Self-Synchronization in Duty-cycled Internet of Things (IoT) Applications

In recent years, the networks of low-power devices have gained popularity. Typically these devices are wireless and interact to form large networks such as the Machine to Machine (M2M) networks, Internet of Things (IoT), Wearable Computing, and Wireless Sensor Networks. The collaboration among these devices is a key to achieving the full potential of these networks. A major problem in this field is to guarantee robust communication between elements while keeping the whole network energy efficient. In this paper, we introduce an extended and improved emergent broadcast slot (EBS) scheme, which facilitates collaboration for robust communication and is energy efficient. In the EBS, nodes communication unit remains in sleeping mode and are awake just to communicate. The EBS scheme is fully decentralized, that is, nodes coordinate their wake-up window in partially overlapped manner within each duty-cycle to avoid message collisions. We show the theoretical convergence behavior of the scheme, which is confirmed through real test-bed experimentation.Comment: 12 Pages, 11 Figures, Journa

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Sustainable IoT Sensing Applications Development through GraphQL-Based Abstraction Layer

Internet of Things (IoT) networks are mostly comprised of power-constrained devices, therefore the most important consideration in designing IoT applications, based on sensor networks is energy efficiency. Minor improvement in energy conservation methods can lead to a significant increase in the lifetime of IoT devices and overall network. To achieve efficient utilisation of energy, different solutions are proposed such as duty cycling optimization, design changes at the MAC layer, etc. In this paper, we propose a new approach to overcome this challenge in cloud-based IoT sensing applications, based on integration of an abstraction layer with constrained application mechanism. To achieve energy conservation and efficient data management in IoT sensing applications, we incorporate modules of efficient web framework with cloud services, in order to minimize the number of round trips for data delivery and graph-based data representation. Our study is the first attempt in the literature, to the best of our knowledge, which introduces the potential of this integration for achieving the aforementioned objectives in the target applications. We implemented the proposed interfacing of abstraction layer in constrained applications, to develop a testbed using Z1 IoT motes, Contiki OS and GraphQL web framework with Google cloud services. Experimental comparisons against baseline REST architecture approach show that our proposed approach achieved significant reductions in data delivery delay and energy consumption (minimum 51.53% and 52.88%, respectively) in IoT applications involving sensor network.</jats:p

DESIGN OF RELIABLE AND SUSTAINABLE WIRELESS SENSOR NETWORKS: CHALLENGES, PROTOCOLS AND CASE STUDIES

Integrated with the function of sensing, processing, and wireless communication, wireless sensors are attracting strong interest for a variety of monitoring and control applications. Wireless sensor networks (WSNs) have been deployed for industrial and remote monitoring purposes. As energy shortage is a worldwide problem, more attention has been placed on incorporating energy harvesting devices in WSNs. The main objective of this research is to systematically study the design principles and technical approaches to address three key challenges in designing reliable and sustainable WSNs; namely, communication reliability, operation with extremely low and dynamic power sources, and multi-tier network architecture. Mathematical throughput models, sustainable WSN communication strategies, and multi-tier network architecture are studied in this research to address these challenges, leading to protocols for reliable communication, energy-efficient operation, and network planning for specific application requirements. To account for realistic operating conditions, the study has implemented three distinct WSN testbeds: a WSN attached to the high-speed rotating spindle of a turning lathe, a WSN powered by a microbial fuel cell based energy harvesting system, and a WSN with a multi-tier network architecture. With each testbed, models and protocols are extracted, verified and analyzed. Extensive research has studied low power WSNs and energy harvesting capabilities. Despite these efforts, some important questions have not been well understood. This dissertation addresses the following three dimensions of the challenge. First, for reliable communication protocol design, mathematical throughput or energy efficiency estimation models are essential, yet have not been investigated accounting for specific application environment characteristics and requirements. Second, for WSNs with energy harvesting power sources, most current networking protocols do not work efficiently with the systems considered in this dissertation, such as those powered by extremely low and dynamic energy sources. Third, for multi-tier wireless network system design, routing protocols that are adaptive to real-world network conditions have not been studied. This dissertation focuses on these questions and explores experimentally derived mathematical models for designing protocols to meet specific application requirements. The main contributions of this research are 1) for industrial wireless sensor systems with fast-changing but repetitive mobile conditions, understand the performance and optimal choice of reliable wireless sensor data transmission methods, 2) for ultra-low energy harvesting wireless sensor devices, design an energy neutral communication protocol, and 3) for distributed rural wireless sensor systems, understand the efficiency of realistic routing in a multi-tier wireless network. Altogether, knowledge derived from study of the systems, models, and protocols in this work fuels the establishment of a useful framework for designing future WSNs

Sustainable Traffic Aware Duty-Cycle Adaptation in Harvested Multi-Hop Wireless Sensor Networks

International audienceSustainable power management techniques in energy harvesting wireless sensors currently adapt the consumption of sensors to their harvesting rate within the limits of their battery residual energy, but regardless of the traffic profile. To provide a fairer distribution of the energy according to application needs, we propose a new sustainable traffic aware duty-cycle adaptation scheme (STADA) that takes into account the traffic load in addition to previous factors. We evaluate our protocol in the specific context of multi-hop IEEE 802.15.4 beacon-enabled wireless sensor networks powered by solar energy. Simulations show that our solution outperforms traffic-unaware adaptation schemes while minimizing the variance of the quality of service provided to applications