Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Accelerated Backpressure Algorithm

We develop an Accelerated Back Pressure (ABP) algorithm using Accelerated Dual Descent (ADD), a distributed approximate Newton-like algorithm that only uses local information. Our construction is based on writing the backpressure algorithm as the solution to a network feasibility problem solved via stochastic dual subgradient descent. We apply stochastic ADD in place of the stochastic gradient descent algorithm. We prove that the ABP algorithm guarantees stable queues. Our numerical experiments demonstrate a significant improvement in convergence rate, especially when the packet arrival statistics vary over time.Comment: 9 pages, 4 figures. A version of this work with significantly extended proofs is being submitted for journal publicatio

Socio-economic aware data forwarding in mobile sensing networks and systems

The vision for smart sustainable cities is one whereby urban sensing is core to optimising city operation which in turn improves citizen contentment. Wireless Sensor Networks are envisioned to become pervasive form of data collection and analysis for smart cities but deployment of millions of inter-connected sensors in a city can be cost-prohibitive. Given the ubiquity and ever-increasing capabilities of sensor-rich mobile devices, Wireless Sensor Networks with Mobile Phones (WSN-MP) provide a highly flexible and ready-made wireless infrastructure for future smart cities. In a WSN-MP, mobile phones not only generate the sensing data but also relay the data using cellular communication or short range opportunistic communication. The largest challenge here is the efficient transmission of potentially huge volumes of sensor data over sometimes meagre or faulty communications networks in a cost-effective way. This thesis investigates distributed data forwarding schemes in three types of WSN-MP: WSN with mobile sinks (WSN-MS), WSN with mobile relays (WSN-HR) and Mobile Phone Sensing Systems (MPSS). For these dynamic WSN-MP, realistic models are established and distributed algorithms are developed for efficient network performance including data routing and forwarding, sensing rate control and and pricing. This thesis also considered realistic urban sensing issues such as economic incentivisation and demonstrates how social network and mobility awareness improves data transmission. Through simulations and real testbed experiments, it is shown that proposed algorithms perform better than state-of-the-art schemes.Open Acces

Practical opportunistic data collection in wireless sensor networks with mobile sinks

Wireless Sensor Networks with Mobile Sinks (WSN-MSs) are considered a viable alternative to the heavy cost of deployment of traditional wireless sensing infrastructures at scale. However, current state-of-the-art approaches perform poorly in practice due to their requirement of mobility prediction and specific assumptions on network topology. In this paper, we focus on lowdelay and high-throughput opportunistic data collection in WSN-MSs with general network topologies and arbitrary numbers of mobile sinks. We first propose a novel routing metric, Contact-Aware ETX (CA-ETX), to estimate the packet transmission delay caused by both packet retransmissions and intermittent connectivity. By implementing CA-ETX in the defacto TinyOS routing standard CTP and the IETF IPv6 routing protocol RPL, we demonstrate that CA-ETX can work seamlessly with ETX. This means that current ETXbased routing protocols for static WSNs can be easily extended to WSN-MSs with minimal modification by using CA-ETX. Further, by combing CA-ETX with the dynamic backpressure routing, we present a throughput-optimal scheme Opportunistic Backpressure Collection (OBC). Both CA-ETX and OBC are lightweight, easy to implement, and require no mobility prediction. Through test-bed experiments and extensive simulations, we show that the proposed schemes significantly outperform current approaches in terms of packet transmission delay, communication overhead, storage overheads, reliability, and scalability

Optimal Network Control in Partially-Controllable Networks

The effectiveness of many optimal network control algorithms (e.g., BackPressure) relies on the premise that all of the nodes are fully controllable. However, these algorithms may yield poor performance in a partially-controllable network where a subset of nodes are uncontrollable and use some unknown policy. Such a partially-controllable model is of increasing importance in real-world networked systems such as overlay-underlay networks. In this paper, we design optimal network control algorithms that can stabilize a partially-controllable network. We first study the scenario where uncontrollable nodes use a queue-agnostic policy, and propose a low-complexity throughput-optimal algorithm, called Tracking-MaxWeight (TMW), which enhances the original MaxWeight algorithm with an explicit learning of the policy used by uncontrollable nodes. Next, we investigate the scenario where uncontrollable nodes use a queue-dependent policy and the problem is formulated as an MDP with unknown queueing dynamics. We propose a new reinforcement learning algorithm, called Truncated Upper Confidence Reinforcement Learning (TUCRL), and prove that TUCRL achieves tunable three-way tradeoffs between throughput, delay and convergence rate