767 research outputs found

    Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

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    Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

    Modeling and Implementation of Wireless Sensor Networks for Logistics Applications

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    Logistics has experienced a long time of developments and improvements based on the advanced vehicle technologies, transportation systems, traffic network extension and logistics processes. In the last decades, the complexity has increased significantly and this has created complex logistics networks over multiple continents. Because of the close cooperation, these logistics networks are highly dependent on each other in sharing and processing the logistics information. Every customer has many suppliers and vice versa. The conventional centralized control continues but reaches some limitations such as the different distribution of suppliers, the complexity and flexibility of processing orders or the dynamics of the logistic objects. In order to overcome these disadvantages, the paradigm of autonomous logistics is proposed and promises a better technical solution for current logistics systems. In autonomous logistics, the decision making is shifted toward the logistic objects which are defined as material items (e.g., vehicles, containers) or immaterial items (e.g., customer orders) of a networked logistics system. These objects have the ability to interact with each other and make decisions according to their own objectives. In the technical aspect, with the rapid development of innovative sensor technology, namely Wireless Sensor Networks (WSNs), each element in the network can self-organize and interact with other elements for information transmission. The attachment of an electronic sensor element into a logistic object will create an autonomous environment in both the communication and the logistic domain. With this idea, the requirements of logistics can be fulfilled; for example, the monitoring data can be precise, comprehensive and timely. In addition, the goods flow management can be transferred to the information logistic object management, which is easier by the help of information technologies. However, in order to transmit information between these logistic objects, one requirement is that a routing protocol is necessary. The Opportunistic relative Distance-Enabled Uni-cast Routing (ODEUR ) protocol which is proposed and investigated in this thesis shows that it can be used in autonomous environments like autonomous logistics. Moreover, the support of mobility, multiple sinks and auto-connection in this protocol enhances the dynamics of logistic objects. With a general model which covers a range from low-level issues to high-level protocols, many services such as real time monitoring of environmental conditions, context-aware applications and localization make the logistic objects (embedded with sensor equipment) more advanced in information communication and data processing. The distributed management service in each sensor node allows the flexible configuration of logistic items at any time during the transportation. All of these integrated features introduce a new technical solution for smart logistic items and intelligent transportation systems. In parallel, a management system, WSN data Collection and Management System (WiSeCoMaSys), is designed to interact with the deployed Wireless Sensor Networks. This tool allows the user to easily manipulate the sensor networks remotely. With its rich set of features such as real time data monitoring, data analysis and visualization, per-node management, and alerts, this tool helps both developers and users in the design and deployment of a sensor network. In addition, an analytical model is developed for comparison with the results from simulations and experiments. Focusing on the use of probability theory to model the network links, this model considers several important factors such as packet reception rate and network traffic which are used in the simulation and experiment parts. Moreover, the comparison between simulation, experiment and analytical results is also carried out to estimate the accuracy of the design and make several improvements of the simulation accuracy. Finally, all of the above parts are integrated in one unique system. This system is verified by both simulations in logistic scenarios (e.g., harbors, warehouses and containers) and experiments. The results show that the proposed model and protocol have a good packet delivery rate, little memory requirements and low delay. Accordingly, this system design is practical and applicable in logistics

    An Implementation of Grouping Nodes in Wireless Sensor Network Based on Distance by Using K-Means Clustering

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    Wireless Sensor Network (WSN) is a network consisting of several sensor nodes that communicate with each other and work together to collect data from the surrounding environment. One of the WSN problems is the limited available power. Therefore, nodes on WSN need to communicate by using a cluster-based routing protocol. To solve this, the researchers propose a node grouping based on distance by using k-means clustering with a hardware implementation. Cluster formation and member node selection are performed based on the nearest device of the sensor node to the cluster head. The k-means algorithm utilizes Euclidean distance as the main grouping nodes parameter obtained from the conversion of the Received Signal Strength Indication (RSSI) into the distance estimation between nodes. RSSI as the parameter of nearest neighbor nodes uses lognormal shadowing channel modeling method that can be used to get the path loss exponent in an observation area. The estimated distance in the observation area has 27.9% error. The average time required for grouping is 58.54 s. Meanwhile, the average time used to retrieve coordinate data on each cluster to the database is 45.54 s. In the system, the most time-consuming process is the PAN ID change process with an average time of 14.20 s for each change of PAN ID. The grouping nodes in WSN using k-means clustering algorithm can improve the power efficiency by 6.5%

    Localization method for low power consumption systems

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    Locating nodes is a fundamental problem in wireless networks with hundreds of devices deployed in a wide area. This is especially relevant for mobile nodes. Wireless sensor nodes are usually powered by small batteries, solar panels or piezoelectric generators, so that, and consequently, power consumption is the main constraint to deal with. But classic localization techniques do not consider the problem of energy consumption as a key point. This paper presents a novel low power and range-free localization technique based on distributed fuzzy logic and cooperative processing among a set of fixed nodes and its neighbours. This feature permits better accuracy with less power consumption than most relevant localization techniquesJunta de Andalucía P07-TIC-0247

    A decentralized framework for multi-agent robotic systems

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    Over the past few years, decentralization of multi-agent robotic systems has become an important research area. These systems do not depend on a central control unit, which enables the control and assignment of distributed, asynchronous and robust tasks. However, in some cases, the network communication process between robotic agents is overlooked, and this creates a dependency for each agent to maintain a permanent link with nearby units to be able to fulfill its goals. This article describes a communication framework, where each agent in the system can leave the network or accept new connections, sending its information based on the transfer history of all nodes in the network. To this end, each agent needs to comply with four processes to participate in the system, plus a fifth process for data transfer to the nearest nodes that is based on Received Signal Strength Indicator (RSSI) and data history. To validate this framework, we use differential robotic agents and a monitoring agent to generate a topological map of an environment with the presence of obstacles

    Practical and Robust Power Management for Wireless Sensor Networks

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    Wireless Sensor Networks: WSNs) consist of tens or hundreds of small, inexpensive computers equipped with sensors and wireless communication capabilities. Because WSNs can be deployed without fixed infrastructure, they promise to enable sensing applications in environments where installing such infrastructure is not feasible. However, the lack of fixed infrastructure also presents a key challenge for application developers: sensor nodes must often operate for months or years at a time from fixed or limited energy sources. The focus of this dissertation is on reusable power management techniques designed to facilitate sensor network developers in achieving their systems\u27 required lifetimes. Broadly speaking, power management techniques fall into two categories. Many power management protocols developed within the WSN community target specific hardware subsystems in isolation, such as sensor or radio hardware. The first part of this dissertation describes the Adaptive and Robust Topology control protocol: ART), a representative hardware-specific technique for conserving energy used by packet transmissions. In addition to these single-subsystem approaches, many applications can benefit greatly from holistic power management techniques that jointly consider the sensing, computation, and communication costs of potential application configurations. The second part of this dissertation extends this holistic power management approach to two families of structural health monitoring applications. By applying a partially-decentralized architecture, the cost of collecting vibration data for analysis at a centralized base station is greatly reduced. Finally, the last part of this dissertation discusses work toward a system for clinical early warning and intervention. The feasibility of this approach is demonstrated through preliminary study of an early warning component based on historical clinical data. An ongoing clinical trial of a real-time monitoring component also provides important guidelines for future clinical deployments based on WSNs

    Localization method for low-power wireless sensor networks

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    Context awareness is an important issue in ambient intelligence to anticipate the desire of the user and, in consequence, to adapt the system. In context awareness, localization is very important to enable a responsive environment for the users. Focusing on this issue, this paper presents a localization system based on the use of Wireless Sensor Networks devices. In contrast to a traditional RFID, these devices offer the possibility of a collaborative sensing and processing of environmental information. The proposed system is a range-free localization algorithm that uses fuzzy inference to process the RSSI measurement and to estimate the position of mobile devices. The main goal of the algorithm is to reduce the power consumption and the cost of the devices, especially for the mobiles ones, maintaining the accuracy of the inferred position

    Energy-efficient vertical handover parameters, classification and solutions over wireless heterogeneous networks: a comprehensive survey

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    In the last few decades, the popularity of wireless networks has been growing dramatically for both home and business networking. Nowadays, smart mobile devices equipped with various wireless networking interfaces are used to access the Internet, communicate, socialize and handle short or long-term businesses. As these devices rely on their limited batteries, energy-efficiency has become one of the major issues in both academia and industry. Due to terminal mobility, the variety of radio access technologies and the necessity of connecting to the Internet anytime and anywhere, energy-efficient handover process within the wireless heterogeneous networks has sparked remarkable attention in recent years. In this context, this paper first addresses the impact of specific information (local, network-assisted, QoS-related, user preferences, etc.) received remotely or locally on the energy efficiency as well as the impact of vertical handover phases, and methods. It presents energy-centric state-of-the-art vertical handover approaches and their impact on energy efficiency. The paper also discusses the recommendations on possible energy gains at different stages of the vertical handover process
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