International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Delay-tolerant networking (DTN) is a system for constructing automated data networks in which end-to-end communication is reliable despite low data rates, possible sustained interruptions in connectivity, and potentially high signal propagation latency. As such it promises to provide an inexpensive and robust medium for returning telemetry from research vehicles in environments that provide meager support for communications: deep space, the surface of Mars, the poles or the sub- Arctic steppes of Earth, and others. This paper presents an overview of DTN concepts, including “bundles” and the Bundling overlay protocol. One possible scenario for the application of DTN to a telemetry return problem is described, and there is a brief discussion of the current state of DTN technology development.

Wikis are a popular collaboration technology. They support the collaborative editing of web pages through a simple mark-up language. Mobile devices are becoming ubiquitous, powerful and affordable. Thus it is advantageous for people to get the benefits of wikis in a mobile setting. However, mobile computing leads to its own challenges such as limited screen size, bandwidth, memory and battery life; they also have intermittent connectivity due to the mobility and the coverage of network. I investigate how wikis can be made mobile; that is how wiki forms of collaborative editing can be achieved through mobile devices such as PDAs and smart phones. A prototype mobile wiki has been created using .NET, which addresses these issues and enables simple collaborative working whilst on and offline through smart mobile devices. A cut down wiki runs on the mobile device. This communicates with a main central wiki to cache pages for off line use. When sitting in a powered cradle eager, downloading and synchronization of pages is supported. During mobile operation, pages are cached lazily on demand to minimize power use and to save the limited and expensive bandwidth. On re-connection, offline edited as well as new pages are synchronized with the main wiki server. Finally a pluggable page rendering engine enables pages to be rendered in different ways to suit different sized screens.

Wireless sensor networks (WSNs) have become pivotal in precision agriculture, environmental monitoring, and smart healthcare applications. However, the challenges of energy consumption and security, particularly concerning the reliance on large battery-operated nodes, pose significant hurdles for these networks. Energy-harvesting wireless sensor networks (EH-WSNs) emerged as a solution, enabling nodes to replenish energy from the environment remotely. Yet, the transition to EH-WSNs brought forth new obstacles in ensuring reliable and secure data transmission. In our initial study, we tackled the intermittent connectivity issue prevalent in EH-WSNs due to the dynamic behavior of energy harvesting nodes. Rapid shifts between ON and OFF states led to frequent changes in network topology, causing reduced link stability. To counter this, we introduced the hybrid routing method (HRM), amalgamating grid-based and opportunistic-based routing. HRM incorporated a packet fragmentation mechanism and cooperative localization for both static and mobile networks. Simulation results demonstrated HRM's superior performance, enhancing key metrics such as throughput, packet delivery ratio, and energy consumption in comparison to existing energy-aware adaptive opportunistic routing approaches. Our second research focused on countering emerging threats, particularly the malicious energy attack (MEA), which remotely powers specific nodes to manipulate routing paths. We developed intelligent energy attack methods utilizing Q-learning and Policy Gradient techniques. These methods enhanced attacking capabilities across diverse network settings without requiring internal network information. Simulation results showcased the efficacy of our intelligent methods in diverting traffic loads through compromised nodes, highlighting their superiority over traditional approaches. In our third study, we developed a deep learning-based two-stage framework to detect MEAs. Utilizing a stacked residual network (SR-Net) for global classification and a stacked LSTM network (SL-Net) to pinpoint specific compromised nodes, our approach demonstrated high detection accuracy. By deploying trained models as defenses, our method outperformed traditional threshold filtering techniques, emphasizing its accuracy in detecting MEAs and securing EH-WSNs. In summary, our research significantly advances the reliability and security of EH-WSN, particularly focusing on enhancing the network layer. These findings offer promising avenues for securing the future of wireless sensor technologies.