Efficient Data Dissemination and Collection Protocols for Wireless Sensor Networks Manjunath Doddavenkatappa SCHOOL OF COMPUTING NATIONAL UNIVERSITY OF SINGAPORE 2013 Efficient Data Dissemination and Collection Protocols for Wireless Sensor Networks Manjunath Doddavenkatappa A THESIS SUBMITTED FOR THE DEGREE OF PhD IN COMPUTER SCIENCE SCHOOL OF COMPUTING NATIONAL UNIVERSITY OF SINGAPORE 2013 Declaration I hereby declare that this thesis is my original work and it has been written by me in its entirety. I have duly acknowledged all the sources of information which have been used in the thesis. This thesis has also not been submitted for any degree in any university previously. Manjunath Doddavenkatappa 7th October, 2013 Acknowledgements I first and foremost thank my advisor Prof. Chan Mun Choon for his invaluable guidance and all the support that he has rendered throughout my graduate studies. I can recall many occasions during paper deadlines, we working at as late as AM in the morning, which speaks itself for his commitment. I am most grateful to him for his efforts in nurturing my research skills and critical thinking. I am grateful to Prof. Ananda, he was always there for everything. His simplicity makes him special. I thoroughly enjoyed our lunch-time discussions that mainly included a topic of spirituality. I thank the inspiring Prof. Ben Leong for his collaboration on my work. I am most thankful to him for his guidance on improving my writing skills, I can recall he sitting with me and working on it for long hours. Many thanks to many users of Indriya testbed, their warming words kept my motivation levels always high to maintain Indriya. I also thank our own CS 4222 students for their questions and bug reports which have greatly contributed to the stability of Indriya. I am grateful to our wireless meeting group: Prof. Wei Tsang ooi, Prof. Jason Gu, Wang Wei, Guoqing Yu, and James Yong. A special thanks to Ms. Lim Chew Eng from technical services, without her help I would not have had as an easy access as I had to project equipments. Thanks to all my friends in our lab for their friendship and support: Bhojan Anand, Chetan, Grisha, Hweexian, Kartik, Mustafa, Naba, Shao Tao, and Xiangfa. Thanks also to Sudipta for his friendship. I would also like to thank my friends who helped and supported me during my pre- iii university and undergraduate studies: Vilas Reddy Chuda, Srinivas Kapilavai, and Gangadhar Reddy. I never would have been eligible to join a PhD program if I had not met Prasanna Kumar, who was my mentor during my one-year training at the Defense Research and Development Organization of India (DRDO). I am most grateful to him for introducing me to Sai Baba. I also thank S.V Gopalaiah and SVR Anand, who were my mentors when I was working at the Indian Institute of Science (IISc), they have always encouraged me to take challenging tasks. Finally, I dedicate this dissertation to my wife, Namitha. I have no words to describe her support throughout my graduate studies, I just want to thank God for blessing me with such a wonderful person as my life partner. Contents Abstract iv List of Tables vi List of Figures vii List of Publications viii Introduction 1.1 Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Case for Dissemination of Large Data Objects . . . . . . . . . . . . . . 1.3 Case for Collection of Large Data Objects . . . . . . . . . . . . . . . . 1.4 Overview of the Proposed Protocols . . . . . . . . . . . . . . . . . . . 1.4.1 Splash: Fast Data Dissemination . . . . . . . . . . . . . . . . . 1.4.2 ILTP: Transforming Intermediate Quality Links into Good Links 1.4.3 P3 : Practical Packet Pipelining . . . . . . . . . . . . . . . . . . 1.5 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Thesis Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 5 10 . . . . . . . . . . 11 11 13 14 15 16 17 18 19 19 20 Splash: Fast Data Dissemination 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Measurement Study of Constructive Interference . . . . . . . . . . . . . . . . 22 22 26 Related Work and Background 2.1 Constructive Interference . . . . . . . . . . . . . . . . . 2.2 Channel Diversity . . . . . . . . . . . . . . . . . . . . . 2.3 Dissemination Protocols . . . . . . . . . . . . . . . . . 2.4 Bulk Data Collection . . . . . . . . . . . . . . . . . . . 2.4.1 Opportunistic Routing . . . . . . . . . . . . . . 2.4.2 High-Throughput Bulk Data Collection Protocols 2.4.3 Collection Tree Protocol (CTP) . . . . . . . . . 2.5 Methods for Handling Channels-Quality Differences . . 2.6 Correlation among Packet Receptions . . . . . . . . . . 2.7 Practical Testbeds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CONTENTS 3.3 3.4 3.5 3.2.1 Scalability . . . . . . . . . . . . . . . . . 3.2.2 Receiver Correlation . . . . . . . . . . . . Splash Protocol . . . . . . . . . . . . . . . . . . . 3.3.1 Tree Pipelining . . . . . . . . . . . . . . . 3.3.2 Channel Cycling & Channel Assignment . 3.3.3 Exploiting Transmission Density Diversity 3.3.4 XOR Coding . . . . . . . . . . . . . . . . 3.3.5 Local Recovery . . . . . . . . . . . . . . . 3.3.6 Implementation . . . . . . . . . . . . . . . Performance Evaluation of Splash . . . . . . . . . 3.4.1 Experimental Methodology . . . . . . . . 3.4.2 Summary of Testbed Results . . . . . . . . 3.4.3 Contribution of Individual Techniques . . . 3.4.4 Effect of Packet Size . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . ii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ILTP: Transforming Intermediate Quality Links into Good Links 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Measurement Study of Channels on IQ Links . . . . . . . . . . 4.2.1 Collection of Traces . . . . . . . . . . . . . . . . . . . 4.2.2 Correlation among Different Channels . . . . . . . . . . 4.2.3 Rate of Fluctuation of Channel Quality . . . . . . . . . 4.2.4 How Easy is it to Find a Good Channel? . . . . . . . . . 4.3 ILTP Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 ILTP for Bulk Data Collection . . . . . . . . . . . . . . 4.3.2 An Efficient Channel Selection Strategy . . . . . . . . . 4.3.3 Coordinating Channel Switching . . . . . . . . . . . . . 4.3.4 Implementation . . . . . . . . . . . . . . . . . . . . . . 4.4 Performance Evaluation of ILTP . . . . . . . . . . . . . . . . . 4.4.1 Experimental Methodology . . . . . . . . . . . . . . . 4.4.2 Transformation of IQ Links into Good Links using ILTP 4.4.3 Channel Durations . . . . . . . . . . . . . . . . . . . . 4.4.4 Overhead . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.5 Improvement in Routing Performance . . . . . . . . . . 4.4.6 Effect of Packet Rate . . . . . . . . . . . . . . . . . . . 4.4.7 Periodic Traffic over a Duty-Cycling MAC . . . . . . . 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P3 : Practical Packet Pipelining 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . 5.2 Measurement Study of Channels-Quality Differences 5.2.1 Channels-Quality Differences . . . . . . . . 5.2.2 Correlation among Packet Receptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 28 30 31 34 35 36 38 38 41 41 42 46 51 53 . . . . . . . . . . . . . . . . . . . . 54 54 58 58 59 60 60 64 64 65 67 69 74 74 74 75 76 77 79 80 82 . . . . 83 83 87 87 89 CONTENTS 5.3 P3 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 PIP Pipelining . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Practical Packet Pipelining with Constructive Interference 5.3.3 Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.4 Channel Assignment . . . . . . . . . . . . . . . . . . . . 5.3.5 Scalability at the Last Stage . . . . . . . . . . . . . . . . 5.3.6 Fast Retransmissions . . . . . . . . . . . . . . . . . . . . 5.3.7 Implementation . . . . . . . . . . . . . . . . . . . . . . . Performance Evaluation of P3 . . . . . . . . . . . . . . . . . . . 5.4.1 Experimental Methodology . . . . . . . . . . . . . . . . 5.4.2 Summary of Testbed Results . . . . . . . . . . . . . . . . 5.4.3 Effective Utilization . . . . . . . . . . . . . . . . . . . . 5.4.4 Effect of Packet Size . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 92 92 95 96 97 97 98 101 101 102 103 105 107 Conclusion and Future Work 6.1 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 Splash: Fast Data Dissemination . . . . . . . . . . . . . . . . . 6.1.2 ILTP: Transforming Intermediate Quality Links into Good Links 6.1.3 P3 : Practical Packet Pipelining . . . . . . . . . . . . . . . . . . 6.1.4 Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 108 109 109 110 110 111 5.4 5.5 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 Abstract Dissemination and collection of large amounts of data are two fundamental services required in wireless sensor networks. Despite almost a decade of research, existing large data dissemination and collection protocols still take long completion times and consume a significant amount of energy. This is due to the effects of various issues: contention overhead, intra- and interflow interferences, external interference, link asymmetry, varying channel conditions, channelsquality differences, and/or energy-intensive requirements such as packet overhearing. In this work, we effectively handle these issues by exploiting constructive interference and channel diversity, in addition to using various techniques such as exploiting transmission density diversity and node diversity, XOR coding, channel cycling, etc. This leads us to make three important contributions, which constitute this dissertation: (1) We design and implement Splash, a data dissemination protocol that is more than an order of magnitude faster than stateof-the-art dissemination protocols. (2) We propose a new approach that allows data collection protocols to exploit long-range communication links of intermediate quality (IQ) through channel diversity with a new protocol, called ILTP (IQ Link Transformation Protocol), which does not require an energy-intensive operation of packet overhearing. (3) We design and implement P (Practical Packet Pipeline), a high-throughput data collection protocol that on average utilizes 84.2% of the effective data rate of the underlying de facto standard CC2420 radio, whereas average utilization for the state-of-the-art high-throughput protocol is only 16.2%. Splash. It is well-known that the time taken for disseminating a large data object over a wireless sensor network is dominated by the overhead of resolving the contention for the underlying wireless channel. On the other hand, Splash eliminates the need for contention resolution by exploiting constructive interference and channel diversity to effectively create fast and parallel packet pipelines over multiple paths that cover all the nodes in a network. We call this tree pipelining. In order to ensure high reliability, Splash also incorporates several techniques, including exploiting transmission density diversity, opportunistic overhearing, channel cycling, and XOR coding. Our evaluation results on two large-scale testbeds show that Splash is more than an order of magnitude faster than state-of-the-art dissemination protocols and achieves a CONTENTS v reduction in data dissemination time by a factor of more than 20 compared to the most commonly used DelugeT2. ILTP. A large percentage of links in low-power wireless sensor networks are of intermediate quality (IQ). Opportunistic exploitation is currently the only way to exploit longer range offered by these links. However, such exploitation requires packet overhearing which consumes a significant amount of energy. Whereas ILTP takes a novel approach of exploiting IQ links through channel diversity, which does not require packet overhearing. ILTP transforms IQ links into good links thus allowing us to exploit such links continuously rather than using them only opportunistically. Our evaluations on three large-scale testbeds demonstrate that ILTP is able to consistently transform the IQ links into good links. When ILTP is integrated with CTP, the default data collection protocol for sensor networks, the average number of transmissions per end-to-end packet delivery is reduced by 24-58%, without incurring any overhearing energy costs. P3 . While state-of-the-art large data collection protocol (PIP (Packets In Pipeline)) exploits channel diversity to create a fast packet pipeline, it ignores the drastic performance differences that exist among different channels – there exists a high chance that a good link on one channel not even existing on another channel. Such differences significantly degrade throughput by causing pipeline stalls. On the other hand, P keeps its packet pipeline flowing despite substantial quality differences among different channels. In order to so, P exploits node diversity on both senders and receivers through constructive interference. Moreover, unlike existing approaches whose maximum achievable goodput is half of the effective data rate of an underlying radio device, P can achieve a maximum goodput that is equal to the effective data rate. Our evaluation results on a 139-node practical testbed show that P achieves an average goodput of 177.8 Kbps while PIP’s average goodput is only 35.6 Kbps. More importantly, P achieves an average goodput of about 179.3 Kbps in cases where goodput of PIP reduces to zero which happens often in practice. Overall, in this dissertation, we design and implement efficient large data dissemination and collection protocols for wireless sensor networks, which outperform state-of-the-art protocols by a large margin. 5.4 Performance Evaluation of P3 106 effect of this tradeoff on P 3’s goodput, we execute P configured with different payload sizes on a randomly chosen route on Indriya with its source and destination nodes locating in different floors. For each considered payload size, we execute runs. Fig 5.6 plots P 3’s goodput in each of those runs for all considered sizes. As we can see, goodput is maximum for the maximum-sized payload of 118 bytes as gain that is rendered by a larger packet outweighs the loss that it incurs due to corruption under constructive interference. 5.5 Summary 107 5.5 Summary Due to drastic performance differences that exist among different channels, performance of the state-of-the-art approach of PIP that exploits channel diversity to create a high-throughput packet pipeline is often poor in practice. In order to tackle this problem, we have proposed P , a practical packet pipelining protocol that keeps its packet pipeline flowing despite substantial quality differences among channels. Our key technique is to account for such quality differences by exploiting both receiver and sender diversities through constructive interference. Moreover, unlike existing approaches whose maximum achievable goodput is half of the effective data rate of an underlying radio, P achieves a maximum goodput that is close to the effective data rate. Our evaluation results on a 139-node testbed show that P achieves an average goodput of 177.8 Kbps while PIP’s average goodput is only 35.6 Kbps. Moreover, we observed P achieving an average goodput of about 179.3 Kbps in cases where goodput of PIP reduces to zero which happens often in practice. Chapter Conclusion and Future Work In this chapter, we summarize our research contributions and provide a discussion on future work. 6.1 Research Contributions Our main research contributions are as follows: (1) We design and implement Splash, a data dissemination protocol that is more than an order of magnitude faster than state-of-the-art dissemination protocols. (2) We propose a new approach to exploit long-range communication links through channel diversity with a new protocol called ILTP, which does not require an energy-intensive operation of packet overhearing. Thus saving a significant amount of energy compared to existing approaches all of which require packet overhearing. (3) We design and implement P 3, a high-throughput data collection protocol that is on average times faster than the state-of-the-art PIP protocol. More importantly, P maintains a high average throughput even in cases where PIP’s throughput reduces to zero which happens often in practice. 6.1 Research Contributions 109 (4) Our measurement study of constructive interference demonstrates that packet receptions across receivers decoding simultaneous transmissions are not correlated on all ZigBee channels. (5) Using our experiments in both outdoor and indoor settings, we show that reception qualities of different channels on IQ links are not correlated and some of the channels change in their quality on a time scale of minutes. 6.1.1 Splash: Fast Data Dissemination Splash is a fast and scalable dissemination protocol for wireless sensor networks, that exploits constructive interference and channel diversity to achieve high speed and scalability. To achieve high reliability, Splash incorporates the use of transmission density diversity, opportunistic overhearing, channel-cycling, and XOR coding. We evaluated Splash on two large multihop sensor networks. Our results show that Splash is able to disseminate a 32-kilobyte data object in about 25 seconds on both the testbeds. Compared to DelugeT2, Splash reduces dissemination time on average by a factor of 21, and in the best case, by up to a factor of 57.8. This is significantly better than MT-Deluge [16], the best state-of-the-art dissemination protocol, which achieves a reduction factor of only 2.42 compared to Deluge. 6.1.2 ILTP: Transforming Intermediate Quality Links into Good Links ILTP enables data collection protocols to exploit long-range IQ links without having to engage in an energy-intensive operation of packet overhearing. Moreover, unlike in existing approaches, ILTP allows to exploit such links continuously rather than using them only opportunistically. In order to so, ILTP transforms IQ links into good links by exploiting channel diversity. Our key insight is that the PRR across different channels on IQ links are not correlated 6.1 Research Contributions 110 and it is common on such links to find channels that change in their quality on a time scale of minutes. Consequently, when the link quality of a channel is bad, it is highly likely that a good channel can be found and its quality will remain good for at least a few minutes. We evaluated ILTP and its integration with CTP on three large-scale testbeds and show that ILTP is able to consistently transform IQ links into good links. We observe that even a poor link with a PRR 0.05 can be transformed into a good link with a PRR greater than 0.9. With ILTP integrated, the average number of transmissions per end-to-end packet delivery is reduced by 24-58%, without incurring any overhearing energy costs. 6.1.3 P3 : Practical Packet Pipelining P is high-throughput data collection protocol that keeps its packet pipeline flowing despite substantial quality differences among different channels. It does so by exploiting node diversity on both senders and receivers through constructive interference. Moreover, unlike existing approaches whose maximum achievable goodput is half of the effective data rate of an underlying radio device, P can achieve a maximum goodput that is equal to the effective data rate. Our evaluation results on Indriya testbed show that P achieves an end-to-end average goodput of 177.8 Kbps while state-of-the-art PIP’s average goodput is only 35.6 Kbps. This times improvement is achieved despite of the fact that we reimplemented PIP and our reimplementation is 57% faster than its original implementation. More interestingly, P maintains an average goodput of 179.3 Kbps in cases where goodput of PIP reduces to zero which happens often in practice. Overall, average end-to-end utilization of P is 84.2% of the effective data rate of the underlying CC2420 radio while PIP’s average utilization is only 16.2%. 6.1.4 Correlation We empirically show that packet receptions under constructive interference are not correlated on all ZigBee channels, particularly on those ZigBee channels which not overlap with the 6.2 Future Work 111 WiFi channels occupied in a target environment. While we exploit this observation for handling channels-quality differences, it is useful in general in designing communication protocols based on constructive interference. Our measurements also demonstrate that reception qualities of different channels on longrange IQ links are not correlated, and sufficient number of channels on such links tend to change in quality on a time scale of minutes. This means when the link quality of a channel is bad, it is highly likely that a good channel can be found and its quality will remain good for at least a few minutes. 6.2 Future Work Following are some of the possible extensions of our work, and applications of our methods to other scenarios. Reliability of Constructive Interference. It is a challenging task to efficiently ensure a high reception reliability under constructive interference, given that its reliability is affected by several factors such as packet size, number of concurrent transmitters, and channel conditions. While Splash uses smart retransmissions exploiting transmission density diversity, channel cycling, opportunistic overhearing, and XOR coding for its high reliability, not all protocols exploiting constructive interference can incorporate these techniques. A more generic solution is to retransmit every packet for a pre-defined number of times and it is adopted by all other previous protocols based on constructive interference [36, 45]. Clearly, this method is not adaptive to dynamic factors such as channel conditions and number of concurrent transmissions overlapping at a receiver, both of which vary from node to node even within the same network. While this lack of adaptiveness affects all previous protocols [36, 45], it also affects P that also relies on a pre-defined number of retransmissions for conveying control information such as bit-vector messages. We are currently working on an adaptive method to learn which nodes 6.2 Future Work 112 of a hop should engage in concurrent transmissions (forwarding) of a received packet so that the average reception reliability at the nexthop is maximized. This future work will be useful in general to protocols that exploit constructive interference and which rely on a fixed number of packet retransmissions for high reliability. Radio Chips Supporting Constructive Interference. As our work demonstrates the fact that constructive interference can offer significant gains, it is considerably worth designing future radio chips to support synchronous transmissions in hardware. This can be significantly useful for two reasons: (1) Hardware support can improve reception reliability under such transmissions as it can avoid desynchronization caused by the currently required interactions between MCU and radio, by completely eliminating such interactions. (2) Because of GCC compiler optimizations, changes to parts of the current code that supports synchronous transmissions can affect the code’s capability to result in constructive interference, thus making it difficult to build applications over such code. On the other hand, a hardware support is independent of such optimizations. Designing hardware support for constructive interference is feasible as it has already been demonstrated in Backcast [96] that hardware acknowledgement frames supported by CC2420 can result in constructive interference with a reliability of above 97%, when multiple nodes concurrently acknowledge reception of the same packet. Multimedia Applications. As observed in our evaluation studies, P achieves an average collection goodput of 177.8 Kbps and Splash takes only about seconds for disseminating about 97.8% which translates into a dissemination goodput of about 32 Kbps per node. Such high rates mean there is a potential for exploiting constructive interference to realize multimedia wireless sensor networks [97, 98, 99], which in turn benefits applications such as video surveillance as for example, by reducing wiring costs or by enhancing the field of view [100]. Reasons for Channels-Quality Differences. As we demonstrated by experiments, there exists drastic quality differences among different channels in practice. As such differences are 6.2 Future Work 113 true even among channels that are non-interfering with the WiFi channels, we are not sure of the exact causes for the observed differences. Although multipath fading that varies from channel to channel [101] is a potential cause, it is unknown that whether such fading can cause differences which are as substantial as a good link on one channel may not even exists on another channel. It is worth understating the causes as it will be useful in general in designing communication protocols based on channel diversity. Extension to WiFi Networks. A natural extension of our work is to implement them in the WiFi domain. However, a major hurdle is the fact that we are not sure whether off-theshelf WiFi radio chips can be exploited to support constructive interference. 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Mitigating Multipath Fading Through Channel Hopping in Wireless Sensor Networks. In Proceedings of ICC, 2010. [...]... of the three protocols of Splash, ILTP, and P 3 which are designed and implemented in this work 1.4.1 Splash: Fast Data Dissemination A data dissemination protocol, like Deluge [21], is a fundamental service required for the deployment and maintenance of practical wireless sensor networks because of the need to periodically reprogram sensor nodes in the field Existing data dissemination protocols employ... Development of Networks and Communities (TRIDENTCOM), April 2011 Chapter 1 Introduction 1.1 Wireless Sensor Networks Wireless networks of tiny embedded devices commonly known as wireless sensor networks have numerous applications, ranging from monitoring of serene habitats of birds to turbulent volcanos [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] Moreover, a paradigm of Internet of Things makes sensor networks. .. designing efficient communication protocols Typically, communication in sensor networks is a part of either data dissemination or collection Dissemination service involves disseminating large data objects to the entire network and it is required for almost every application of sensor networks Similarly, a service that collects large amount of data from a network is also of similar significance Because of their... Chan, and Ananda A L, “A Dual-Radio Framework for MAC Protocol Implementation in Wireless Sensor Networks, ” In Proceedings of International Conference on Communications (ICC), June 2011 (5) Manjunath Doddavenkatappa, Mun Choon Chan, and Ananda A L, “Indriya: A LowCost, 3D Wireless Sensor Network Testbed,” In Proceedings of 7th International ICST Conference on Testbeds and Research Infrastructures for. .. and Background In this chapter, we provide an overview of the literature and background information that is relevant to our work We mainly cover the following topics: (1) constructive interference; (2) channel diversity; (3) dissemination protocols; (4) bulk data collection with a discussion on opportunistic routing, high-throughput collection, and collection tree protocol (CTP); (5) methods for handling... implementation, and evaluation of Splash, a fast data dissemination protocol for wireless sensor networks For its speed, Splash mainly exploits constructive interference and channel diversity In order to ensure high reliability, Splash uses techniques such as exploiting transmission density diversity, opportunistic overhearing, channel cycling, and XOR coding Compared to existing dissemination protocols, ... dissemination protocols, Splash reduces dissemination time by an order of magnitude, from minutes to seconds 3.1 Introduction A data dissemination protocol, like Deluge [21], is a fundamental service required for the deployment and maintenance of practical wireless sensor networks because of the need to periodically reprogram sensor nodes in the field Existing data dissemination protocols employ either a contention... Trickle ‘[73] and 4-bit link estimator (4BLE) [80] It uses Trickle to reduce the control overhead, and 4BLE is used for estimating the quality of links so that a collection tree with stable and high-quality routes can be formed for reliable data delivery While CTP is originally designed for low data- rate applications without any emphasis on high throughput, however, it is also used as a de facto standard... Doddavenkatappa, Mun Choon Chan, and Ben Leong, “Splash: Fast Data Dissemination with Constructive Interference in Wireless Sensor Networks, ” In Proceedings of 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI), April 2013 (3) Manjunath Doddavenkatappa, Mun Choon Chan, and Ben Leong, “Improving Link Quality by Exploiting Channel Diversity in Wireless Sensor Networks, ” In Proceedings... 18, 19, 20, 21, 22, 23, 24] 1.2 Case for Dissemination of Large Data Objects 2 However, despite such an attention, performance of these existing bulk data dissemination and collection protocols is often poor in practice, taking long completion times and consuming a significant amount of energy This is due to the effects of various issues: contention overhead, intra- and inter-flow interferences, external . Efficient Data Dissemination and Collection Protocols for Wireless Sensor Networks Manjunath Doddavenkatappa SCHOOL OF COMPUTING NATIONAL UNIVERSITY OF SINGAPORE 2013 Efficient Data Dissemination and Collection. this dissertation, we design and implement efficient large data dissemination and collection protocols for wireless sensor networks, which outperform state-of-the-art protocols by a large margin. List. 114 Abstract Dissemination and collection of large amounts of data are two fundamental services required in wireless sensor networks. Despite almost a decade of research, existing large data dissemination and