Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Low Duty-Cycled Wireless Sensor Networks: Connectivity and Opportunistic Routing

This thesis addresses a number of performance and design issues that arise in a low duty-cycled wireless sensor network. The advances in sensing technology, miniaturization and wireless communication have led to a large number of emerging applications using networked wireless sensors. One of the most critical design goals is the longevity of the system. A widely accepted and commonly used method of energy conservation is duty cycling -- sensor nodes are periodically put to sleep mode to conserve energy. While effective in prolonging the system lifetime, duty-cycling disrupts communication and sensing capabilities as sensors alternate between sleep and wake modes. This not only affects network coverage and connectivity, but also causes delay in message delivery. A central theme of this thesis is to understand the energy-performance trade-off and design good networking algorithms that work well with low duty-cycled sensors. Our work thus centers on how the performance degradation caused by duty-cycling may be mitigated. The first method is to add redundancy to the deployment: the more sensors we deploy, the more we can reduce the duty cycle of individual sensors while maintaining the system level performance. In this context we investigate the fundamental relationship between the amount of redundancy required vs. the achievable reduction in duty cycle for a fixed performance criterion. We examine this relationship in the case of asymptotic network connectivity. A second method is to design good algorithms that effectively deal with temporal loss of connectivity. Within this context, we first develop a routing scheme using an optimal stochastic (also referred to as opportunistic) routing framework, designed to work in the presence of duty-cycling as well as unreliable wireless channels. We then examine how the routing delay of this type of algorithms scales compared to conventional (non-opportunistic) routing algorithms in a limiting regime where the network becomes dense. Lastly, for any routing algorithm to work properly there needs to be an efficient broadcast mechanism that discovers and disseminates topology information. In this context we develop an analysis-emulation hybrid model that combines analytical models with elements of numerical simulation to obtain the desired modeling accuracy and computational efficiency.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61569/1/kimds_1.pd

A framework for energy based performability models for wireless sensor networks

A novel idea of alternating node operations between Active and Sleep modes in Wireless Sensor Network (WSN) has successfully been used to save node power consumption. The idea which started off as a simple implementation of a timer in most protocols has been improved over the years to dynamically change with traffic conditions and the nature of application area. Recently, use of a second low power radio transceiver to triggered Active/Sleep modes has also been made. Active/Sleep operation modes have also been used to separately model and evaluate performance and availability of WSNs. The advancement in technology and continuous improvements of the existing protocols and application implementation demands continue to pose great challenges to the existing performance and availability models. In this study the need for integrating performance and availability studies of WSNs in the presence of both channel and node failures and repairs is investigated. A framework that outlines and characterizes key models required for integration of performance and availability of WSN is in turn outlined. Possible solution techniques for such models are also highlighted. Finally it is shown that the resulting models may be used to comparatively evaluate energy consumption of the existing motes and WSNs as well as deriving required performance measures

The Beauty of the Commons: Optimal Load Sharing by Base Station Hopping in Wireless Sensor Networks

In wireless sensor networks (WSNs), the base station (BS) is a critical sensor node whose failure causes severe data losses. Deploying multiple fixed BSs improves the robustness, yet requires all BSs to be installed with large batteries and large energy-harvesting devices due to the high energy consumption of BSs. In this paper, we propose a scheme to coordinate the multiple deployed BSs such that the energy supplies required by individual BSs can be substantially reduced. In this scheme, only one BS is selected to be active at a time and the other BSs act as regular sensor nodes. We first present the basic architecture of our system, including how we keep the network running with only one active BS and how we manage the handover of the role of the active BS. Then, we propose an algorithm for adaptively selecting the active BS under the spatial and temporal variations of energy resources. This algorithm is simple to implement but is also asymptotically optimal under mild conditions. Finally, by running simulations and real experiments on an outdoor testbed, we verify that the proposed scheme is energy-efficient, has low communication overhead and reacts rapidly to network changes

Stochastic Performance Trade-offs in the Design of Real-Time Wireless Sensor Networks

Future sensing applications call for a thorough evaluation of network performance trade-offs so that desired guarantees can be provided for the realization of real-time wireless sensor networks (WSNs). Recent studies provide insight into the performance metrics in terms of first-order statistics, e.g., the expected delay. However, WSNs are characterized by the stochastic nature of the wireless channel and the queuing processes, which result in non-deterministic delay, throughput, and network lifetime. For the design of WSNs with predictable performance, probabilistic analysis of these performance metrics and their intrinsic trade-offs is essential. Moreover, providing stochastic guarantees is crucial since each deployment may result in a different realization. In this paper, the trade-offs between delay, throughput, and lifetime are quantified through a stochastic network design approach. To this end, two novel probabilistic network design measures, quantile and quantile interval, are defined to capture the dependability and predictability of the performance metrics, respectively. Extensive evaluations are conducted to explore the performance trade-offs in real-time WSNs