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J. Sens. Actuator Netw., Volume 2, Issue 3 (September 2013) – 10 articles , Pages 388-652

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2489 KiB  
Article
Marmote SDR: Experimental Platform for Low-Power Wireless Protocol Stack Research
by Sándor Szilvási, Benjámin Babják, Péter Völgyesi and Ákos Lédeczi
J. Sens. Actuator Netw. 2013, 2(3), 631-652; https://doi.org/10.3390/jsan2030631 - 9 Sep 2013
Cited by 12 | Viewed by 8485
Abstract
Over the past decade, wireless sensor network research primarily relied on highly-integrated commercial off-the-shelf radio chips. The rigid silicon implementation of the radio stack restricted access to the lower layers; thus, research focused mainly on the medium access control (MAC) layer and above. [...] Read more.
Over the past decade, wireless sensor network research primarily relied on highly-integrated commercial off-the-shelf radio chips. The rigid silicon implementation of the radio stack restricted access to the lower layers; thus, research focused mainly on the medium access control (MAC) layer and above. SRAM field-programmable gate array (FPGA)-based software-defined radios (SDR), on the other hand, provide a flexible architecture to experiment with any and all layers of the radio stack, but usually require desktop computers and draw high currents that prohibit mobile or longer-term outdoor deployments. To address these issues, we have developed a modular flash FPGA-based wireless research platform, called Marmote SDR, that has computational resources comparable to those of SRAM FPGA-based radio platforms, but at a reduced power consumption, with duty cycling support. We discuss the design decisions underlying Marmote SDR and evaluate its power consumption. Furthermore, we present and evaluate an asynchronous and multiple access communication protocol specifically designed for data-gathering wireless sensor networks. Full article
(This article belongs to the Special Issue Feature Papers)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Photo (<b>left</b>) and block diagram (<b>right</b>) of the three-layer Marmote SDR platform comprising a <span class="html-italic">Joshua</span> 2.4 GHz radio front-end (<b>top</b>), a <span class="html-italic">Teton</span> mixed-signal processing (<b>middle</b>) and a <span class="html-italic">Yellowstone</span> battery-operated power management (<b>bottom</b>) module.</p> Full article ">Figure 2
<p>Wake-up time and current draw comparison of SRAM (<b>left</b>) and flash (<b>right</b>) FPGAs measured with development kits that utilize similar size SRAM and flash FPGAs.</p> Full article ">Figure 3
<p>Power consumption comparison of the CC1000 commodity radio-frequency (RF) chip, the Marmote SDR platform and the USRP N210 in various power modes.</p> Full article ">Figure 4
<p>Measurement setup with four Marmote SDR sensor nodes (transmitters), a USRP N210 and desktop computer base station (receiver).</p> Full article ">Figure 5
<p>Simplified block diagram of a direct-sequence spread spectrum (DSSS) transceiver.</p> Full article ">Figure 6
<p>Simplified block diagram of the DSSS-based code division multiple access (DS-CDMA) transmitter implemented on the Marmote software-defined radio (SDR) platform.</p> Full article ">Figure 7
<p>Simplified block diagram of the DS-CDMA receiver implemented on the USRP N210 and GNU Radio platform.</p> Full article ">Figure 8
<p>The output of the PN-matched filter in the synchronizer, showing distinct pulses at the start of five spread packet frames and insensitivity to noise and other ongoing network traffic.</p> Full article ">Figure 9
<p>The amplitude (blue) and phase (red) of the integrate and dump block output for an inaccurately (<b>left</b>) and an accurately (<b>right</b>) synchronized packet.</p> Full article ">Figure 10
<p>Packet reception ratio for the single-node setup under various traffic loads with spreading factors of 8, 16 and 32.</p> Full article ">Figure 11
<p>Packet reception ratio for the two-node setup under various traffic loads with spreading factors of 8, 16 and 32.</p> Full article ">Figure 12
<p>Packet reception ratio for the four-node setup under various traffic loads with spreading factors of 8, 16 and 32.</p> Full article ">
286 KiB  
Article
Physical Layer Design in Wireless Sensor Networks for Fading Mitigation
by Stevan Berber and Nuo Chen
J. Sens. Actuator Netw. 2013, 2(3), 614-630; https://doi.org/10.3390/jsan2030614 - 2 Sep 2013
Cited by 10 | Viewed by 9258
Abstract
This paper presents the theoretical analysis, simulation results and suggests design in digital technology of a physical layer for wireless sensor networks. The proposed design is able to mitigate fading inside communication channel. To mitigate fading the chip interleaving technique is proposed. For [...] Read more.
This paper presents the theoretical analysis, simulation results and suggests design in digital technology of a physical layer for wireless sensor networks. The proposed design is able to mitigate fading inside communication channel. To mitigate fading the chip interleaving technique is proposed. For the proposed theoretical model of physical layer, a rigorous mathematical analysis is conducted, where all signals are presented and processed in discrete time domain form which is suitable for further direct processing necessary for devices design in digital technology. Three different channels are used to investigate characteristics of the physical layer: additive white Gaussian noise channel (AWGN), AWG noise and flat fading channel and AWG noise and flat fading channel with interleaver and deinterleaver blocks in the receiver and transmitter respectively. Firstly, the mathematical model of communication system representing physical layer is developed based on the discrete time domain signal representation and processing. In the existing theory, these signals and their processing are represented in continuous time form, which is not suitable for direct implementation in digital technology. Secondly, the expressions for the probability of chip, symbol and bit error are derived. Thirdly, the communication system simulators are developed in MATLAB. The simulation results confirmed theoretical findings. Full article
(This article belongs to the Special Issue Feature Papers)
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<p>Block schematic of communication system.</p> Full article ">Figure 2
<p>BER curves for a single-correlator receiver in presence of additive white Gaussian noise channel (AWGN): theory (blue) and simulation (red).</p> Full article ">Figure 3
<p>BER curves for a single-correlator receiver in the presence of AWGN and fading: theoretical (blue), simulation (red) for fading, and theory for noise only (black).</p> Full article ">Figure 4
<p>BER curves for a single correlator receiver with interleavers in presence of AWGN and fading: theoretical (blue) for fading, simulated (red) for fading with interleavers and theoretical for noise only (black).</p> Full article ">Figure 5
<p>BER curves for 16-correlator receiver in presence of AWGN: theoretical (black) chip error rate (CER), simulation CER (red) and BER on CER SNR scale (red dashed).</p> Full article ">Figure 6
<p>Rayleigh fading and AWGN are present in the channel.</p> Full article ">Figure 7
<p>Fading channel and interleaver and deinterleaver are present on the transceiver.</p> Full article ">Figure 8
<p>Estimated position of the BER curve when fading channel and interleavers and deinterleavers are present in communication system.</p> Full article ">
3264 KiB  
Article
Performance Analysis and Comparison of Bluetooth Low Energy with IEEE 802.15.4 and SimpliciTI
by Konstantin Mikhaylov, Nikolaos Plevritakis and Jouni Tervonen
J. Sens. Actuator Netw. 2013, 2(3), 589-613; https://doi.org/10.3390/jsan2030589 - 22 Aug 2013
Cited by 83 | Viewed by 24031
Abstract
Bluetooth Low Energy (BLE) is a recently developed energy-efficient short-range wireless communication protocol. In this paper, we discuss and compare the maximum peer-to-peer throughput, the minimum frame turnaround time, and the energy consumption for three protocols, namely BLE, IEEE 802.15.4 and SimpliciTI. The [...] Read more.
Bluetooth Low Energy (BLE) is a recently developed energy-efficient short-range wireless communication protocol. In this paper, we discuss and compare the maximum peer-to-peer throughput, the minimum frame turnaround time, and the energy consumption for three protocols, namely BLE, IEEE 802.15.4 and SimpliciTI. The specifics and the main contributions are the results both of the theoretical analysis and of the empirical measurements, which were executed using the commercially available hardware transceivers and software stacks. The presented results reveal the protocols’ capabilities and enable one to estimate the feasibility of using these technologies for particular applications. Based on the presented results, we draw conclusions regarding the feasibility and the most suitable application scenarios of the BLE technology. Full article
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<p>Structure of IEEE 802.15.4 SF (example of a superframe (SF) with six slots for contention access period (CAP) and ten slots for an contention-free period (CFP) with three guaranteed time slots (GTS)) [<a href="#B27-jsan-02-00589" class="html-bibr">27</a>].</p> Full article ">Figure 2
<p>(<b>a</b>) Timing for data transfer in CAP in the beacon-enabled IEEE 802.15.4 PAN; (<b>b</b>) Timing for data transfer in CFP in the beacon-enabled IEEE 802.15.4 PAN; (<b>c</b>) Timing for data transfer in the nonbeacon-enabled IEEE 802.15.4 PAN.</p> Full article ">Figure 3
<p>(<b>a</b>) Data frame format for IEEE 802.15.4; (<b>b</b>) Data and advertising frame formats for BLE; (<b>c</b>) Data frame format for Texas Instrument’s SimpliciTI.</p> Full article ">Figure 4
<p>BLE stack architecture.</p> Full article ">Figure 5
<p>(<b>a</b>) Timing for data transfer on BLE advertising channels using ADV_IND and ADV_SCAN_IND; (<b>b</b>) Timing for data transfer on BLE advertising channels using ADV_NONCONN_IND; (<b>c</b>) Timing for data transfer on BLE data channels.</p> Full article ">Figure 6
<p>Hardware modules used for the tests: the front row includes extension radio boards (from left to right) CC2540 (BLE), CC2430 (IEEE 802.15.4) and CC2510 (SimpliciTI); the back row includes the battery extender board (left) and the SmartRF04 development board (right).</p> Full article ">Figure 7
<p>LL frame payload’s effect on the maximum unidirectional single-hop throughput for IEEE 802.15.4, BLE and SimpliciTI (analytic results).</p> Full article ">Figure 8
<p>LL frame payload’s effect on the maximum unidirectional single-hop throughput for IEEE 802.15.4, BLE and SimpliciTI (experimental results).</p> Full article ">Figure 9
<p>LL frame payload’s effect on the minimum single-hop turnaround time for IEEE 802.15.4, BLE and SimpliciTI (analytic results).</p> Full article ">Figure 10
<p>LL frame payload’s effect on the minimum single-hop turnaround time for IEEE 802.15.4, BLE and SimpliciTI (experimental results).</p> Full article ">Figure 11
<p>Power consumption for the tested transceivers (see <a href="#jsan-02-00589-t002" class="html-table">Table 2</a>) and protocols for transmitting a 19-byte frame (supply voltage 3 V, radio transmit power 0 dBm).</p> Full article ">
2002 KiB  
Article
A Multi-Agent-Based Intelligent Sensor and Actuator Network Design for Smart House and Home Automation
by Qingquan Sun, Weihong Yu, Nikolai Kochurov, Qi Hao and Fei Hu
J. Sens. Actuator Netw. 2013, 2(3), 557-588; https://doi.org/10.3390/jsan2030557 - 19 Aug 2013
Cited by 72 | Viewed by 14300
Abstract
The smart-house technology aims to increase home automation and security with reduced energy consumption. A smart house consists of various intelligent sensors and actuators operating on different platforms with conflicting objectives. This paper proposes a multi-agent system (MAS) design framework to achieve smart [...] Read more.
The smart-house technology aims to increase home automation and security with reduced energy consumption. A smart house consists of various intelligent sensors and actuators operating on different platforms with conflicting objectives. This paper proposes a multi-agent system (MAS) design framework to achieve smart house automation. The novelties of this work include the developments of (1) belief, desire and intention (BDI) agent behavior models; (2) a regulation policy-based multi-agent collaboration mechanism; and (3) a set of metrics for MAS performance evaluation. Simulations of case studies are performed using the Java Agent Development Environment (JADE) to demonstrate the advantages of the proposed method. Full article
(This article belongs to the Special Issue Feature Papers)
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<p>The proposed design and evaluation procedure of multi-agent systems for smart house technology.</p> Full article ">Figure 2
<p>Illustration of (<b>a</b>) agent architecture; (<b>b</b>) situation perception from event sequences.</p> Full article ">Figure 3
<p>Illustration of multi-agent collaboration.</p> Full article ">Figure 4
<p>The proposed multi-agent system (MAS) architecture for smart house technology.</p> Full article ">Figure 5
<p>BDI model based individual agent behavior design.</p> Full article ">Figure 6
<p>Regulation policy-based multi-agent group behavior design.</p> Full article ">Figure 7
<p>The architecture of a multi-agent system and the finite states of each agent.</p> Full article ">Figure 8
<p>Collaboration scheme design using a Petri-net (PN) graph for three agents. (<b>a</b>) a reachable PN model; (<b>b</b>) the state reachability graph of a valid collaboration model; (<b>c</b>) an unreachable PN model.</p> Full article ">Figure 9
<p>Java Agent Development Environment (JADE) multi-agent implementation.</p> Full article ">Figure 10
<p>Snapshot of the JADE development environment.</p> Full article ">Figure 11
<p>The finite state machines for each agent under the response-time-oriented policy.</p> Full article ">Figure 12
<p>The finite state machines for each agent under the energy-efficient- oriented policy.</p> Full article ">Figure 13
<p>The finite state machines for each agent under the QoS oriented policy.</p> Full article ">Figure 14
<p>BDI models for four types of agents.</p> Full article ">Figure 15
<p>Performance of multi-agent collaborations under three different policies.</p> Full article ">Figure 16
<p>Environment, user and system information: (<b>first row</b>) light intensity; (<b>second row</b>) number of humans; (<b>third row</b>) power condition.</p> Full article ">Figure 17
<p>Comparison of the quality of service (QoS).</p> Full article ">Figure 18
<p>Histogram of the QoS performance under the response-time-oriented policy for (<b>left</b>) high and (<b>right</b>) low power storage levels.</p> Full article ">Figure 19
<p>Histogram of the QoS performance under the quality of service-oriented policy for (<b>left</b>) high and (<b>right</b>) low power storage levels.</p> Full article ">Figure 20
<p>Histogram of the QoS performance under the energy-efficiency-oriented policy for (<b>left</b>) high and (<b>right</b>) low power storage levels.</p> Full article ">Figure 21
<p>Comparison of computation (response) time.</p> Full article ">Figure 22
<p>Histogram of the computation time under the response-time-oriented policy for (<b>left</b>) high and (<b>right</b>) low power storage levels.</p> Full article ">Figure 23
<p>Histogram of the computation time under the quality of service-oriented policy for (<b>left</b>) high and (<b>right</b>) low power storage levels.</p> Full article ">Figure 24
<p>Histogram of the computation time under the energy-efficiency-oriented policy for (<b>left</b>) high and (<b>right</b>) low power storage levels.</p> Full article ">Figure 25
<p>Comparison of system cost.</p> Full article ">Figure 26
<p>Performance of multi-agent systems under three different policies.</p> Full article ">Figure 27
<p>A testbed for MAS-based smart house technology.</p> Full article ">
675 KiB  
Article
Wireless Sensor Network Operating System Design Rules Based on Real-World Deployment Survey
by Girts Strazdins, Atis Elsts, Krisjanis Nesenbergs and Leo Selavo
J. Sens. Actuator Netw. 2013, 2(3), 509-556; https://doi.org/10.3390/jsan2030509 - 16 Aug 2013
Cited by 39 | Viewed by 10696
Abstract
Wireless sensor networks (WSNs) have been a widely researched field since the beginning of the 21st century. The field is already maturing, and TinyOS has established itself as the de facto standard WSN Operating System (OS). However, the WSN researcher community is still [...] Read more.
Wireless sensor networks (WSNs) have been a widely researched field since the beginning of the 21st century. The field is already maturing, and TinyOS has established itself as the de facto standard WSN Operating System (OS). However, the WSN researcher community is still active in building more flexible, efficient and user-friendly WSN operating systems. Often, WSN OS design is based either on practical requirements of a particular research project or research group's needs or on theoretical assumptions spread in the WSN community. The goal of this paper is to propose WSN OS design rules that are based on a thorough survey of 40 WSN deployments. The survey unveils trends of WSN applications and provides empirical substantiation to support widely usable and flexible WSN operating system design. Full article
(This article belongs to the Special Issue Advances in Sensor Network Operating Systems)
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<p><b>Distribution function of mote count in surveyed deployments</b>—Eighty percent of deployments contain less than 50 motes; 50%: less than 20 motes; and 34%: ten or less.</p> Full article ">Figure 2
<p><b>Maximum mote count in surveyed deployments, in each year</b>— peak size in the years 2004–2006; over 100 motes used.</p> Full article ">Figure 3
<p><b>Sensors used in deployments</b>—Temperature, light and acceleration sensors are the most popular: each of them used in more than 20% of analyzed deployments.</p> Full article ">Figure 4
<p><b>Sensor sampling rate used in deployments</b>—Low duty cycle applications with sampling rate below 1 Hz are the most popular; however, high-frequency sampling is also used; the ranges 10–100 Hz and 10–100 kHz are popular.</p> Full article ">Figure 5
<p><b>Custom, adapted and off-the-shelf mote usage in deployments</b>—Almost half of deployments adapt off-the-shelf motes by custom sensing and packaging hardware, 32% use custom platforms and only 20% use commercial motes with default sensing modules.</p> Full article ">Figure 6
<p><b>Deployment network connectivity</b>—Eighty percent of deployments consider a network to be continuously connected, while only 12% experience significant disconnections and 8% use opportunistic communication.</p> Full article ">Figure 7
<p><b>Deployment network topologies</b>—Almost half (47%) use a multi-hop mesh network. One-hop networks are used in 25% of cases; 15% use multiple one-hop networks.</p> Full article ">Figure 8
<p><b>Deployment in-network processing</b>—Raw data preprocessing is used in half of deployments; distributed algorithms and aggregation are seldom used.</p> Full article ">Figure 9
<p><b>Operating systems used in analyzed deployments</b>—Sixty percent of deployments use the <span class="html-italic">de-facto</span> standard: TinyOS. Seventeen percent use self-made or customized OSs.</p> Full article ">Figure 10
<p><b>The number of kernel level software services used in deployments</b>—fifty-five percent of deployments use two or less kernel services. For 28%, the kernel service count is unknown.</p> Full article ">Figure 11
<p><b>The number of application layer software tasks used in deployments.</b>—Thirty-three percent of deployments use just one task; however, up to six tasks are used in more complex cases. The task count is unknown in 18% of deployments.</p> Full article ">
915 KiB  
Article
Real-Time Recognition of Action Sequences Using a Distributed Video Sensor Network
by Rahul Kavi and Vinod Kulathumani
J. Sens. Actuator Netw. 2013, 2(3), 486-508; https://doi.org/10.3390/jsan2030486 - 18 Jul 2013
Cited by 8 | Viewed by 6476
Abstract
In this paper, we describe how information obtained from multiple views usinga network of cameras can be effectively combined to yield a reliable and fast humanactivity recognition system. First, we present a score-based fusion technique for combininginformation from multiple cameras that can handle [...] Read more.
In this paper, we describe how information obtained from multiple views usinga network of cameras can be effectively combined to yield a reliable and fast humanactivity recognition system. First, we present a score-based fusion technique for combininginformation from multiple cameras that can handle the arbitrary orientation of the subjectwith respect to the cameras and that does not rely on a symmetric deployment of thecameras. Second, we describe how longer, variable duration, inter-leaved action sequencescan be recognized in real-time based on multi-camera data that is continuously streaming in.Our framework does not depend on any particular feature extraction technique, and as aresult, the proposed system can easily be integrated on top of existing implementationsfor view-specific classifiers and feature descriptors. For implementation and testing of theproposed system, we have used computationally simple locality-specific motion informationextracted from the spatio-temporal shape of a human silhouette as our feature descriptor.This lends itself to an efficient distributed implementation, while maintaining a high framecapture rate. We demonstrate the robustness of our algorithms by implementing them ona portable multi-camera, video sensor network testbed and evaluating system performanceunder different camera network configurations. Full article
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<p>A frame of a subject performing a <span class="html-italic">kicking</span> action from six different views. Given visual data from a subset of such views, the objective of the proposed system is to recognize a sequence of actions being performed.</p> Full article ">Figure 2
<p>Deployment of cameras in the system.</p> Full article ">Figure 3
<p>View-angle of a camera and view-angle sets. (<b>a</b>) View angle of a camera, C; (<b>b</b>) view angle sets defined in the system.</p> Full article ">Figure 4
<p>A subset of the background subtracted blobs extracted by a camera are shown for the single arm waving action. The background subtracted binary blobs are used to generate the motion energy image, which shows motion concentrated near the top-left region. The motion energy is then represented as a 7-by-7 array feature vector that represents the spatial distribution of motion energy over the set of frames.</p> Full article ">Figure 5
<p>Collection of training data. The subject is at the center of the network and performs each training action while facing the region between rays, <math display="inline"> <mrow> <mi>Z</mi> <mi>A</mi> </mrow> </math> and <math display="inline"> <mrow> <mi>Z</mi> <mi>B</mi> </mrow> </math>. All cameras record the data. Because of symmetry, data collected in camera <math display="inline"> <msub> <mi>C</mi> <mi>i</mi> </msub> </math> corresponds to view <math display="inline"> <msub> <mi>V</mi> <mi>i</mi> </msub> </math> (<math display="inline"> <mrow> <mo>∀</mo> <mi>i</mi> <mo>:</mo> <mn>1</mn> <mo>≤</mo> <mi>i</mi> <mo>≤</mo> <mn>8</mn> <mo>)</mo> </mrow> </math>.</p> Full article ">Figure 6
<p>Determining configuration set for subject location, <span class="html-italic">Z</span>. Consider <math display="inline"> <msub> <mi>C</mi> <mn>1</mn> </msub> </math> as the reference camera. The real orientation of the subject with respect to the reference camera is not known. However, the angles, <math display="inline"> <msub> <mi>θ</mi> <mrow> <mi>r</mi> <mo>,</mo> <mi>s</mi> </mrow> </msub> </math>, between the principal axes of each pair of cameras <math display="inline"> <mrow> <mo>(</mo> <mi>r</mi> <mo>,</mo> <mi>s</mi> <mo>)</mo> </mrow> </math> is known. Then, for each possible view, <math display="inline"> <mrow> <msub> <mi>V</mi> <mi>j</mi> </msub> <mrow> <mo>(</mo> <mn>1</mn> <mo>≤</mo> <mi>j</mi> <mo>≤</mo> <mn>8</mn> <mo>)</mo> </mrow> </mrow> </math>, that camera <math display="inline"> <msub> <mi>C</mi> <mn>1</mn> </msub> </math> can provide for the action being performed, the views provided by other available cameras can be determined, resulting in <math display="inline"> <msub> <mi>N</mi> <mi>v</mi> </msub> </math> possible configurations.</p> Full article ">Figure 7
<p>Recognition accuracy for the system with a different number of available camera views.</p> Full article ">Figure 8
<p>True/false matches <span class="html-italic">vs</span>. threshold with all views intact for Localized Motion Energy Image (LMEI)-Linear Discriminant Analysis (LDA)-based classifier and Histogram of Oriented Gradients (HOG)-Support Vector Machines (SVM)-based classifier. (<b>a</b>) LMEI-LDA classifier. (<b>b</b>) HOG-SVM classifier.</p> Full article ">Figure 9
<p>True/false matches <span class="html-italic">vs</span>. threshold with two views removed for LMEI-LDA-based classifier and HOG-SVM-based classifier. (<b>a</b>) LMEI-LDA classifier. (<b>b</b>) HOG-SVM classifier.</p> Full article ">Figure 10
<p>True/false matches <span class="html-italic">vs</span>. threshold with four views removed for LMEI-LDA-based classifier and HOG-SVM-based classifier. (<b>a</b>) LMEI-LDA classifier. (<b>b</b>) HOG-SVM classifier.</p> Full article ">Figure 11
<p>True/false matches <span class="html-italic">vs</span>. threshold with six views removed for LMEI-LDA-based classifier and HOG-SVM-based classifier. (<b>a</b>) LMEI-LDA classifier. (<b>b</b>) HOG-SVM classifier.</p> Full article ">Figure 12
<p>True/false matches <span class="html-italic">vs</span>. threshold with seven views removed for LMEI-LDA-based classifier and HOG-SVM-based classifier. (<b>a</b>) LMEI-LDA classifier. (<b>b</b>) HOG-SVM classifier.</p> Full article ">Figure 13
<p>True match rate and misclassification rate at false match rate of 20% for LMEI-LDA-based classifier and HOG-SVM-based classifier. (<b>a</b>) LMEI-LDA classifier. (<b>b</b>) HOG-SVM classifier.</p> Full article ">
1133 KiB  
Article
An Adaptive Strategy for an Optimized Collision-Free Slot Assignment in Multichannel Wireless Sensor Networks
by Ridha Soua, Erwan Livolant and Pascale Minet
J. Sens. Actuator Netw. 2013, 2(3), 449-485; https://doi.org/10.3390/jsan2030449 - 16 Jul 2013
Cited by 12 | Viewed by 7185
Abstract
Convergecast is the transmission paradigm used by data gathering applications in wireless sensor networks (WSNs). For efficiency reasons, a collision-free slotted medium access is typically used: time slots are assigned to non-conflicting transmitters. Furthermore, in any slot, only the transmitters and the corresponding [...] Read more.
Convergecast is the transmission paradigm used by data gathering applications in wireless sensor networks (WSNs). For efficiency reasons, a collision-free slotted medium access is typically used: time slots are assigned to non-conflicting transmitters. Furthermore, in any slot, only the transmitters and the corresponding receivers are awake, the other nodes sleeping in order to save energy. Since a multichannel network increases the throughput available to the application and reduces interference, multichannel slot assignment is an emerging research domain in WSNs. First, we focus on a multichannel time slot assignment that minimizes the data gathering delays. We compute the optimal time needed for a raw data convergecast in various multichannel topologies. Then, we focus on how to adapt such an assignment to dynamic demands of transmissions (e.g., alarms, temporary additional application needs and retransmissions). We formalize the problem using linear programming, and we propose an incremental technique that operates on an optimized primary schedule to provide bonus slots to meet new transmission needs. We propose AMSA, an Adaptive Multichannel Slot Assignment algorithm, which takes advantage of bandwidth spatial reuse, and we evaluate its performances in terms of the number of slots required, slot reuse, throughput and the number of radio state switches. Full article
(This article belongs to the Special Issue Advances in Sensor Network Operating Systems)
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Figure 1
<p>A data gathering frame.</p> Full article ">Figure 2
<p>The optimal number of slots, <math display="inline"> <msub> <mi>n</mi> <mrow> <mi>s</mi> <mi>l</mi> <mi>o</mi> <mi>t</mi> </mrow> </msub> </math>, for various topologies with different numbers of sink interfaces, <math display="inline"> <msub> <mi>n</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>t</mi> <mi>e</mi> <mi>r</mi> <mi>f</mi> </mrow> </msub> </math>, and channels, <math display="inline"> <msub> <mi>n</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>a</mi> <mi>n</mi> <mi>n</mi> <mi>e</mi> <mi>l</mi> </mrow> </msub> </math>, with the notation: (<math display="inline"> <msub> <mi>n</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>t</mi> <mi>e</mi> <mi>r</mi> <mi>f</mi> </mrow> </msub> </math>;<math display="inline"> <msub> <mi>n</mi> <mrow> <mi>c</mi> <mi>h</mi> <mi>a</mi> <mi>n</mi> <mi>n</mi> <mi>e</mi> <mi>l</mi> </mrow> </msub> </math>)=<math display="inline"> <msub> <mi>n</mi> <mrow> <mi>s</mi> <mi>l</mi> <mi>o</mi> <mi>t</mi> </mrow> </msub> </math>. (<b>a</b>) (1;2) = 24; (<b>b</b>) (1;2) = 33 (2;2) = 26 (3;3) = 26; (<b>c</b>) (1;2) = 65 (2;2) = 45 (3;3) = 45.</p> Full article ">Figure 3
<p>Case where <math display="inline"> <mrow> <mi>δ</mi> <mo>=</mo> <mn>1</mn> </mrow> </math> when the sink is equipped with three radio interfaces and three channels. (<b>a</b>) (3;3) = 14; (<b>b</b>) (3;3) = 13.</p> Full article ">Figure 3 Cont.
<p>Case where <math display="inline"> <mrow> <mi>δ</mi> <mo>=</mo> <mn>1</mn> </mrow> </math> when the sink is equipped with three radio interfaces and three channels. (<b>a</b>) (3;3) = 14; (<b>b</b>) (3;3) = 13.</p> Full article ">Figure 4
<p>Extra slots scheduling when <math display="inline"> <mrow> <mi>M</mi> <mi>a</mi> <mi>x</mi> <mi>d</mi> <mi>e</mi> <mi>p</mi> <mi>t</mi> <mi>h</mi> <mo>=</mo> <mn>6</mn> </mrow> </math>.</p> Full article ">Figure 5
<p>The topology of the network.</p> Full article ">Figure 6
<p>The primary schedule.</p> Full article ">Figure 7
<p>The bonus slot assignment for node 6.</p> Full article ">Figure 8
<p>The new slot assignment taking into account the additional demand of node 6.</p> Full article ">Figure 9
<p>The bonus slot assignment for node 9.</p> Full article ">Figure 10
<p>The new schedule taking into account the additional demand of node 9.</p> Full article ">Figure 11
<p>Two configurations of 20 nodes. (<b>a</b>) <math display="inline"> <msub> <mi>T</mi> <mi>s</mi> </msub> </math> configuration; (<b>b</b>) <math display="inline"> <msub> <mi>T</mi> <mi>n</mi> </msub> </math> configuration.</p> Full article ">Figure 12
<p>Percentage of required extra slots. (<b>a</b>) in <math display="inline"> <msub> <mi>T</mi> <mi>s</mi> </msub> </math> configuration <a href="#jsan-02-00449-f011" class="html-fig">Figure 11</a>a; (<b>b</b>) in <math display="inline"> <msub> <mi>T</mi> <mi>n</mi> </msub> </math> configuration <a href="#jsan-02-00449-f011" class="html-fig">Figure 11</a>b.</p> Full article ">Figure 13
<p>Percentage of required extra slots. (<b>a</b>) in <math display="inline"> <msub> <mi>T</mi> <mi>s</mi> </msub> </math> configuration; (<b>b</b>) in <math display="inline"> <msub> <mi>T</mi> <mi>n</mi> </msub> </math> configuration.</p> Full article ">Figure 14
<p>Two configurations for the same topology of 20 nodes. (<b>a</b>) <math display="inline"> <msub> <mi>T</mi> <mi>s</mi> </msub> </math> configuration; (<b>b</b>) <math display="inline"> <msub> <mi>T</mi> <mi>n</mi> </msub> </math> configuration.</p> Full article ">Figure 15
<p>Percentage of extra slots required. (<b>a</b>) in <math display="inline"> <msub> <mi>T</mi> <mi>s</mi> </msub> </math> configuration; (<b>b</b>) in <math display="inline"> <msub> <mi>T</mi> <mi>n</mi> </msub> </math> configuration.</p> Full article ">Figure 16
<p>Distribution of nodes requiring extra slots.</p> Full article ">Figure 17
<p>Impact on schedule length in case of nodes requiring extra slots.</p> Full article ">Figure 18
<p>Average number of extra slots required with 5% of nodes having a bonus request. (<b>a</b>) in <math display="inline"> <msub> <mi>T</mi> <mi>s</mi> </msub> </math> configuration; (<b>b</b>) in <math display="inline"> <msub> <mi>T</mi> <mi>n</mi> </msub> </math> configurations.</p> Full article ">Figure 19
<p>Average number of extra slots required with 10% of nodes having a bonus request. (<b>a</b>) in <math display="inline"> <msub> <mi>T</mi> <mi>s</mi> </msub> </math> configuration; (<b>b</b>) in <math display="inline"> <msub> <mi>T</mi> <mi>n</mi> </msub> </math> configurations.</p> Full article ">Figure 20
<p>Average number of extra slots required with 20% of nodes having a bonus request. (<b>a</b>) in <math display="inline"> <msub> <mi>T</mi> <mi>s</mi> </msub> </math> configuration; (<b>b</b>) in <math display="inline"> <msub> <mi>T</mi> <mi>n</mi> </msub> </math> configurations.</p> Full article ">Figure 21
<p>Slot reuse ratio with 20% of nodes having a bonus request. (<b>a</b>) in <math display="inline"> <msub> <mi>T</mi> <mi>s</mi> </msub> </math> configuration; (<b>b</b>) in <math display="inline"> <msub> <mi>T</mi> <mi>n</mi> </msub> </math> configurations.</p> Full article ">Figure 22
<p>Average number of state switches per node with 20% of nodes having a bonus request. (<b>a</b>) in <math display="inline"> <msub> <mi>T</mi> <mi>s</mi> </msub> </math> configuration; (<b>b</b>) in <math display="inline"> <msub> <mi>T</mi> <mi>n</mi> </msub> </math> configuration.</p> Full article ">Figure 23
<p>Sink interfaces occupation ratio with 20% of nodes having a bonus request. (<b>a</b>) in <math display="inline"> <msub> <mi>T</mi> <mi>s</mi> </msub> </math> configuration; (<b>b</b>) in <math display="inline"> <msub> <mi>T</mi> <mi>n</mi> </msub> </math> configuration.</p> Full article ">Figure 24
<p>Distribution of nodes requiring extra slots.</p> Full article ">Figure 25
<p>Impact of demands for bonus slots on the schedule length.</p> Full article ">Figure 26
<p>Number of slots required for convergecast in (<b>a</b>) <math display="inline"> <msub> <mi>T</mi> <mi>S</mi> </msub> </math> configurations (<b>b</b>) <math display="inline"> <msub> <mi>T</mi> <mi>N</mi> </msub> </math> configurations.</p> Full article ">Figure 27
<p>Optimality of MODESA recalculated and AMSA in <math display="inline"> <msub> <mi>T</mi> <mi>s</mi> </msub> </math> and <math display="inline"> <msub> <mi>T</mi> <mi>n</mi> </msub> </math> configurations.</p> Full article ">Figure 28
<p>Inaccuracy of MODESA recalculated and AMSA in <math display="inline"> <msub> <mi>T</mi> <mi>s</mi> </msub> </math> and <math display="inline"> <msub> <mi>T</mi> <mi>n</mi> </msub> </math> configurations.</p> Full article ">
1760 KiB  
Article
Energy-Efficient Packet Relaying in Wireless Image Sensor Networks Exploiting the Sensing Relevancies of Source Nodes and DWT Coding
by Daniel G. Costa, Luiz Affonso Guedes, Francisco Vasques and Paulo Portugal
J. Sens. Actuator Netw. 2013, 2(3), 424-448; https://doi.org/10.3390/jsan2030424 - 10 Jul 2013
Cited by 9 | Viewed by 8607
Abstract
When camera-enabled sensors are deployed for visual monitoring, a new set of innovative applications is allowed, enriching the use of wireless sensor network technologies. In these networks, energy-efficiency is a highly desired optimization issue, mainly because transmission of images and video streams over [...] Read more.
When camera-enabled sensors are deployed for visual monitoring, a new set of innovative applications is allowed, enriching the use of wireless sensor network technologies. In these networks, energy-efficiency is a highly desired optimization issue, mainly because transmission of images and video streams over resource-constrained sensor networks is more stringent than transmission of conventional scalar data. Due to the nature of visual monitoring, that follows a directional sensing model, camera-enabled sensors may have different relevancies for the application, according to the desired monitoring tasks and the current sensors’ poses and fields of view. Exploiting this concept, each data packet may be associated with a priority level related to the packet’s origins, which may be in turn mapped to an energy threshold level. In such way, we propose an energy-efficient relaying mechanism where data packets are only forwarded to the next hop if the associated energy threshold level is below the current energy level of the relaying node. Thus, packets from low-relevant source nodes will be silently dropped when the current energy level of intermediate nodes run below the pre-defined thresholds. Doing so, energy is saved potentially prolonging the network lifetime. Besides the sensing relevancies of source nodes, the relevance of DWT subbands for reconstruction of original images is also considered. This allows the creation of a second level of packet prioritization, assuring a minimal level of image quality even for the least relevant source nodes. We performed simulations for the proposed relaying mechanism, assessing the expected performance over a traditional relaying paradigm. Full article
(This article belongs to the Special Issue Multimedia Sensor Networks for Mission-Critical Surveillance Applications)
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Figure 1

Figure 1
<p>Different relevancies according to the application requirements and cameras’ poses.</p> Full article ">Figure 2
<p>Threshold-based packet relaying regarding the sensing relevancies of source nodes.</p> Full article ">Figure 3
<p>Discrete Wavelet Transform (DWT) processing generating two and three levels of resolution.</p> Full article ">Figure 4
<p>Threshold-based packet relaying, regarding the sensing relevancies of source nodes and DWT subbands.</p> Full article ">Figure 5
<p>Different reconstructed images according to the considered 1-level 2D DWT subbands.</p> Full article ">Figure 6
<p>Communication scenarios for the simulations.</p> Full article ">Figure 7
<p>Energy consumption for Scenario 1. (<b>a</b>) <span class="html-italic">e</span><sub>1</sub> = 0.9, <span class="html-italic">e</span><sub>2</sub> = 0.7 and <span class="html-italic">e</span><sub>3</sub> = 0.3. (<b>b</b>) <span class="html-italic">e</span><sub>1</sub> = 0.95, <span class="html-italic">e</span><sub>2</sub> = 0.8 and <span class="html-italic">e</span><sub>3</sub> = 0.7.</p> Full article ">Figure 8
<p>Average quality of the images that reach the sink for different transmission configurations.</p> Full article ">Figure 9
<p>Energy consumption for Scenario 2. (<b>a</b>) <span class="html-italic">e</span><sub>1</sub> = 0.9, <span class="html-italic">e</span><sub>2</sub> = 0.7 and <span class="html-italic">e</span><sub>3</sub> = 0.3. (<b>b</b>) <span class="html-italic">e</span><sub>1 </sub>= 0.95, <span class="html-italic">e</span><sub>2 </sub>= 0.8 and <span class="html-italic">e</span><sub>3 </sub>= 0.7.</p> Full article ">Figure 10
<p>Relation between energy consumption and the defined energy thresholds.</p> Full article ">Figure 11
<p>Energy consumption after 12 days. (<b>a</b>) <span class="html-italic">e</span><sub>1 </sub>= 0.9, <span class="html-italic">e</span><sub>2 </sub>= 0.7 and <span class="html-italic">e</span><sub>3 </sub>= 0.3. (<b>b</b>) <span class="html-italic">e</span><sub>1 </sub>= 0.95, <span class="html-italic">e</span><sub>2 </sub>= 0.8 and <span class="html-italic">e</span><sub>3 </sub>= 0.7.</p> Full article ">Figure 12
<p>Average quality of the images that reach the sink for different transmission configurations and also considering the SR-DWT-based relaying approach.</p> Full article ">
273 KiB  
Article
Sufficiency of Local Feedback for Sensor-Actuator Network-Based Control Systems with Distance Sensitivity Properties
by Vinod Kulathumani and Bryan Lemon
J. Sens. Actuator Netw. 2013, 2(3), 409-423; https://doi.org/10.3390/jsan2030409 - 3 Jul 2013
Cited by 1 | Viewed by 5977
Abstract
Timely dissemination of required state information poses a significant challenge in the design of distributed sensor/actuator network-based control systems. In this paper, distance sensitivity properties inherent in many sensor-actuator network-based control systems are exploited to establish conditions under which information within a bounded [...] Read more.
Timely dissemination of required state information poses a significant challenge in the design of distributed sensor/actuator network-based control systems. In this paper, distance sensitivity properties inherent in many sensor-actuator network-based control systems are exploited to establish conditions under which information within a bounded locality of each controller closely approximates optimal control based on knowledge of system-wide state information. By doing so, it is shown that optimal control in extremely large-scale distributed control systems can be achieved in O(1) time using information only within a fixed neighborhood around each controller, the size of which depends on the decay characteristics of the actuator influence matrix. Full article
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<p>(<b>a</b>) Influence matrix for a set of 32 linearly deployed sensor-actuator system with a bounded influence region; (<b>b</b>) inverse of corresponding influence matrix (gray-scale shades are used to represent the magnitude of each matrix element, with black denoting zero and white denoting the maximum value).</p> Full article ">Figure 2
<p>(<b>a</b>) Influence matrix for a set of 32 linearly deployed sensor-actuator system with a polynomially decaying influence region; (<b>b</b>) inverse of corresponding influence matrix (gray-scale shades are used to represent the magnitude of each matrix element, with black denoting zero and white denoting the maximum value).</p> Full article ">Figure 3
<p>(<b>a</b>) Influence of an actuator in the center of a uniform 32-by-32 grid network of a collocated sensor and actuator system with a polynomially decaying influence region; (<b>b</b>) elements of the inverse matrix corresponding to the center actuator (gray-scale shades are used to represent the magnitude of each matrix element, with black denoting zero and white denoting the maximum value).</p> Full article ">Figure 4
<p>(<b>a</b>) Impact of control distance on the root mean square error across all sensors for a 256 node network; (<b>b</b>) impact of control distance on the root mean square error across all sensors for a <math display="inline"> <mrow> <mn>1</mn> <mo>,</mo> <mn>024</mn> </mrow> </math> node network.</p> Full article ">Figure 5
<p>(<b>a</b>) Required control distance so that error across all sensors is less than <math display="inline"> <mrow> <mn>10</mn> <mo>%</mo> </mrow> </math> across 10 trials in a 256 node network and <math display="inline"> <mrow> <mn>1</mn> <mo>,</mo> <mn>024</mn> </mrow> </math> node network; (<b>b</b>) required control distance so that error across all sensors is less than <math display="inline"> <mrow> <mn>10</mn> <mo>%</mo> </mrow> </math>, as a function of network size.</p> Full article ">Figure 6
<p>(<b>a</b>) Convergence as a function of the control distance in a 256 node network; (<b>b</b>) convergence as a function of the control distance in a <math display="inline"> <mrow> <mn>1</mn> <mo>,</mo> <mn>024</mn> </mrow> </math> node network.</p> Full article ">
799 KiB  
Article
Prototyping an Operational System with Multiple Sensors for Pasture Monitoring
by David L. Gobbett, Rebecca N. Handcock, Andre Zerger, Chris Crossman, Philip Valencia, Tim Wark and Micah Davies
J. Sens. Actuator Netw. 2013, 2(3), 388-408; https://doi.org/10.3390/jsan2030388 - 1 Jul 2013
Cited by 7 | Viewed by 9044
Abstract
Combining multiple proximal sensors within a wireless sensor network (WSN) enhances our capacity to monitor vegetation, compared to using a single sensor or non-networked setup. Data from sensors with different spatial and temporal characteristics can provide complementary information. For example, point-based sensors such [...] Read more.
Combining multiple proximal sensors within a wireless sensor network (WSN) enhances our capacity to monitor vegetation, compared to using a single sensor or non-networked setup. Data from sensors with different spatial and temporal characteristics can provide complementary information. For example, point-based sensors such as multispectral sensors which monitor at high temporal frequency but, at a single point, can be complemented by array-based sensors such as digital cameras which have greater spatial resolution but may only gather data at infrequent intervals. In this article we describe the successful deployment of a prototype system for using multiple proximal sensors (multispectral sensors and digital cameras) for monitoring pastures. We show that there are many technical issues involved in such a deployment, and we share insights relevant for other researchers who may consider using WSNs for an operational deployment for pasture monitoring under often difficult environmental conditions. Although the sensors and infrastructure are important, we found that other issues arise and that an end-to-end workflow is an essential part of effectively capturing, processing and managing the data from a WSN. Our deployment highlights the importance of testing and ongoing monitoring of the entire workflow to ensure the quality of data captured. We demonstrate that the combination of different sensors enhances our ability to identify sensor problems necessary to collect accurate data for pasture monitoring. Full article
(This article belongs to the Special Issue Feature Papers)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Node with solar powered Fleck<sup>®</sup>, paired multispectral sensors and digital cameras. Note the cosine diffusion filter is fitted to only the upward-pointing sensor.</p> Full article ">Figure 2
<p>Upward-pointing multispectral sensor readings showing gain calibration issue resulting in clipping of NIR band (830 nm) readings during the middle hours of a summer day (3 December 2009), due to incorrect analogue to digital converter calibration.</p> Full article ">Figure 3
<p>A dip observed in the raw readings and NDVI, resulting from the sensor node shading the sensed area during the middle of summer days (13 December 2010). The shade effect illustrated here is far less apparent in the raw readings from the downward-pointing sensors (which sense upward-welling radiation) than in the NDVI index calculated from the four sensors. Data from the other two bands, and the other calculated vegetation indices, is not shown.</p> Full article ">Figure 4
<p>Time-series of images from the RGB digital camera mounted on one of the nodes at the Crace site. Shadows from the node arm can be seen in iii, v, vi, and ix. The 20 mm diameter yellow markers used as GCPs for rectifying images are visible as white dots, but are obscured by grass in some images.</p> Full article ">
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