Data Collection Techniques Using Multi-Channel Network Coding in Low-Power and Lossy Networks

DATA COLLECTION TECHNIQUES USING MULTI-CHANNEL NETWORK CODING IN LOW-POWER AND LOSSY NETWORKS Mansour Abdulaziz, PhD George Mason University, 2016 Dissertation Director: Dr. Robert Simon The underlying motivation for my dissertation research is to investigate the combined use of wireless network coding with multi-channel communication to perform data collection in low-power and lossy networks (LLNs). Network coding allows eavesdropping on non-source, non-destination wireless nodes in order to recombine overheard data with its own data, which increases the ability of the ultimate destination to recover a high amount of information, even in the case of high bit error rates. Coordinated multi-channel communication has the potential to dramatically increase the throughput for many types of LLN applications. My work seeks to unify network coding and coordinated multi-channel communication. I consider several important classes of LLN applications, including scenarios with static data collection sinks and mobile data collection sinks. This dissertation has three primary contributions to improve communication performance in data collection LLN systems. The first is a protocol called MuCode, which is designed to support single or multiple sinks that support a Convergecast (many-to-one) communication pattern. I describe a synchronized channel switching policy that takes advantage of wireless overhearing to perform network coding operations. I show that optimally solving certain aspects of this problem is quite challenging, and present a set of heuristics for building the delivery mechanism. I have evaluated MuCode against several other schemes and my results show significant performance improvements under a variety of scenarios. The next two contributions use the results of the MuCode approach and extend them into mobile environments. The second contribution is the protocol MuTrans, which is employed in environments that require mobile data collection in a way that is predictable but uncontrollable. I formally analyze the complexity of this problem in terms of how to minimize data collection latency, which shows it is a challenging problem. I present a synchronized dynamic round-robin scheduling policy for uploading data to a mobile collector that is based on assigning a method for load balancing. My evaluation of data aggregation in the presence of packet errors shows that MuTrans can significantly reduce the latency for data collection, thus providing strong support for mobile data collection. Finally, I present the MuCC protocol, which is designed to support data collection when mobile data collectors motions are both controllable and predictable. After designing and evaluating a two-layer architecture, I provide algorithms on cluster head selection, cluster membership assignment, and trajectory planning. My experimental tests indicate that MuCC substantially outperforms similar approaches in hierarchical data collection. Chapter 1: Introduction Network coding is a mathematical technique whereby nodes combine bits from different packets to form new packets instead of relaying the packets of information they receive [1]. Network coding works by treating bit strings as elements in a Galois field and performs finite field operations over different sets of bits. Network coding methods can be used to reduce the number of packet transmissions, in the case of wired networks, or to reduce the impact of transmission errors on wireless systems. Ostovari et al. [2] has shown that by using network coding systems that experience high packet loss rates, such as wireless sensor networks (WSNs), network coding can offer tremendous benefits regarding increased throughput and reliability for successful packet transmissions received by the destination. Another technique to improve performance in wireless systems is to use multiple communication channels. This method allows nodes to transmit packets in different frequencies without interference, as long as the gap between any adjacent frequencies is relatively large. It has also been shown that using multiple communication channels WSNs decreases end-toend communication latency [3] and increases throughput [4]. This improvement is because using a single channel forces nodes to compete with their neighbors prior starting transmission in order to avoid collisions. By using multiple channel neighboring, nodes can simultaneously transmit packets. Despite the advantages offered by both network coding and multi-channel transmission strategies, there has been little work done in the combination of these two techniques for wireless sensor networks. One of the reasons for this lack of attention in combining network coding and multi-channel communication is that transmitting on different frequencies eliminates an essential advantage of network coding in a wireless system: The ability to overhear neighboring transmissions. These eavesdropped packets can then be encoded with other packets to improve reliability by allowing the same information to be sent to the gateway 1 via different paths. One of the core ideas in my dissertation work is to explore how network coding can be used with multiple communication channels in a variety of application scenarios. Over the last few years, WSNs have been central as an enabling technology for a broad range of Internet of Things (IoTs) applications. The IoT can be defined as networking for embedded objects in a way that allows them to communicate, interact or exchange data with other connected objects. IoT systems are growing at an explosive rate. For instance, on December 2013, Gartner 1 stated that “the internet of things installed-base will grow to 26 billion units by 2020. ” Here, WSN nodes are distributed and can monitor environmental conditions [5], precision agriculture [6] ,or cyber-physical systems [7]. A typical scenario in IoT system is to have the sensors periodically report their readings upwards toward a gateway or base station. This style of networking is often called Convergecast [8]. From a broader perspective, WSNs form an important subclass of a rapidly emerging system class called Low-power and Lossy Networks (LLNs). LLNs are embedded systems with limited resources such as processing, power, and memory [9]. LLNs may or may not sense their environment. They are lossy because they use wireless communication that has a high error rate and unpredictable reception. This situation generates a complex, unpredictable, and challenging environment. Due to the broad importance of LLNs in the IoT world, my work concentrates on this system architecture. The primary motivation of my research combines multi-channel with network coding in both tree-based and cluster-based LLN systems, where these systems represent and support Convergecast communication style. As explained above, Convergecast is a form of networking in which source nodes transmit their data to the base station (sink) through multi-hop routing or mobile data collection. My dissertation makes three primary contributions: the MuCode protocol for static Convergecast, the MuTrans protocol for uncontrollable predictable mobile sink supporting Convergecast and MuCC protocol for controllable predictable mobile sink supporting Convergecast. The technology research and advisory corporation