Just-in-Time Adaptive Algorithm for Optimal Parameter Setting in 802.15.4 WSNs

Recent studies have shown that the IEEE 802.15.4 MAC protocol suffers from severe limitations, in terms of reliability and energy efficiency, when the CSMA/CA parameter setting is not appropriate. However, selecting the optimal setting that guarantees the application reliability requirements, with minimum energy consumption, is not a trivial task in wireless sensor networks, especially when the operating conditions change over time. In this paper we propose a Just-in-Time LEarning-based Adaptive Parameter tuning (JIT-LEAP) algorithm that adapts the CSMA/CA parameter setting to the time-varying operating conditions by also exploiting the past history to find the most appropriate setting for the current conditions. Following the approach of active adaptive algorithms, the adaptation mechanism of JIT-LEAP is triggered by a change detection test only when needed (i.e., in response to a change in the operating conditions). Simulation results show that the proposed algorithm outperforms other similar algorithms, both in stationary and dynamic scenarios

Strategies for Optimal MAC Parameter Setting in IEEE 802.15.4 Wireless Sensor Networks: a Performance Comparison

Recent studies have shown that the IEEE 802.15.4 MAC protocol may suffer from severe limitations in terms of reliability and energy efficiency if a non appropriate parameter setting is used. Hence, a number of solutions have been proposed to select the optimal parameter setting to provide reliability with minimum energy consumption. In this paper we compare, by simulation, three different algorithms that take different approaches to the problem, namely offline computation, model-based adaptation, and measurement-based adaptation. We show that adaptive algorithms perform well, however the model-based adaptive approach has some limitations that make it unsuitable in practical scenarios, where operating conditions may vary over time and transmission errors cannot be neglected. Instead, the measurement-based adaptive approach is flexible and effectiv

On a Joint Physical Layer and Medium Access Control Sublayer Design for Efficient Wireless Sensor Networks and Applications

Wireless sensor networks (WSNs) are distributed networks comprising small sensing devices equipped with a processor, memory, power source, and often with the capability for short range wireless communication. These networks are used in various applications, and have created interest in WSN research and commercial uses, including industrial, scientific, household, military, medical and environmental domains. These initiatives have also been stimulated by the finalisation of the IEEE 802.15.4 standard, which defines the medium access control (MAC) and physical layer (PHY) for low-rate wireless personal area networks (LR-WPAN). Future applications may require large WSNs consisting of huge numbers of inexpensive wireless sensor nodes with limited resources (energy, bandwidth), operating in harsh environmental conditions. WSNs must perform reliably despite novel resource constraints including limited bandwidth, channel errors, and nodes that have limited operating energy. Improving resource utilisation and quality-of-service (QoS), in terms of reliable connectivity and energy efficiency, are major challenges in WSNs. Hence, the development of new WSN applications with severe resource constraints will require innovative solutions to overcome the above issues as well as improving the robustness of network components, and developing sustainable and cost effective implementation models. The main purpose of this research is to investigate methods for improving the performance of WSNs to maintain reliable network connectivity, scalability and energy efficiency. The study focuses on the IEEE 802.15.4 MAC/PHY layers and the carrier sense multiple access with collision avoidance (CSMA/CA) based networks. First, transmission power control (TPC) is investigated in multi and single-hop WSNs using typical hardware platform parameters via simulation and numerical analysis. A novel approach to testing TPC at the physical layer is developed, and results show that contrary to what has been reported from previous studies, in multi-hop networks TPC does not save energy. Next, the network initialization/self-configuration phase is addressed through investigation of the 802.15.4 MAC beacon interval setting and the number of associating nodes, in terms of association delay with the coordinator. The results raise doubt whether that the association energy consumption will outweigh the benefit of duty cycle power management for larger beacon intervals as the number of associating nodes increases. The third main contribution of this thesis is a new cross layer (PHY-MAC) design to improve network energy efficiency, reliability and scalability by minimising packet collisions due to hidden nodes. This is undertaken in response to findings in this thesis on the IEEE 802.15.4 MAC performance in the presence of hidden nodes. Specifically, simulation results show that it is the random backoff exponent that is of paramount importance for resolving collisions and not the number of times the channel is sensed before transmitting. However, the random backoff is ineffective in the presence of hidden nodes. The proposed design uses a new algorithm to increase the sensing coverage area, and therefore greatly reduces the chance of packet collisions due to hidden nodes. Moreover, the design uses a new dynamic transmission power control (TPC) to further reduce energy consumption and interference. The above proposed changes can smoothly coexist with the legacy 802.15.4 CSMA/CA. Finally, an improved two dimensional discrete time Markov chain model is proposed to capture the performance of the slotted 802.15.4 CSMA/CA. This model rectifies minor issues apparent in previous studies. The relationship derived for the successful transmission probability, throughput and average energy consumption, will provide better performance predictions. It will also offer greater insight into the strengths and weaknesses of the MAC operation, and possible enhancement opportunities. Overall, the work presented in this thesis provides several significant insights into WSN performance improvements with both existing protocols and newly designed protocols. Finally, some of the numerous challenges for future research are described

Towards the efficient use of LoRa for wireless sensor networks

Since their inception in 1998 with the Smart Dust Project from University of Berkeley, Wireless Sensor Networks (WSNs) had a tremendous impact on both science and society, influencing many (new) research fields, like Cyber-physical System (CPS), Machine to Machine (M2M), and Internet of Things (IoT). In over two decades, WSN researchers have delivered a wide-range of hardware, communication protocols, operating systems, and applications, to deal with the now classic problems of resourceconstrained devices, limited energy sources, and harsh communication environments. However, WSN research happened mostly on the same kind of hardware. With wireless communication and embedded hardware evolving, there are new opportunities to resolve the long standing issues of scaling, deploying, and maintaining a WSN. To this end, we explore in this work the most recent advances in low-power, longrange wireless communication, and the new challenges these new wireless communication techniques introduce. Specifically, we focus on the most promising such technology: LoRa. LoRa is a novel low-power, long-range communication technology, which promises a single-hop network with millions of sensor nodes. Using practical experiments, we evaluate the unique properties of LoRa, like orthogonal spreading factors, nondestructive concurrent transmissions, and carrier activity detection. Utilising these unique properties, we build a novel TDMA-style multi-hop Medium Access Control (MAC) protocol called LoRaBlink. Based on empirical results, we develop a communication model and simulator called LoRaSim to explore the scalability of a LoRa network. We conclude that, in its current deployment, LoRa cannot support the scale it is envisioned to operate at. One way to improve this scalability issue is Adaptive Data Rate (ADR). We develop two ADR protocols, Probing and Optimistic Probing, and compare them with the de facto standard ADR protocol used in the crowdsourced TTN LoRaWAN network. We demonstrate that our algorithms are much more responsive, energy efficient, and able to reach a more efficient configuration quicker, though reaching a suboptimal configuration for poor links, which is offset by the savings caused by the convergence speed. Overall, this work provides theoretical and empirical proofs that LoRa can tackle some of the long standing problems within WSN. We envision that future work, in particular on ADR and MAC protocols for LoRa and other low-power, long-range communication technologies, will help push these new communication technologies to main-stream status in WSNs

Efficient Information Access in Data-Intensive Sensor Networks

Recent advances in wireless communications and microelectronics have enabled wide deployment of smart sensor networks. Such networks naturally apply to a broad range of applications that involve system monitoring and information tracking (e.g., fine-grained weather/environmental monitoring, structural health monitoring, urban-scale traffic or parking monitoring, gunshot detection, monitoring volcanic eruptions, measuring rate of melting glaciers, forest fire detection, emergency medical care, disaster response, airport security infrastructure, monitoring of children in metropolitan areas, product transition in warehouse networks etc.).Meanwhile, existing wireless sensor networks (WSNs) perform poorly when the applications have high bandwidth needs for data transmission and stringent delay constraints against the network communication. Such requirements are common for Data Intensive Sensor Networks (DISNs) implementing Mission-Critical Monitoring applications (MCM applications).We propose to enhance existing wireless network standards with flexible query optimization strategies that take into account network constraints and application-specific data delivery patterns in order to meet high performance requirements of MCM applications.In this respect, this dissertation has two major contributions: First, we have developed an algebraic framework called Data Transmission Algebra (DTA) for collision-aware concurrent data transmissions. Here, we have merged the serialization concept from the databases with the knowledge of wireless network characteristics. We have developed an optimizer that uses the DTA framework, and generates an optimal data transmission schedule with respect to latency, throughput, and energy usage. We have extended the DTA framework to handle location-based trust and sensor mobility. We improved DTA scalability with Whirlpool data delivery mechanism, which takes advantage of partitioning of the network. Second, we propose relaxed optimization strategy and develop an adaptive approach to deliver data in data-intensive wireless sensor networks. In particular, we have shown that local actions at nodes help network to adapt in worse network conditions and perform better. We show that local decisions at the nodes can converge towards desirable global network properties e.g.,high packet success ratio for the network. We have also developed a network monitoring tool to assess the state and dynamic convergence of the WSN, and force it towards better performance