Design and analysis of adaptive hierarchical low-power long-range networks

A new phase of evolution of Machine-to-Machine (M2M) communication has started where vertical Internet of Things (IoT) deployments dedicated to a single application domain gradually change to multi-purpose IoT infrastructures that service different applications across multiple industries. New networking technologies are being deployed operating over sub-GHz frequency bands that enable multi-tenant connectivity over long distances and increase network capacity by enforcing low transmission rates to increase network capacity. Such networking technologies allow cloud-based platforms to be connected with large numbers of IoT devices deployed several kilometres from the edges of the network. Despite the rapid uptake of Long-power Wide-area Networks (LPWANs), it remains unclear how to organize the wireless sensor network in a scaleable and adaptive way. This paper introduces a hierarchical communication scheme that utilizes the new capabilities of Long-Range Wireless Sensor Networking technologies by combining them with broadly used 802.11.4-based low-range low-power technologies. The design of the hierarchical scheme is presented in detail along with the technical details on the implementation in real-world hardware platforms. A platform-agnostic software firmware is produced that is evaluated in real-world large-scale testbeds. The performance of the networking scheme is evaluated through a series of experimental scenarios that generate environments with varying channel quality, failing nodes, and mobile nodes. The performance is evaluated in terms of the overall time required to organize the network and setup a hierarchy, the energy consumption and the overall lifetime of the network, as well as the ability to adapt to channel failures. The experimental analysis indicate that the combination of long-range and short-range networking technologies can lead to scalable solutions that can service concurrently multiple applications

Investigating Master-Slave Architecture for Underwater Wireless Sensor Network.

A significant increase has been observed in the use of Underwater Wireless Sensor Networks (UWSNs) over the last few decades. However, there exist several associated challenges with UWSNs, mainly due to the nodes' mobility, increased propagation delay, limited bandwidth, packet duplication, void holes, and Doppler/multi-path effects. To address these challenges, we propose a protocol named "An Efficient Routing Protocol based on Master-Slave Architecture for Underwater Wireless Sensor Network (ERPMSA-UWSN)" that significantly contributes to optimizing energy consumption and data packet's long-term survival. We adopt an innovative approach based on the master-slave architecture, which results in limiting the forwarders of the data packet by restricting the transmission through master nodes only. In this protocol, we suppress nodes from data packet reception except the master nodes. We perform extensive simulation and demonstrate that our proposed protocol is delay-tolerant and energy-efficient. We achieve an improvement of 13% on energy tax and 4.8% on Packet Delivery Ratio (PDR), over the state-of-the-art protocol

Reliable load-balancing routing for resource-constrained wireless sensor networks

Wireless sensor networks (WSNs) are energy and resource constrained. Energy limitations make it advantageous to balance radio transmissions across multiple sensor nodes. Thus, load balanced routing is highly desirable and has motivated a significant volume of research. Multihop sensor network architecture can also provide greater coverage, but requires a highly reliable and adaptive routing scheme to accommodate frequent topology changes. Current reliability-oriented protocols degrade energy efficiency and increase network latency. This thesis develops and evaluates a novel solution to provide energy-efficient routing while enhancing packet delivery reliability. This solution, a reliable load-balancing routing (RLBR), makes four contributions in the area of reliability, resiliency and load balancing in support of the primary objective of network lifetime maximisation. The results are captured using real world testbeds as well as simulations. The first contribution uses sensor node emulation, at the instruction cycle level, to characterise the additional processing and computation overhead required by the routing scheme. The second contribution is based on real world testbeds which comprises two different TinyOS-enabled senor platforms under different scenarios. The third contribution extends and evaluates RLBR using large-scale simulations. It is shown that RLBR consumes less energy while reducing topology repair latency and supports various aggregation weights by redistributing packet relaying loads. It also shows a balanced energy usage and a significant lifetime gain. Finally, the forth contribution is a novel variable transmission power control scheme which is created based on the experience gained from prior practical and simulated studies. This power control scheme operates at the data link layer to dynamically reduce unnecessarily high transmission power while maintaining acceptable link reliability