Monitoring Quality Maximization through Fair Rate Allocation in Harvesting Sensor Networks

In this paper, we consider an energy harvesting sensor network where sensors are powered by reusable energy such as solar energy, wind energy, and so on, from their surroundings. We first formulate a novel monitoring quality maximization problem that aim

Performance optimization of wireless sensor networks for remote monitoring

Wireless sensor networks (WSNs) have gained worldwide attention in recent years because of their great potential for a variety of applications such as hazardous environment exploration, military surveillance, habitat monitoring, seismic sensing, and so on. In this thesis we study the use of WSNs for remote monitoring, where a wireless sensor network is deployed in a remote region for sensing phenomena of interest while its data monitoring center is located in a metropolitan area that is geographically distant from the monitored region. This application scenario poses great challenges since such kind of monitoring is typically large scale and expected to be operational for a prolonged period without human involvement. Also, the long distance between the monitored region and the data monitoring center requires that the sensed data must be transferred by the employment of a third-party communication service, which incurs service costs. Existing methodologies for performance optimization of WSNs base on that both the sensor network and its data monitoring center are co-located, and therefore are no longer applicable to the remote monitoring scenario. Thus, developing new techniques and approaches for severely resource-constrained WSNs is desperately needed to maintain sustainable, unattended remote monitoring with low cost. Specifically, this thesis addresses the key issues and tackles problems in the deployment of WSNs for remote monitoring from the following aspects. To maximize the lifetime of large-scale monitoring, we deal with the energy consumption imbalance issue by exploring multiple sinks. We develop scalable algorithms which determine the optimal number of sinks needed and their locations, thereby dynamically identifying the energy bottlenecks and balancing the data relay workload throughout the network. We conduct experiments and the experimental results demonstrate that the proposed algorithms significantly prolong the network lifetime. To eliminate imbalance of energy consumption among sensor nodes, a complementary strategy is to introduce a mobile sink for data gathering. However, the limited communication time between the mobile sink and nodes results in that only part of sensed data will be collected and the rest will be lost, for which we propose the concept of monitoring quality with the exploration of sensed data correlation among nodes. We devise a heuristic for monitoring quality maximization, which schedules the sink to collect data from selected nodes, and uses the collected data to recover the missing ones. We study the performance of the proposed heuristic and validate its effectiveness in improving the monitoring quality. To strive for the fine trade-off between two performance metrics: throughput and cost, we investigate novel problems of minimizing cost with guaranteed throughput, and maximizing throughput with minimal cost. We develop approximation algorithms which find reliable data routing in the WSN and strategically balance workload on the sinks. We prove that the delivered solutions are fractional of the optimum. We finally conclude our work and discuss potential research topics which derive from the studies of this thesis

Quality-Aware Scheduling Algorithms in Renewable Sensor

Wireless sensor network has emerged as a key technology for various applications such as environmental sensing, structural health monitoring, and area surveillance. Energy is by far one of the most critical design hurdles that hinders the deployment of wireless sensor networks. The lifetime of traditional battery-powered sensor networks is limited by the capacities of batteries. Even many energy conservation schemes were proposed to address this constraint, the network lifetime is still inherently restrained, as the consumed energy cannot be replenished easily. Fully addressing this issue requires energy to be replenished quite often in sensor networks (renewable sensor networks). One viable solution to energy shortages is enabling each sensor to harvest renewable energy from its surroundings such as solar energy, wind energy, and so on. In comparison with their conventional counterparts, the network lifetime in renewable sensor networks is no longer a main issue, since sensors can be recharged repeatedly. This results in a research focus shift from the network lifetime maximization in traditional sensor networks to the network performance optimization (e.g., monitoring quality). This thesis focuses on these issues and tackles important problems in renewable sensor networks as follows. We first study the target coverage optimization in renewable sensor networks via sensor duty cycle scheduling, where a renewable sensor network consisting of a set of heterogeneous sensors and a stationary base station need to be scheduled to monitor a set of targets in a monitoring area (e.g., some critical facilities) for a specified period, by transmitting their sensing data to the base station through multihop relays in a real-time manner. We formulate a coverage maximization problem in a renewable sensor network which is to schedule sensor activities such that the monitoring quality is maximized, subject to that the communication network induced by the activated sensors and the base station at each time moment is connected. We approach the problem for a given monitoring period by adopting a general strategy. That is, we divide the entire monitoring period into equal numbers of time slots and perform sensor activation or inactivation scheduling in the beginning of each time slot. As the problem is NP-hard, we devise efficient offline centralized and distributed algorithms for it, provided that the amount of harvested energy of each sensor for a given monitoring period can be predicted accurately. Otherwise, we propose an online adaptive framework to handle energy prediction fluctuation for this monitoring period. We conduct extensive experiments, and the experimental results show that the proposed solutions are very promising. We then investigate the data collection optimization in renewable sensor networks by exploiting sink mobility, where a mobile sink travels around the sensing field to collect data from sensors through one-hop transmission. With one-hop transmission, each sensor could send data directly to the mobile sink without any relay, and thus no energy are consumed on forwarding packets for others which is more energy efficient in comparison with multi-hop relays. Moreover, one-hop transmission particularly is very useful for a disconnected network, which may be due to the error-prone nature of wireless communication or the physical limit (e.g., some sensors are physically isolated), while multi-hop transmission is not applicable. In particular, we investigate two different kinds of mobile sinks, and formulate optimization problems under different scenarios, for which both centralized and distributed solutions are proposed accordingly. We study the performance of the proposed solutions and validate their effectiveness in improving the data quality. Since the energy harvested often varies over time, we also consider the scenario of renewable sensor networks by utilizing wireless energy transfer technology, where a mobile charging vehicle periodically travels inside the sensing field and charges sensors without any plugs or wires. Specifically, we propose a novel charging paradigm and formulate an optimization problem with an objective of maximizing the number of sensors charged per tour. We devise an offline approximation algorithm which runs in quasi-polynomial time and develop efficient online sensor charging algorithms, by considering the dynamic behaviors of sensors’ various sensing and transmission activities. To study the efficiency of the proposed algorithms, we conduct extensive experiments and the experimental results demonstrate that the proposed algorithms are very efficient. We finally conclude our work and discuss potential research topics which derive from the studies of this thesis