Distributed sensing coverage maintenance in sensor networks

Sensing coverage is one of the key performance indicators of a large-scale sensor network. Sensing coverage holes may appear anywhere in the network field at any time due to random deployment, depletion of sensor battery power, or natural events in the deployment environment such as strong wind blowing some sensors away. Discovering the exact boundaries of coverage holes is important because it enables fast and efficient patching of coverage holes. In this thesis, we propose a framework of sensing coverage maintenance in sensor networks. In our framework, a sensor network consists of stationary and mobile sensors, where mobile sensors are used as patching hosts. We divide the coverage maintenance into two components: coverage hole discovery and coverage hole patching, and propose new solutions to both components. (1) We present two efficient distributed algorithms that periodically discover the precise boundaries of coverage holes. Our algorithms can handle the case that the transmission range of a sensor is smaller than twice the sensing range of the sensor. This case is largely ignored by previous work. (2) We present an efficient hole patching algorithm, which runs in linear time, based on the knowledge of the precise boundary of each coverage hole. We further propose new solutions for looking up available patching hosts, and movement planning. We present rigorous mathematical proofs of the correctness of the proposed hole discovery algorithms. We also show the running time and the performance bound in terms of mobile sensors needed of our hole patching algorithm through solid mathematical analysis. Our simulation results show that our distributed discovery algorithms are much more efficient than their centralized counterparts in terms of network overhead and total discovery time while still achieving the same correctness in discovering the boundaries of coverage holes. Furthermore, our patching algorithm performs well in terms of number of mobile sensors needed with a linear running time, and our hole patching scheme can achieve fast hole patching time when moving mobile sensors in a parallel manner

A trajectory-based recruitment strategy of social sensors for participatory sensing

Participatory sensing, a promising sensing paradigm, enables people to collect and share sensor data on phenomena of interest using mobile devices across many applications, such as smart transportation and air quality monitoring. This article presents a framework of participatory sensing and then focuses on a key technical challenge: developing a trajectory-based recruitment strategy of social sensors in order to enable service providers to identify well suited participants for data sensing based on temporal availability, trust, and energy. To devise a basic recruitment strategy, the Dynamic Tensor Analysis algorithm is initially adopted to learn the time-series tensor of trajectory so that the users' trajectory can be predicted. To guarantee reliable sensing data collection and communication, the trust and energy factors are taken into account jointly in our multi-objective recruitment strategy. In particular, friend-like social sensors are also defined to deal with an emergency during participatory sensing. An illustrative example and experiment are conducted on a university campus to evaluate and demonstrate the feasibility and extensibility of the proposed recruitment strategy

Efficient multi-resolution data dissemination in wireless sensor networks

A large-scale distributed wireless sensor network is composed of a large collection of small low-power, unattended sensing devices equipped with limited memory, processors, and short-range wireless communication. The network is capable of controlling and monitoring ambient conditions, such as temperature, movement, sound, light and others, and thus enable smart environments. Energy efficient data dissemination is one of the fundamental services in large-scale wireless sensor networks. Based on the study of the data dissemination problem, we propose two efficient data dissemination schemes for two categories of applications in large-scale wireless sensor networks. In addition, our schemes provide spatial-based multi-resolution data dissemination for some applications to achieve further energy efficiency. Analysis and simulation results are given to show the performance of our schemes in comparison with current techniques