The unlicensed secondary users contend among themselves over a common control channel. Winners of the contention get to access the available channels ensuring high utilization and minimum collision with primary incumbent. Last, we propose a multi-channel, multi-hop routing protocol with secondary transmission power control. The spectrum map, created and maintained by a set of sensors, acts as the basis of finding the best route for every source destination pair. The proposed routing protocol ensures primary receiver protection and maximizes achievable link capacity. Through simulation experiments we show the correctness of the prediction model and how it can be used by secondary networks for strategic positioning of secondary transmitter-receiver pairs and selecting the best candidate channels. The simulation model mimics realistic distribution of TV stations for urban and non-urban areas. Results validate the nature and accuracy of estimation, prediction of performance metrics, and efficiency of the allocation process in an IEEE 802.22 network. Results for the proposed MAC protocol show high channel utilization with primary quality of service degradation within a tolerable limit. Performance evaluation of the proposed routing scheme reveals that it ensures primary receiver protection through secondary power control and maximizes route capacity

    PFARS: Enhancing Throughput and Lifetime of Heterogeneous WSNs through Power-aware Fusion, Aggregation and Routing Scheme

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    Heterogeneous wireless sensor networks (WSNs) consist of resource-starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy-efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature , these issues are addressed individually and most of the proposed solutions are either application-specific or too complex that make their implementation unrealis-tic, specifically, in a resource-constrained environment. In this paper, we propose a novel node level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in-network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, i.e., the residual energy of neighboring nodes and their importance from a network's con-nectivity perspective. All our proposed algorithms were tested on a real-time dataset obtained through our deployed heterogeneous WSN in an orange orchard, and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in term of various performance metrics such as throughput, lifetime, data accuracy, computational time and delay

    On the Application of Zone Flooding in a Traffic Warning System

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    The LIWAS Traffic Warning System aims at providing early warning to vehicles about slippery conditions on the road. The LIWAS system is currently under development and consists of two main parts: sensors for measuring and classifying the state of the road, and a communication infrastructure for distributing road-state information to vehicles. This paper concentrates on the communication infrastructure, and considers the application of zone flooding for implementing the distribution of road-state information. Zone flooding combines flooding and geocasting to distribute road-state information in a geographically bounded area. To evaluate the applicability of zone flooding in the LIWAS system, a simulation model has been created using the Network Simulator 2. The simulation model captures a representative road-scenario and has been used to evaluate several flooding protocols when used to implement zone flooding. The primary evaluationcriteria are the load on the network and the capability to warn other vehicles in time
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