Radio link quality estimation in Wireless Sensor Networks (WSNs) has a fundamental impact on the network performance and also affects the design of higher-layer protocols. Therefore, for about a decade, it has been attracting a vast array of research works. Reported works on link quality estimation are typically based on different assumptions, consider different scenarios, and provide radically different (and sometimes contradictory) results. This article provides a comprehensive survey on related literature, covering the characteristics of low-power links, the fundamental concepts of link quality estimation in WSNs, a taxonomy of existing link quality estimators, and their performance analysis. To the best of our knowledge, this is the first survey tackling in detail link quality estimation in WSNs. We believe our efforts will serve as a reference to orient researchers and system designers in this area.

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Radio link quality estimation in wireless sensor networks: A survey

A sensor in wireless sensor networks (WSNs) periodically produces data as it monitors its vicinity. The basic operation in such a network is the systematic gathering (with or without in-network aggregation) and transmitting of sensed data to a base station for further processing. A key challenging question in WSNs is to schedule nodes' activities to reduce energy consumption. In this paper, we focus on designing energy-efficient protocols for low-data-rate WSNs, where sensors consume different energy in different radio states (transmitting, receiving, listening, sleeping, and being idle) and also consume energy for state transition. We use TDMA as the MAC layer protocol and schedule the sensor nodes with consecutive time slots at different radio states while reducing the number of state transitions. We prove that the energy consumption by our scheduling for homogeneous network is at most twice of the optimum and the timespan of our scheduling is at most a constant times of the optimum. The energy consumption by our scheduling for heterogeneous network is at most ? (log Rmax/Rmin) times of the optimum. We also propose effective algorithms to construct data gathering tree such that the energy consumption and the network throughput is within a constant factor of the optimum. Extensive simulation studies show that our algorithms do considerably reduce energy consumption.

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Energy-Efficient Wake-Up Scheduling for Data Collection and Aggregation

The problem of data sampling and collection in wireless sensor networks (WSNs) is becoming critical as larger networks are being deployed. Increasing network size poses significant data collection challenges, for what concerns sampling and transmission coordination as well as network lifetime. To tackle these problems, in-network compression techniques without centralized coordination are becoming important solutions to extend lifetime. In this paper, we consider a scenario in which a large WSN, based on ZigBee protocol, is used for monitoring (e.g., building, industry, etc.). We propose a new algorithm for in-network compression aiming at longer network lifetime. Our approach is fully distributed: each node autonomously takes a decision about the compression and forwarding scheme to minimize the number of packets to transmit. Performance is investigated with respect to network size using datasets gathered by a real-life deployment. An enhanced version of the algorithm is also introduced to take into account the energy spent in compression. Experiments demonstrate that the approach helps finding an optimal tradeoff between the energy spent in transmission and data compression.

Distributed Compressive Sampling for Lifetime Optimization in Dense Wireless Sensor Networks

Compressive sensing (CS) is a new approach to simultaneous sensing and compressing that is highly promising for fully distributed compression in wireless sensor networks (WSNs). While a wide investigation has been performed about theory and practice of CS for individual signals, real and practical cases, in general, involve multiple signals, extending the problem of compression from 1-D single-sensor to 2-D multiple-sensors data. In this paper the two most prominent frameworks on sparsity and compressibility of multidimensional signals and signal ensembles, Distributed compressed sensing (DCS) and Kronecker compressive sensing (KCS), are investigated. In this paper we compare these two frameworks against a common set of artificial signals properly built to embody the main characteristics of natural signals. We further investigate how, in a real deployment, DCS can be used to reduce the power consumption and to prolong lifetime. In particular an extensive analysis is performed using real commercial off-the-shelf (COTS) hardware evaluating how different kind of compression matrices can affect the jointly reconstruction, trying to achieve the better tradeoff between quality and energy expenditure.

Compressive Sensing Optimization for Signal Ensembles in WSNs

This paper describes a system design approach for wireless sensor network based wildfire monitoring, including the system architecture, hardware and software framework. Effectiveness of the solution is evaluated in terms of reactivity, reliability, robustness and network lifetime. The main contribution of the paper is the design of a sensor network that can meets the goal of reactivity and reliability, and that demonstrates satisfactory robustness and relatively longer network lifetime.

Wireless Sensor Network Design for Wildfire Monitoring

Due to the bandwidth limitation as well as workload variations in today's networked control systems (NCSs), available network resources for control applications are prone to be scarce. With traditional static schedule, the quality of control (QoC) could be significantly degraded. To attack the problem of network scheduling in a NCS with several control loops, a network scheduler that can be easily implemented in existing networked control applications is presented. The objective is to improve the overall system performance via dynamic resource allocation. Following the newly emerging methodology of feedback scheduling, the control requirements are integrated in the scheduling of the shared network. As a novel approach to allocating network resources, the proposed network scheduler dynamically adjusts control loops' priorities according to their current urgency values. Preliminary simulation results highlight the advantages of the proposed approach.

https://ieeexplore.ieee.org/iel5/10234/32635/01528309.pdf

Feedback based network scheduling of networked control systems

A Link Quality Estimation based Routing protocol (LQER) is proposed to meet the high reliability of transmitting data in water environment monitoring in wetlands based on (m,k)-firm link quality estimating. It considers both energy efficiency and link qualities when the route is selected, which makes routing data more reliable and decreases the probability of retransmission, thus saves the energy and prolongs the lifetime of the whole network. Simulation results show that LQER can meet the requirements of energy efficiency, reliability and scalability for water environment monitoring in wetlands.

Research on Wireless Sensor Networks Routing Protocol for Wetland Water Environment Monitoring

The main goal of wireless sensor networks is to collect information from the target environment through a large number of micro sensor nodes. However, the constrained resource on each senor node brings great challenges to the information processing procedure in sensor networks. An overview and some open issues of the intelligent information processing approaches in wireless sensor networks are given in this paper to meet these challenges, which include in-network data aggregation and data compression algorithms, distributed database storage and query strategies, and multi-sensor data fusion rules. The advantages and disadvantages of every approach are also discussed.

A survey of intelligent information processing in wireless sensor network

Power awareness has become a critical issue in real-time scheduling of embedded systems In the context of control applications, the goal of high control performance and low energy consumption are at odds with each other While dynamic voltage/frequency scaling (DVS) has proved to be promising in energy saving while preserving task schedulability, traditional DVS algorithms use either open loop or ad hoc solutions, and hence cannot perform well for dynamic systems where the workload varies significantly By targeting these systems, a novel scheme, namely DVS-FS, which combines DVS and feedback scheduling, is suggested The objective is to save CPU energy as much as possible, while still providing control performance guarantees, which largely depends on successful schedule of the control task set DVS-FS exploits feedback control methodology, and facilitates tradeoffs between energy consumption and control performance through controlling the CPU utilization at a considerably high level Simulation experiments demonstrate that DVS-FS can easily reduce significant energy consumption at the expense of only minor control performance degradation

http://ieeexplore.ieee.org/iel5/10706/33796/01609920.pdf

Feedback scheduling of real-time control tasks in power-aware embedded systems

The performance of multitasking control systems with resource constraints depends not only on controller design, but also on efficient scheduling of the shared computing resources. To support codesign of feedback control and real-time scheduling under overload conditions, a direct feedback scheduling approach is proposed. The goal is to optimize the overall system performance through dynamically allocating limited CPU time based on control requirements. A control application oriented task model is suggested, providing an interface between control performance and task scheduling decision. Based on this model, a novel scheduling algorithm intended for control tasks is presented. The overload condition is effectively handled using a job skipping scheme. Preliminary simulations exhibit the benefits of the proposed approach,

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Xiaohua Dai

Papers

Feedback based network scheduling of networked control systems

Research on Wireless Sensor Networks Routing Protocol for Wetland Water Environment Monitoring

A survey of intelligent information processing in wireless sensor network

Feedback scheduling of real-time control tasks in power-aware embedded systems

Control Oriented Direct Feedback Scheduling