Energy-aware Fault-tolerant and Real-time Wireless Sensor Network for Control System
|
|
- Marion Wilson
- 6 years ago
- Views:
Transcription
1 Energy-aware Fault-tolerant and Real-time Wireless Sensor Network for Control System Thesis Proposal Wenchen Wang Computer Science, University of Pittsburgh Committee: Dr. Daniel Mosse, Computer Science, University of Pittsburgh (Advisor) Dr. Rami Melhem, Computer Science, University of Pittsburgh Dr. Youtao Zhang, Computer Science, University of Pittsburgh Dr. Daniel Cole, Mechanical Eng and Materials Science, University of Pittsburgh
2 Outline Background and Motivation Wireless control systems Major challenges Thesis statement Preliminary work Fault-tolerant network design Network reconfiguration: time-correlated faults Proposed work Network reconfiguration: space-correlated faults Real-time network flow scheduling Timeline 2
3 Background and Motivation
4 Wireless Control Systems Ehealth, smart home, power grid etc Background and Motivation 4
5 Wired vs. Wireless Control System (WCS) Wired Control System Actuator control signal Plant Remote Controller Sensors measurements Not easy to do deployment and maintenance Wireless Control System (WCS) Actuator Plant Wireless Network Sensors control signal Remote Controller measurements Background and Motivation 5
6 Wired vs. Wireless Control System (WCS) Wired Control System Actuator control signal Plant Remote Controller Sensors measurements Not easy to do deployment and maintenance Wireless Control System (WCS) Actuator control signal Plant Delay and Message Loss Remote Controller Sensors measurements Network Imperfections Background and Motivation 6
7 Major Challenges of WCS Instability [Zhang CS 01, Jusuf ICCSII 12] When the physical system is unstable, the plant or the device can be damaged and leads to serious safety issues and financial loss. Performance Degradation [Li ICCPS 16] Network imperfections can induce additional error, network-induced error Wired control system output Wireless control system output Network-induced error Background and Motivation 7
8 Current Solutions Control system solution [De AC 08, Shi IJC 10] Network solution Fault-tolerance [Han RTAS 11] Real-time scheduling [Hong ECRTS 15] Network and control system co-design solution Limited works Simulator development [Li ICCPS 15] Redesign network protocol [Gatsis ICCPS 16] Limitations No study from network perspective to address control system stability issue No research addressing time/space-correlated link failures in WCS Lack of research on the impact of network real-time performance on control quality Background and Motivation 8
9 My proposal Actuator Plant Sensors Wireless Energy Consumption Network control signal measurements Controller Instability P1: how do we guarantee control system stability? Performance Degradation P2: how do we reduce network-induced error for a single control system? P3: how do we reduce the total network-induced error for multiple control systems? Background and Motivation 9
10 Thesis statement Is it possible to build a power-aware fault-tolerant real-time wireless sensor network for control system? P1: how do we guarantee control system stability? Fault-tolerant network design (completed) [Wang IRI 16] P2: how do we reduce network-induced error for a single control system? Network reconfiguration: time-correlated faults (completed) [Wang RTAS 17: WiP, Wang ECRTS 17 submitted] P2: how do we reduce network-induced error for a single control system? Network reconfiguration: space-correlated faults (future) P3: how do we reduce total network-induced error for multiple control systems? Real-time network flow scheduling (future) [Wang RTAS 17: WiP] Background and Motivation 10
11 11 Fault-tolerant Network Design (Completed) P1: how do we guarantee control system stability?
12 Background Based on a fault-tolerant wireless protocol: ridesharing [Gobriel SECON 06] TDMA scheduling A node has one primary parent and multiple backup parents Link failures Link success ratio (LSR) Link fails with probability, (1-LSR) Network reliability Delivery ratio (DR) Preliminary Work 1 12
13 Background: Our Control System Primary heat exchanger system (PHX) in a small modular reactor (SMR) of a nuclear power plant (NPP) Transfer power from inside the reactor to the outside Temperature and mass flow rate Preliminary Work 1 13
14 Problem Statement Control system stability requirement, network health (NH): NH = p 1 network 2 + p 2 network + p 3 (1 DR) network delay delivery ratio where p 1, p 2, p 3 are constants when NH 0, the control system is stable. Objective To satisfy the stability requirement: NH 0 Minimum energy consumption Solution Fault-tolerant node placement design Computation model design to select the best node placement with minimum number of relay nodes Preliminary Work 1 14
15 Fault-tolerant Node Placement Design K-connected region K-edge disjoint paths from sensors to virtual roots Consume fewer nodes, less flexible Relay region One line of primary nodes Several lines of backup nodes Nodes placed as close as possible More flexible, consume more nodes Node placement set creation Activate backup paths/ backup nodes Preliminary Work 1 15
16 Computation Model Network health estimation on the node placement set Delivery ratio Network delay Choose best node placement design for a given average LSR NH 0 Minimum number of relay nodes -> minimum energy consumption Preliminary Work 1 16
17 Computation Model: Delivery Ratio Estimation Expected number of messages received by remote controller (RC) m DR= i=1 (p RC i i), p RC i is the probability of received i messages by the remote controller, m is the total number of messages sent from sensors State: message-receiving situation for a level sorted array m 0, m 1,, m n, p i probability, depends on LSR level l ,2,3, 0.2 Preliminary Work 1 17
18 Computation Model: Delivery Ratio Estimation Expected number of messages received by remote controller (RC) m DR= i=1 (p RC i i), p RC i is the probability of received i messages by the remote controller, m is the total number of messages sent from sensors State: message-receiving situation for a level sorted array m 0, m 1,, m n p RC i calculation Probabilities of final states at RC level are corresponding to p RC i (1 i m), p i probability, depends on LSR states of level (l+1) 0 1 State-generation Intermediate states of level l p 1 2 p 2 3 p 3 4 p 4 5 p 5 6 State-combination Final states of level l p 1 + p 3 7 p 2 + p 4 8 p 5 9 Preliminary Work 1 18
19 Computation Model: Network Delay and NH Worst-case network delay ( network ) estimation network = slot N Total #nodes TDMA scheduling time slot NH estimation NH = p 1 network 2 + p 2 network + p 3 (1 DR) Node placement selection with minimum number of nodes, given LSR Preliminary Work 1 19
20 Evaluation Computation Model Up to 3 lines of backup nodes Up to 4-edge disjoint paths Simulation Up to 7 lines of backup nodes TOSSIM simulator [Levis SenSys 03] Metrics DR Meaning Delivery ratio Network health Minimum number of nodes of computation model results Minimum number of nodes of simulation results Comparison Preliminary Work 1 20
21 Computation Model Results The inflection points happen when there is a complete line of backup nodes. The slope decreases when adding more lines of backup nodes. With the NH computation results, we can estimate best node placement design for given LSR Preliminary Work 1 21
22 Simulation Results Adding more nodes does not always help When there are 52 nodes, DR reaches maximum NH decreases Preliminary Work 1 22
23 Minimum Number of Nodes Comparison: Computation vs. Simulation RSSI LSR LSR stdv MinCMR MinSR Diff % % % % % Computation model is accurate with average 4.1% difference. Preliminary Work 1 23
24 24 Network Reconfiguration: Time-correlated Faults (Completed) P2: how do we reduce network-induced error for a single control system?
25 Background Sensors sense and send measurements periodically to the controller with sensing sampling period Controller calculates control signal with control sampling period Actuator Plant Sensors control signal Wireless Network measurements sensing sampling period 0.05s Controller Control sampling period 0.1s Preliminary Work 2 25
26 Problem Statement Trade-off between delivery ratio and delay Higher delivery ratio -> more redundant nodes -> more delay Optimal network configuration Time-correlated link failures [Baccour TOSN 12] Network reconfiguration Objective: network-induced error reduction for a single control system Solution Network reconfiguration framework Preliminary Work 2 26
27 Network Reconfiguration Framework Input: network configuration set The network node placement set Offline Optimal network configuration table indexed by LSR values. Online LSR estimation at run time Centralized network reconfiguration Preliminary Work 2 27
28 Offline Computation Network imperfection model Define total induced delay to the control system estimation as consecutive message losses sensing sampling period n loss ~ dr = network + n loss ssp csp csp Control sampling period Preliminary Work 2 28
29 Offline Computation Network imperfection model Define total induced delay to the control system estimation as consecutive message losses sensing sampling period = network + n loss ssp csp csp n loss ~ dr Estimate for each network placement Control sampling period Optimal network placement Given LSR, placement with minimum estimation Optimal network placement table indexed by LSR values Preliminary Work 2 29
30 Online Reconfiguration Remote Controller Network LSR estimation Estimated LSR Estimated LSR LSR Estimate node placement 0.8 Placement Placement Placement 20 Optimal estimated placement Online reconfiguration algorithm New node placement Preliminary Work 2 30
31 Online Reconfiguration Remote Controller Network LSR estimation Estimated LSR Estimated LSR LSR Estimate node placement 0.8 Placement Placement Placement 20 Optimal estimated placement Online reconfiguration algorithm New node placement Preliminary Work 2 31
32 Online Reconfiguration LSR Estimation During LSR interval (LSRI), each node will record its own average LSR over all its receiving links Every LSRI, each node sends out its own LSR. Parent node will average all its children s LSRs and its own LSR. RC estimates average LSR over all the links. LSRI = 20s T = [0s, 19s] 3 LSR 3 T = 20s 3 LSR 3 = (LSR 1 + LSR 2 + LSR 3 )/3 1 2 LSR 1 LSR LSR 1 LSR 2 Preliminary Work 2 32
33 Online Reconfiguration Centralized Reconfiguration algorithms 1. Direct Jump to Optimal (DO) 2. Multiplicative Increase Conservative Decrease (MICD) 3. Adaptive Control (AC) # nodes # nodes # nodes estimate current t 1 t 2 t 3 t 4 time t 1 t 2 t 3 t 4 time t 1 t 2 t 3 t 4 time DO MICD AC Preliminary Work 2 33
34 Online Reconfiguration Centralized Reconfiguration algorithms 1. Direct Jump to Optimal (DO) 2. Multiplicative Increase Conservative Decrease (MICD) 3. Adaptive Control (AC) # nodes # nodes # nodes current estimate t 1 t 2 t 3 t 4 time t 1 t 2 t 3 t 4 time t 1 t 2 t 3 t 4 time DO MICD AC Considering consecutive losses (CL) Add k more nodes, whenever there are m consecutive losses CL-DO, CL-MICD and CL-AC Preliminary Work 2 34
35 Evaluation Case study: one PHX Simulator: WCPS [Li ICCPS 15] Offline simulation Static RSSI Online simulation Dynamic RSSI: dynamic LSR over time LSRI Metrics RMS error (RMSE): network-induced error (comparing with wired control system) Network lifetime (days) Preliminary Work 2 35
36 Offline Table Number of optimal nodes increases, as the LSR decreases Preliminary Work 2 36
37 Network Imperfection Model vs. Offline Simulation Results Network imperfection model is accurate Network induced delay is statistically correlated with the power output RMSE (Pearson correlation r = 0.993, p < 0.001) Preliminary Work 2 37
38 Network lifetime (days) Power output RMSE (MW) Online Results: sensitivity analysis of LSRI LSRI static30 DO AC MICD CL-DO CL-AC CL-MICD static30 DO AC LSRI Best static scheme performs worse than the dynamic schemes MICD CL-DO CL-AC CL-MICD Preliminary Work 2 38
39 Network lifetime (days) Power output RMSE (MW) Online Results: sensitivity analysis of LSRI LSRI static30 DO AC MICD CL-DO CL-AC CL-MICD static30 DO AC LSRI MICD CL-DO CL-AC CL-MICD Best static scheme performs worse than the dynamic schemes LSRI value affects the performance of schemes without considering CL Preliminary Work 2 39
40 Network lifetime (days) Power output RMSE (MW) Online Results: sensitivity analysis of LSRI LSRI static30 DO AC MICD CL-DO CL-AC CL-MICD static30 DO AC LSRI MICD CL-DO CL-AC CL-MICD Best static scheme performs worse than the dynamic schemes LSRI value affects the performance of schemes without considering CL Schemes considering CL are not affected by the LSRI values Preliminary Work 2 40
41 41 Network Reconfiguration: Space-correlated Faults (Future) P2: how do we reduce network-induced error for a single control system?
42 Motivation and Problem Statement Spatial link failures caused by Interference Sources (IS) affect the network reliability [Low CIMCA 05, Fadel CC 15] -> control system performance Mobile phone, WiFi, radio jammer A Mobile IS has not been fully researched in WSN. Objective: network-induced error reduction for a single control system Proposed Work 1 42
43 Methodology Build a space-correlated fault model with one moving IS With a certain speed Determines which links fail with what probability Study strategies to tolerate space-correlated link failures Distributed network reconfiguration algorithm Conduct a case study in NPP with a single PHX Compare network reconfiguration strategies with baseline of the second prelim work Proposed Work 1 43
44 44 Real-time Network Flow Scheduling (Future) P3: how do we reduce total network-induced error for multiple control systems?
45 Motivation: Observations Test the network-induced error on one PHX Different reference functions with one ramp power change amount (PCA) power change duration (PCD) Different delivery ratio and delay Ramp30 PCA: 10 MW PCD: 30s Proposed Work 2 45
46 Power output RMSE Power output RMSE Motivation: Observations RMSEs are similar PCD (s) delay=0.1s delay=0.2s delay=0.3s delay=0.4s delay=0.5s For reference functions with shorter PCDs, the network delay becomes a more significant factor. PCA: 10 MW; DR: PCA (MW) PCD: 30s; DR: 0.9 RMSEs are similar delay=0.1s delay=0.2s delay=0.3s delay=0.4s delay=0.5s For reference functions with higher PCAs, the network delay becomes more significant factor. Proposed Work 2 46
47 Motivation: NPP demands Multiple Small Modular Reactors (SMRs) in an NPP Different PHX may have different power demands Dynamic application demands -> different reference functions over time Cross-layer real-time scheduling Inject the application demands into the network layer to change measurement deadlines dynamically. Assign smaller deadlines for more urgent application demands (smaller PCDs or larger PCAs) Proposed Work 2 47
48 Required Power Problem Statement Network flow A set of m end-to-end network flows F = F 1, F 2,, F m F i associates with one source s i, a destination d i, a period p i, and a deadline, D i Control systems application demands Control systems have different reference functions with multiple ramps ref1 ref2 t 0 t 1 t 2 t 3 t 4 time Objective: reduce total network-induced errors for multiple control systems: error = n i=1 RMSE i t 5 Proposed Work 2 48
49 Methodology Define the deadline for each network flow, according to the offline control system analysis Related to PCA and PCD Study a cross-layer real-time scheduling algorithm to schedule network flows dynamically. Conduct a case study in an NPP with three PHXs and evaluate the results on WCPS Proposed Work 2 49
50 50 Summary and Timeline
51 Summary Challenges Problems Solutions Instability Performance Degradation stability guarantee Network-induced error reduction for a single control system Network-induced error reduction for multiple control systems Fault-tolerance Network Design Network reconfiguration: time-correlated faults Network reconfiguration: space-correlated faults Real-time network flow scheduling Summary and Timeline 51
52 Timeline Date Content Deliverable results May. - Aug Sep. Dec Jan. Feb March April Dynamic network flow scheduling algorithm design and implementation on WCPS Measure the performance of WCS with dynamic network flow scheduling Finish the implementation of bitvector protocol [Wang ICESS 15] and spacecorrelated fault model on WCPS Come up with a network reconfiguration algorithm and implement it on WCPS Measure the performance of WCS with network reconfiguration mechanism Network deadline formulation and a WCS with the function of dynamic network flow scheduling A paper for publication A WCS with a fault-tolerance protocol to deal with space correlated link failures A WCS with the function of network reconfiguration for space-correlated link failures A paper for publication May. Jun Thesis writing Thesis ready for defense Jul. Aug Thesis revising Completed thesis Summary and Timeline 52
53 Energy-aware Fault Tolerance and Real-time Wireless Sensor Network for Control System Challenges Problems Solutions Instability Performance Degradation stability guarantee Network-induced error reduction for a single control system Network-induced error reduction for multiple control systems Fault-tolerance Network Design Network reconfiguration: timecorrelated faults Network reconfiguration: space-correlated faults Real-time network flow scheduling
54 54 Backup Slides
55 Contributions and Impact A computation model to satisfy control system stability with minimum energy consumption A network reconfiguration framework to address time-correlated and space-correlated link failures in wireless control system Exploration of cross-layer network flow scheduling to enhance overall performance of multiple control systems Summary and timeline 55
56 Comparison: Computation vs. Simulation 56
57 Offline Computation Network imperfection model Transform network delay and message losses to total network induced delay T used T sensed, define total network induced delay as consecutive message losses sensing sampling period Remote controller = network + n loss ssp 0 = 0.2 M 0 csp 1 = = = = = 0.2 M 1 M 1 M 4 M 5 M 1 csp Control sampling period Each network configuration corresponds to different estimation for different LSR values Sensors M 0 M 1 M 2 M 3 M 4 M 5 57
58 Offline Computation Network imperfection model Transform network delay and message losses to total network induced delay T used T sensed, define total network induced delay as consecutive message losses sensing sampling period = network + n loss ssp csp csp Control sampling period Each network configuration corresponds to different estimation for different LSR values n n loss = i 1 dr i ( 1 dr i t) i=0 58
59 Network lifetime (days) Power output RMSE (MW) Online Results: sensitivity analysis of LSRI LSRI LSRI static30 DO AC MICD CL-DO CL-AC CL-MICD static30 DO AC MICD CL-DO CL-AC CL-MICD Static scheme is worse than the dynamic schemes LSRI will affect the performance of schemes without considering CL Schemes considering CL are not affected by the LSRI values 59
60 Power output RMSE (MW) Sensitivity analysis of α values α values AC CL-AC 60
61 Online Results: AC vs CL-AC (LSRI=2s) CL-* schemes add more nodes in the network, when there are consecutive losses 61
62 Interference Source Examples Interference Source An operator walks around with a mobile phone [Baccour TOSN 12] A mobile robot connected with WiFi [Lin RTSS 09] A mobile radio jammer [Wei FGCS 16] Interference example: office building [Lin RTSS 09], >20% difference PDR: packet reception ratio Proposed work 1 62
63 Interference Source Examples Microwave interference on IEEE [Guo 12 TIM] PER: packet error ratio, 1-PDR Proposed work 1 63
64 Distributed Network Reconfiguration Algorithm Primary node in each level will decide how many nodes to be activated or deactivated Compare with centralized algorithms in prelim work2 More Accuracy Reconfiguration according to average LSR estimation is not enough; Local information will improve space-correlated faults detection and tolerance. Low overhead: save network bandwidth No need to send LSR estimation to the remote controller periodically No need to broadcast new configuration to all the nodes in the network Proposed work 1 64
65 Power output RMSE Motivation: observations Delivery ratio PCD: 30s; PCA: 10MW delay=0.1s delay=0.2s delay=0.3s delay=0.4s delay=0.5s Delay has more significant effect on the control system performance Set a deadline according to the application demands Small deadline for reference functions with less PCD or more aggressive PCA Cross-layer dynamic schedule the network flows 65
Dynamic Wireless Network Reconfiguration for Control System: A nuclear reactor case study
Dynamic Wireless Network Reconfiguration for Control System: A nuclear reactor case study Wenchen Wang, Daniel Mosse Computer Science Department University of Pittsburgh Daniel Cole, Jason G. Pickel Mechanical
More informationStudy of RPL DODAG Version Attacks
Study of RPL DODAG Version Attacks Anthéa Mayzaud anthea.mayzaud@inria.fr Rémi Badonnel Isabelle Chrisment Anuj Sehgal s.anuj@jacobs-university.de Jürgen Schönwälder IFIP AIMS 2014 Brno, Czech Republik
More informationIntra and Inter Cluster Synchronization Scheme for Cluster Based Sensor Network
Intra and Inter Cluster Synchronization Scheme for Cluster Based Sensor Network V. Shunmuga Sundari 1, N. Mymoon Zuviria 2 1 Student, 2 Asisstant Professor, Computer Science and Engineering, National College
More informationLecture 8 Wireless Sensor Networks: Overview
Lecture 8 Wireless Sensor Networks: Overview Reading: Wireless Sensor Networks, in Ad Hoc Wireless Networks: Architectures and Protocols, Chapter 12, sections 12.1-12.2. I. Akyildiz, W. Su, Y. Sankarasubramaniam
More informationSelf-Adapting MAC Layer for Wireless Sensor Networks
Self-Adapting MAC Layer for Wireless Sensor Networks Mo Sha, Rahav Dor, Gregory Hackmann, Chenyang Lu Cyber-Physical Systems Laboratory Washington University in St. Louis Tae-Suk Kim, Taerim Park Samsung
More informationFault-Aware Flow Control and Multi-path Routing in Wireless Sensor Networks
Fault-Aware Flow Control and Multi-path Routing in Wireless Sensor Networks X. Zhang, X. Dong Shanghai Jiaotong University J. Wu, X. Li Temple University, University of North Carolina N. Xiong Colorado
More informationLuca Schenato Workshop on cooperative multi agent systems Pisa, 6/12/2007
Distributed consensus protocols for clock synchronization in sensor networks Luca Schenato Workshop on cooperative multi agent systems Pisa, 6/12/2007 Outline Motivations Intro to consensus algorithms
More informationWireless Networks Research Seminar April 22nd 2013
Wireless Networks Research Seminar April 22nd 2013 Distributed Transmit Power Minimization in Wireless Sensor Networks via Cross-Layer Optimization NETS2020 Markus Leinonen, Juha Karjalainen, Marian Codreanu,
More informationWireless Sensor Networks: Clustering, Routing, Localization, Time Synchronization
Wireless Sensor Networks: Clustering, Routing, Localization, Time Synchronization Maurizio Bocca, M.Sc. Control Engineering Research Group Automation and Systems Technology Department maurizio.bocca@tkk.fi
More informationIntra-car Wireless Sensors Data Collection: A Multi-hop Approach
Intra-car Wireless Sensors Data Collection: A Multi-hop Approach Morteza Hashemi, Wei Si, Moshe Laifenfeld, David Starobinski and Ari Trachtenberg Dept. of Electrical and Computer Engineering, Boston University,
More informationComparison of TDMA based Routing Protocols for Wireless Sensor Networks-A Survey
Comparison of TDMA based Routing Protocols for Wireless Sensor Networks-A Survey S. Rajesh, Dr. A.N. Jayanthi, J.Mala, K.Senthamarai Sri Ramakrishna Institute of Technology, Coimbatore ABSTRACT One of
More informationBridging Link Power Asymmetry in Mobile Whitespace Networks Sanjib Sur and Xinyu Zhang
Bridging Link Power Asymmetry in Mobile Whitespace Networks Sanjib Sur and Xinyu Zhang University of Wisconsin - Madison 1 Wireless Access in Vehicles Wireless network in public vehicles use existing infrastructure
More informationIntra-car Wireless Sensors Data Collection: A Multi-hop Approach
Intra-car Wireless Sensors Data Collection: A Multi-hop Approach Morteza Hashemi, Wei Si, Moshe Laifenfeld, David obinski and Ari Trachtenberg Dept. of Electrical and Computer Engineering, Boston University,
More informationModeling Wireless Sensor Network for forest temperature and relative humidity monitoring in Usambara mountain - A review
Modeling Wireless Sensor Network for forest temperature and relative humidity monitoring in Usambara mountain - A review R. Sinde Nelson Mandela African Institution of Science and Technology School of
More informationWireless Sensor Actuator Networks
Wireless Sensor Actuator Networks Feedback Channel implemented over a mul;- hop wireless comm. network with stochas;c guarantees on message delivery Examples are found in controlling spatially distributed
More informationReservation Packet Medium Access Control for Wireless Sensor Networks
Reservation Packet Medium Access Control for Wireless Sensor Networks Hengguang Li and Paul D Mitchell Abstract - This paper introduces the Reservation Packet Medium Access Control (RP-MAC) protocol for
More informationFault Tolerant, Energy Saving Method for Reliable Information Propagation in Sensor Network
Fault Tolerant, Energy Saving Method for Reliable Information Propagation in Sensor Network P.S Patheja, Akhilesh Waoo & Parul Shrivastava Dept.of Computer Science and Engineering, B.I.S.T, Anand Nagar,
More informationEnergy Conservation through Sleep Scheduling in Wireless Sensor Network 1. Sneha M. Patil, Archana B. Kanwade 2
Energy Conservation through Sleep Scheduling in Wireless Sensor Network 1. Sneha M. Patil, Archana B. Kanwade 2 1 Student Department of Electronics & Telecommunication, SITS, Savitribai Phule Pune University,
More informationMAC Layer Performance of ITS G5
MAC Layer Performance of ITS G5 Optimized DCC and Advanced Transmitter Coordination 4 th ETSI TC ITS Workshop Doha, February 7-9, 2012 Marc Werner, Radu Lupoaie, Sundar Subramanian, Jubin Jose mwerner@qualcomm.com
More informationGenetic-Algorithm-Based Construction of Load-Balanced CDSs in Wireless Sensor Networks
Genetic-Algorithm-Based Construction of Load-Balanced CDSs in Wireless Sensor Networks Jing He, Shouling Ji, Mingyuan Yan, Yi Pan, and Yingshu Li Department of Computer Science Georgia State University,
More informationReliable Routing Algorithm on Wireless Sensor Network
International Journal of Engineering & Computer Science IJECS-IJENS Vol:12 No:06 26 Reliable Routing Algorithm on Wireless Sensor Network Jun-jun Liang 1, Zhen-Wu Yuna 1, Jian-Jun Lei 1 and Gu-In Kwon
More informationOn Exploiting Transient Contact Patterns for Data Forwarding in Delay Tolerant Networks
On Exploiting Transient Contact Patterns for Data Forwarding in Delay Tolerant Networks Wei Gao and Guohong Cao Dept. of Computer Science and Engineering Pennsylvania State University Outline Introduction
More information[Telchy, 4(11): November, 2015] ISSN: (I2OR), Publication Impact Factor: 3.785
[Telchy, 4(): November, 5] ISSN: 77-9655 (IOR), Publication Impact Factor:.785 IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY INTELLIGENT OPTIMIZATION AND SCHEDULING OF NETWORKED
More informationImproving the Data Scheduling Efficiency of the IEEE (d) Mesh Network
Improving the Data Scheduling Efficiency of the IEEE 802.16(d) Mesh Network Shie-Yuan Wang Email: shieyuan@csie.nctu.edu.tw Chih-Che Lin Email: jclin@csie.nctu.edu.tw Ku-Han Fang Email: khfang@csie.nctu.edu.tw
More informationZ-SEP: Zonal-Stable Election Protocol for Wireless Sensor Networks
Z-SEP: Zonal-Stable Election Protocol for Wireless Sensor Networks S. Faisal 1, N. Javaid 1, A. Javaid 2, M. A. Khan 1, S. H. Bouk 1, Z. A. Khan 3 1 COMSATS Institute of Information Technology, Islamabad,
More informationReal-Time Internet of Things
Real-Time Internet of Things Chenyang Lu Cyber-Physical Systems Laboratory h7p://www.cse.wustl.edu/~lu/ Internet of Things Ø Convergence of q Miniaturized devices: integrate processor, sensors and radios.
More informationChapter 7 CONCLUSION
97 Chapter 7 CONCLUSION 7.1. Introduction A Mobile Ad-hoc Network (MANET) could be considered as network of mobile nodes which communicate with each other without any fixed infrastructure. The nodes in
More information6th International Workshop on OMNeT++
6th International Workshop on OMNeT++ An OMNeT++ Framework to Evaluate Video Transmission in Mobile Wireless Multimedia Sensor Networks Denis Rosário, Zhongliang Zhao, Claudio Silva, Eduardo Cerqueira,
More informationCROSS LAYER PROTOCOL (APTEEN) USING WSN FOR REAL TIME APPLICATION
CROSS LAYER PROTOCOL (APTEEN) USING WSN FOR REAL TIME APPLICATION V. A. Dahifale 1, N. Y. Siddiqui 2 PG Student, College of Engineering Kopargaon, Maharashtra, India 1 Assistant Professor, College of Engineering
More informationWireless Sensor Networks, energy efficiency and path recovery
Wireless Sensor Networks, energy efficiency and path recovery PhD dissertation Anne-Lena Kampen Trondheim 18 th of May 2017 Outline Introduction to Wireless Sensor Networks WSN Challenges investigated
More informationSubject: Adhoc Networks
ISSUES IN AD HOC WIRELESS NETWORKS The major issues that affect the design, deployment, & performance of an ad hoc wireless network system are: Medium Access Scheme. Transport Layer Protocol. Routing.
More informationSMITE: A Stochastic Compressive Data Collection. Sensor Networks
SMITE: A Stochastic Compressive Data Collection Protocol for Mobile Wireless Sensor Networks Longjiang Guo, Raheem Beyah, and Yingshu Li Department of Computer Science, Georgia State University, USA Data
More informationCHAPTER 1 INTRODUCTION
1 CHAPTER 1 INTRODUCTION 1.1 Motivation The presence of uncertainties and disturbances has always been a vital issue in the control of dynamic systems. The classical linear controllers, PI and PID controllers
More informationS-GinMob: Soft-Handoff Solution for Mobile Users in Industrial Environments
S-GinMob: Soft-Handoff Solution for Mobile Users in Industrial Environments Zinon Zinonos and Vasos Vassiliou Networks Research Laboratory Department of Computer Science University of Cyprus Nicosia, Cyprus
More informationAn Iterative Greedy Approach Using Geographical Destination Routing In WSN
ISSN (Online) : 2319-8753 ISSN (Print) : 2347-6710 International Journal of Innovative Research in Science, Engineering and Technology Volume 3, Special Issue 3, March 2014 2014 International Conference
More informationAirTight: A Resilient Wireless Communication Protocol for Mixed- Criticality Systems
AirTight: A Resilient Wireless Communication Protocol for Mixed- Criticality Systems Alan Burns, James Harbin, Leandro Indrusiak, Iain Bate, Robert Davis and David Griffin Real-Time Systems Research Group
More informationOpportunistic Routing Algorithms in Delay Tolerant Networks
Opportunistic Routing Algorithms in Delay Tolerant Networks Eyuphan Bulut Rensselaer Polytechnic Institute Department of Computer Science and Network Science and Technology (NeST) Center PhD Thesis Defense
More informationNodes Energy Conserving Algorithms to prevent Partitioning in Wireless Sensor Networks
IJCSNS International Journal of Computer Science and Network Security, VOL.17 No.9, September 2017 139 Nodes Energy Conserving Algorithms to prevent Partitioning in Wireless Sensor Networks MINA MAHDAVI
More informationS-GinMob: Soft-Handoff Solution for Mobile Users in Industrial Environments
Neapolis University HEPHAESTUS Repository School of Information Sciences http://hephaestus.nup.ac.cy Articles 211 S-GinMob: Soft-Handoff Solution for Mobile Users in Industrial Environments Zinonos, Zinon
More informationData gathering using mobile agents for reducing traffic in dense mobile wireless sensor networks
Mobile Information Systems 9 (23) 295 34 295 DOI.3233/MIS-364 IOS Press Data gathering using mobile agents for reducing traffic in dense mobile wireless sensor networks Keisuke Goto, Yuya Sasaki, Takahiro
More informationSemainaire Objects connectés industriels, M2M, réseaux June 12th, 2014 IoT et Smart Cities: comment passer à l échelle
Semainaire Objects connectés industriels, M2M, réseaux June 12th, 2014 IoT et Smart Cities: comment passer à l échelle Paolo Medagliani (paolo.medagliani@thalesgroup.com) Agenda IRIS and smart cities Overview
More informationIJREAT International Journal of Research in Engineering & Advanced Technology, Volume 1, Issue 2, April-May, 2013 ISSN:
Fast Data Collection with Reduced Interference and Increased Life Time in Wireless Sensor Networks Jayachandran.J 1 and Ramalakshmi.R 2 1 M.Tech Network Engineering, Kalasalingam University, Krishnan koil.
More informationA Low Latency Data Transmission Scheme for Smart Grid Condition Monitoring Applications 28/05/2012
1 A Low Latency Data Transmission Scheme for Smart Grid Condition Monitoring Applications I R F A N S. A L - A N B A G I, M E L I K E E R O L - K A N T A R C I, H U S S E I N T. M O U F T A H U N I V E
More informationTime Slot Assignment Algorithms for Reducing Upstream Latency in IEEE j Networks
Time Slot Assignment Algorithms for Reducing Upstream Latency in IEEE 802.16j Networks Shimpei Tanaka Graduate School of Information Science and Technology Osaka University, Japan sinpei-t@ist.osaka-u.ac.jp
More information2 Related Work. 1 Introduction. 3 Background
Modeling the Performance of A Wireless Node in Multihop Ad-Hoc Networks Ping Ding, JoAnne Holliday, Aslihan Celik {pding, jholliday, acelik}@scu.edu Santa Clara University Abstract: In this paper, we model
More informationADVANCES in NATURAL and APPLIED SCIENCES
ADVANCES in NATURAL and APPLIED SCIENCES ISSN: 1995-0772 Published BY AENSI Publication EISSN: 1998-1090 http://www.aensiweb.com/anas 2016 April 10(4): pages 239-244 Open Access Journal Design of Gain
More informationKeywords: Medium access control, network coding, routing, throughput, transmission rate. I. INTRODUCTION
Performance Analysis of Network Parameters, Throughput Optimization Using Joint Routing, XOR Routing and Medium Access Control in Wireless Multihop Network 1 Dr. Anuradha M. S., 2 Ms. Anjali kulkarni 1
More informationTroubleshooting Transparent Bridging Environments
Troubleshooting Transparent Bridging Environments Document ID: 10543 This information from the Internetwork Troubleshooting Guide was first posted on CCO here. As a service to our customers, selected chapters
More informationNetwork-Aware Resource Allocation in Distributed Clouds
Dissertation Research Summary Thesis Advisor: Asst. Prof. Dr. Tolga Ovatman Istanbul Technical University Department of Computer Engineering E-mail: aralat@itu.edu.tr April 4, 2016 Short Bio Research and
More information2. LITERATURE REVIEW. Performance Evaluation of Ad Hoc Networking Protocol with QoS (Quality of Service)
2. LITERATURE REVIEW I have surveyed many of the papers for the current work carried out by most of the researchers. The abstract, methodology, parameters focused for performance evaluation of Ad-hoc routing
More informationMedium Access Control (MAC) Protocols for Ad hoc Wireless Networks -IV
Medium Access Control (MAC) Protocols for Ad hoc Wireless Networks -IV CS: 647 Advanced Topics in Wireless Networks Drs. Baruch Awerbuch & Amitabh Mishra Department of Computer Science Johns Hopkins University
More informationQuantitative Analysis and Evaluation of RPL with Various Objective Functions for 6LoWPAN
Indian Journal of Science and Technology, Vol 8(19), DOI: 10.17485/ijst/2015/v8i19/76696, August 2015 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 Quantitative Analysis and Evaluation of RPL with
More informationEnhanced Power Saving Scheme for IEEE DCF Based Wireless Networks
Enhanced Power Saving Scheme for IEEE 802.11 DCF Based Wireless Networks Jong-Mu Choi, Young-Bae Ko, and Jai-Hoon Kim Graduate School of Information and Communication Ajou University, Republic of Korea
More informationPart I. Wireless Communication
1 Part I. Wireless Communication 1.5 Topologies of cellular and ad-hoc networks 2 Introduction Cellular telephony has forever changed the way people communicate with one another. Cellular networks enable
More informationA CLASSIFICATION FRAMEWORK FOR SCHEDULING ALGORITHMS IN WIRELESS MESH NETWORKS Lav Upadhyay 1, Himanshu Nagar 2, Dharmveer Singh Rajpoot 3
A CLASSIFICATION FRAMEWORK FOR SCHEDULING ALGORITHMS IN WIRELESS MESH NETWORKS Lav Upadhyay 1, Himanshu Nagar 2, Dharmveer Singh Rajpoot 3 1,2,3 Department of Computer Science Engineering Jaypee Institute
More informationEnergy Management Issue in Ad Hoc Networks
Wireless Ad Hoc and Sensor Networks - Energy Management Outline Energy Management Issue in ad hoc networks WS 2010/2011 Main Reasons for Energy Management in ad hoc networks Classification of Energy Management
More informationEnergy Efficient Data Gathering For Throughput Maximization with Multicast Protocol In Wireless Sensor Networks
Energy Efficient Data Gathering For Throughput Maximization with Multicast Protocol In Wireless Sensor Networks S. Gokilarani 1, P. B. Pankajavalli 2 1 Research Scholar, Kongu Arts and Science College,
More informationDelay Performance of Multi-hop Wireless Sensor Networks With Mobile Sinks
Delay Performance of Multi-hop Wireless Sensor Networks With Mobile Sinks Aswathy M.V & Sreekantha Kumar V.P CSE Dept, Anna University, KCG College of Technology, Karappakkam,Chennai E-mail : aswathy.mv1@gmail.com,
More informationEnd-To-End Delay Optimization in Wireless Sensor Network (WSN)
Shweta K. Kanhere 1, Mahesh Goudar 2, Vijay M. Wadhai 3 1,2 Dept. of Electronics Engineering Maharashtra Academy of Engineering, Alandi (D), Pune, India 3 MITCOE Pune, India E-mail: shweta.kanhere@gmail.com,
More informationEffects of Sensor Nodes Mobility on Routing Energy Consumption Level and Performance of Wireless Sensor Networks
Effects of Sensor Nodes Mobility on Routing Energy Consumption Level and Performance of Wireless Sensor Networks Mina Malekzadeh Golestan University Zohre Fereidooni Golestan University M.H. Shahrokh Abadi
More informationAn Enhanced General Self-Organized Tree-Based Energy- Balance Routing Protocol (EGSTEB) for Wireless Sensor Network
www.ijecs.in International Journal Of Engineering And Computer Science ISSN: 239-7242 Volume 4 Issue 8 Aug 205, Page No. 3640-3643 An Enhanced General Self-Organized Tree-Based Energy- Balance Routing
More informationContents The Definition of a Fieldbus An Introduction to Industrial Systems Communications.
Contents Page List of Tables. List of Figures. List of Symbols. Dedication. Acknowledgment. Abstract. x xi xv xxi xxi xxii Chapter 1 Introduction to FieldBuses Systems. 1 1.1. The Definition of a Fieldbus.
More informationEnergy Management Issue in Ad Hoc Networks
Wireless Ad Hoc and Sensor Networks (Energy Management) Outline Energy Management Issue in ad hoc networks WS 2009/2010 Main Reasons for Energy Management in ad hoc networks Classification of Energy Management
More informationLiRa: a WLAN architecture for Visible Light Communication with a Wi-Fi uplink
LiRa: a WLAN architecture for Visible Light Communication with a Wi-Fi uplink Sharan Naribole, Shuqing Chen, Ethan Heng and Edward Knightly Naribole Visible Light Communication System (VLC) Dual-purposing
More informationModulation-Aware Energy Balancing in Hierarchical Wireless Sensor Networks 1
Modulation-Aware Energy Balancing in Hierarchical Wireless Sensor Networks 1 Maryam Soltan, Inkwon Hwang, Massoud Pedram Dept. of Electrical Engineering University of Southern California Los Angeles, CA
More informationPower Aware Metrics for Wireless Sensor Networks
Power Aware Metrics for Wireless Sensor Networks Ayad Salhieh Department of ECE Wayne State University Detroit, MI 48202 ai4874@wayne.edu Loren Schwiebert Department of Computer Science Wayne State University
More informationChapter 5 Ad Hoc Wireless Network. Jang Ping Sheu
Chapter 5 Ad Hoc Wireless Network Jang Ping Sheu Introduction Ad Hoc Network is a multi-hop relaying network ALOHAnet developed in 1970 Ethernet developed in 1980 In 1994, Bluetooth proposed by Ericsson
More informationDYNAMIC SEARCH TECHNIQUE USED FOR IMPROVING PASSIVE SOURCE ROUTING PROTOCOL IN MANET
DYNAMIC SEARCH TECHNIQUE USED FOR IMPROVING PASSIVE SOURCE ROUTING PROTOCOL IN MANET S. J. Sultanuddin 1 and Mohammed Ali Hussain 2 1 Department of Computer Science Engineering, Sathyabama University,
More informationMAC Essentials for Wireless Sensor Networks
MAC Essentials for Wireless Sensor Networks Abdelmalik Bachir, Mischa Dohler, Senior Member, IEEE, Thomas Watteyne, Member, IEEE, and Kin K. Leung, Fellow, IEEE Medium access control Part of the link layer
More informationROUTING ALGORITHMS Part 2: Data centric and hierarchical protocols
ROUTING ALGORITHMS Part 2: Data centric and hierarchical protocols 1 Negative Reinforcement Time out Explicitly degrade the path by re-sending interest with lower data rate. Source Gradient New Data Path
More informationQADR with Energy Consumption for DIA in Cloud
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 4, April 2014,
More informationVoice Over Sensor Networks
Voice Over Sensor Networks Rahul Mangharam 1 Anthony Rowe 1 Raj Rajkumar 1 Ryohei Suzuki 2 1 Dept. of Electrical & Computer Engineering 2 Ubiquitous Networking Lab Carnegie Mellon University, U.S.A. {rahulm,agr,raj}@ece.cmu.edu
More informationDeepti Jaglan. Keywords - WSN, Criticalities, Issues, Architecture, Communication.
Volume 5, Issue 8, August 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A Study on Cooperative
More informationField data requirements for the validation of PV module performance models
Field data requirements for the validation of PV module performance models Dipl phys. Gabi Friesen 4th PV Performance Modelling and Monitoring Workshop OUTLINE Introduction Test issues and requirements
More informationWireless Internet Routing. Learning from Deployments Link Metrics
Wireless Internet Routing Learning from Deployments Link Metrics 1 Learning From Deployments Early worked focused traditional routing issues o Control plane: topology management, neighbor discovery o Data
More informationMulti Hop Send Protocol Tool for TinyNodes Semesterthesis
Multi Hop Send Protocol Tool for TinyNodes Semesterthesis Author: Supervisor: Tutor: Remo Niedermann Prof. Dr. Roger Wattenhofer Roland Flury Zurich, February 19, 2009 Acknowledgment At first I want to
More informationIncentive-Aware Routing in DTNs
Incentive-Aware Routing in DTNs Upendra Shevade Han Hee Song Lili Qiu Yin Zhang The University of Texas at Austin IEEE ICNP 2008 October 22, 2008 1 DTNs Disruption tolerant networks No contemporaneous
More informationDPA: A data pattern aware error prevention technique for NAND flash lifetime extension
DPA: A data pattern aware error prevention technique for NAND flash lifetime extension *Jie Guo, *Zhijie Chen, **Danghui Wang, ***Zili Shao, *Yiran Chen *University of Pittsburgh **Northwestern Polytechnical
More informationEnergy Optimized Routing Algorithm in Multi-sink Wireless Sensor Networks
Appl. Math. Inf. Sci. 8, No. 1L, 349-354 (2014) 349 Applied Mathematics & Information Sciences An International Journal http://dx.doi.org/10.12785/amis/081l44 Energy Optimized Routing Algorithm in Multi-sink
More informationCLUSTERING BASED ROUTING FOR DELAY- TOLERANT NETWORKS
http:// CLUSTERING BASED ROUTING FOR DELAY- TOLERANT NETWORKS M.Sengaliappan 1, K.Kumaravel 2, Dr. A.Marimuthu 3 1 Ph.D( Scholar), Govt. Arts College, Coimbatore, Tamil Nadu, India 2 Ph.D(Scholar), Govt.,
More informationA REVIEW ON DATA AGGREGATION TECHNIQUES IN WIRELESS SENSOR NETWORKS
A REVIEW ON DATA AGGREGATION TECHNIQUES IN WIRELESS SENSOR NETWORKS Arshpreet Kaur 1, Simarjeet Kaur 2 1 MTech Scholar, 2 Assistant Professor, Department of Computer Science and Engineering Sri Guru Granth
More informationDO YOU UTILIZE WIRELESS TECHNOLOGY?
DO YOU UTILIZE WIRELESS TECHNOLOGY? January 2015 http:///publications/studies/survey_wireless.html Survey Details Topic: Utilization of wireless technologies among German companies Timeframe: Sep 15, 2014
More informationA Survey - Energy Efficient Routing Protocols in MANET
, pp. 163-168 http://dx.doi.org/10.14257/ijfgcn.2016.9.5.16 A Survey - Energy Efficient Routing Protocols in MANET Jyoti Upadhyaya and Nitin Manjhi Department of Computer Science, RGPV University Shriram
More informationDISCOVERING OPTIMUM FORWARDER LIST IN MULTICAST WIRELESS SENSOR NETWORK
DISCOVERING OPTIMUM FORWARDER LIST IN MULTICAST WIRELESS SENSOR NETWORK G.Ratna kumar, Dr.M.Sailaja, Department(E.C.E), JNTU Kakinada,AP, India ratna_kumar43@yahoo.com, sailaja.hece@gmail.com ABSTRACT:
More informationAn Ant-Based Routing Algorithm to Achieve the Lifetime Bound for Target Tracking Sensor Networks
An Ant-Based Routing Algorithm to Achieve the Lifetime Bound for Target Tracking Sensor Networks Peng Zeng Cuanzhi Zang Haibin Yu Shenyang Institute of Automation Chinese Academy of Sciences Target Tracking
More informationResearch on Heterogeneous Communication Network for Power Distribution Automation
3rd International Conference on Material, Mechanical and Manufacturing Engineering (IC3ME 2015) Research on Heterogeneous Communication Network for Power Distribution Automation Qiang YU 1,a*, Hui HUANG
More informationAbstract. 1. Introduction. 2. Theory DOSA Motivation and Overview
Experiences with Implementing a Distributed and Self-Organizing Scheduling Algorithm for Energy-Efficient Data Gathering on a Real-Life Sensor Network Platform Yang Zhang, Supriyo Chatterjea, Paul Havinga
More informationEfficient Dynamic Multilevel Priority Task Scheduling For Wireless Sensor Networks
Efficient Dynamic Multilevel Priority Task Scheduling For Wireless Sensor Networks Mrs.K.Indumathi 1, Mrs. M. Santhi 2 M.E II year, Department of CSE, Sri Subramanya College Of Engineering and Technology,
More informationLink Estimation and Tree Routing
Network Embedded Systems Sensor Networks Link Estimation and Tree Routing 1 Marcus Chang, mchang@cs.jhu.edu Slides: Andreas Terzis Outline Link quality estimation Examples of link metrics Four-Bit Wireless
More informationA Multipath AODV Reliable Data Transmission Routing Algorithm Based on LQI
Sensors & Transducers 2014 by IFSA Publishing, S. L. http://www.sensorsportal.com A Multipath AODV Reliable Data Transmission Routing Algorithm Based on LQI 1 Yongxian SONG, 2 Rongbiao ZHANG and Fuhuan
More informationA Thermal-aware Application specific Routing Algorithm for Network-on-chip Design
A Thermal-aware Application specific Routing Algorithm for Network-on-chip Design Zhi-Liang Qian and Chi-Ying Tsui VLSI Research Laboratory Department of Electronic and Computer Engineering The Hong Kong
More informationUnicast Routing in Mobile Ad Hoc Networks. Dr. Ashikur Rahman CSE 6811: Wireless Ad hoc Networks
Unicast Routing in Mobile Ad Hoc Networks 1 Routing problem 2 Responsibility of a routing protocol Determining an optimal way to find optimal routes Determining a feasible path to a destination based on
More informationThe Pennsylvania State University. The Graduate School. The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering
The Pennsylvania State University The Graduate School The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering DECISION MAKING ON ROUTING AND QUEUE MANAGEMENT WITH NODE INDEPENDENT
More informationAn Energy Efficient and Delay Aware Data Collection Protocol in Heterogeneous Wireless Sensor Networks A Review
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 5, May 2015, pg.934
More informationNetworked Control Systems for Manufacturing: Parameterization, Differentiation, Evaluation, and Application. Ling Wang
Networked Control Systems for Manufacturing: Parameterization, Differentiation, Evaluation, and Application Ling Wang ling.wang2@wayne.edu Outline Introduction Parameterization Differentiation Evaluation
More informationA Review Paper On The Performance Analysis Of LMPC & MPC For Energy Efficient In Underwater Sensor Networks
www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 4 Issue 5 May 2015, Page No. 12171-12175 A Review Paper On The Performance Analysis Of LMPC & MPC For Energy
More informationSleep/Wake Aware Local Monitoring (SLAM)
Sleep/Wake Aware Local Monitoring (SLAM) Issa Khalil, Saurabh Bagchi, Ness Shroff Dependable Computing Systems Lab (DCSL) & Center for Wireless Systems and Applications (CWSA) School of Electrical and
More informationQoS-Enabled Video Streaming in Wireless Sensor Networks
QoS-Enabled Video Streaming in Wireless Sensor Networks S. Guo and T.D.C. Little Department of Electrical and Computer Engineering Boston University, Boston, MA 02215 {guosong, tdcl}@bu.edu MCL Technical
More informationWSN Routing Protocols
WSN Routing Protocols 1 Routing Challenges and Design Issues in WSNs 2 Overview The design of routing protocols in WSNs is influenced by many challenging factors. These factors must be overcome before
More informationEuropean Network on New Sensing Technologies for Air Pollution Control and Environmental Sustainability - EuNetAir COST Action TD1105
European Network on New Sensing Technologies for Air Pollution Control and Environmental Sustainability - EuNetAir COST Action TD1105 A Holistic Approach in the Development and Deployment of WSN-based
More information