Adaptive sampling for spatial prediction in wireless sensor networks

University of Technology, Sydney. Faculty of Engineering and Information Technology.Networks of wireless sensors are increasingly exploited in crucial applications of monitoring spatially correlated environmental phenomena such as temperature, rainfall, soil ingredients, and air pollution. Such networks enable efficient monitoring and measurements can be included in developing models of the environmental fields even at unobserved locations. This requires determining the number of sensors and their sampling locations which minimize the uncertainty of predictions. Therefore, the aim of this thesis is to present novel, efficient and practically feasible approaches to sample the environments, so that the uncertainties at unobserved locations are minimized. Gaussian process (GP) is utilized to statistically model the spatial field. This thesis includes both stationary wireless sensor networks (SWSNs) and mobile robotic wireless sensor networks (MRWSNs), and thus the issues are correspondingly formulated into sensor selection and sensor placement problems, respectively. In the first part of the thesis, a novel performance metric for the sensor selection in the SWSNs, named average root mean square error, which reflects the average uncertainty of each predicted location, is proposed. In order to minimize this NP-hard and combinatorial optimization problem, a simulated annealing based algorithm is proposed; and the sensor selection problem is effectively addressed. Particularly, when considering the sensor selection in constrained environments, e.g. gas phase hydrogen sulphide in a sewage system, a modified GP with an improved covariance function is developed. An efficient mutual information maximization criterion suitable for this particular scenario is also presented to select the most informative gaseous sensor locations along the sewer system. The second part of this thesis introduces centralized and distributed methods for spatial prediction over time in the MRWSNs. For the purpose of finding the optimal sampling paths of the mobile wireless sensors to take the most informative observations at each time iteration, a sampling strategy is proposed based on minimizing the uncertainty at all unobserved locations. A novel and very efficient optimality criterion for the adaptive sampling problem is then presented so that the minimization can be addressed by a greedy algorithm in polynomial time. The solution is proven to be bounded; and computational time of the proposed algorithm is illustrated to be practically feasible for the resource-constrained MRWSNs. In order to enhance the issue of computational complexity, Gaussian Markov random field (GMRF) is utilized to model the spatial field exploiting sparsity of the precision matrix. A new GMRF optimality criterion for the adaptive navigation problem is also proposed such that computational complexity of a greedy algorithm to solve the resulting optimization is deterministic even with increasing number of measurements. Based on the realistic simulations conducted using the pre-published data sets, it has shown that the proposed algorithms are superior with appealing results

An energy-efficient adaptive sampling scheme for wireless sensor networks

Wireless sensor networks are new monitoring platforms. To cope with their resource constraints, in terms of energy and bandwidth, spatial and temporal correlation in sensor data can be exploited to find an optimal sampling strategy to reduce number of sampling nodes and/or sampling frequencies while maintaining high data quality. Majority of existing adaptive sampling approaches change their sampling frequency upon detection of (significant) changes in measurements. There are, however, applications that can tolerate (significant) changes in measurements as long as measurements fall within a specific range. Using existing adaptive sampling approaches for these applications is not energy-efficient. Targeting this type of applications, in this paper, we propose an energy-efficient adaptive sampling technique ensuring a certain level of data quality. We compare our proposed technique with two existing adaptive sampling approaches in a simulation environment and show its superiority in terms of energy efficiency and data quality

A Cross-Layer Approach for Minimizing Interference and Latency of Medium Access in Wireless Sensor Networks

In low power wireless sensor networks, MAC protocols usually employ periodic sleep/wake schedule to reduce idle listening time. Even though this mechanism is simple and efficient, it results in high end-to-end latency and low throughput. On the other hand, the previously proposed CSMA/CA-based MAC protocols have tried to reduce inter-node interference at the cost of increased latency and lower network capacity. In this paper we propose IAMAC, a CSMA/CA sleep/wake MAC protocol that minimizes inter-node interference, while also reduces per-hop delay through cross-layer interactions with the network layer. Furthermore, we show that IAMAC can be integrated into the SP architecture to perform its inter-layer interactions. Through simulation, we have extensively evaluated the performance of IAMAC in terms of different performance metrics. Simulation results confirm that IAMAC reduces energy consumption per node and leads to higher network lifetime compared to S-MAC and Adaptive S-MAC, while it also provides lower latency than S-MAC. Throughout our evaluations we have considered IAMAC in conjunction with two error recovery methods, i.e., ARQ and Seda. It is shown that using Seda as the error recovery mechanism of IAMAC results in higher throughput and lifetime compared to ARQ.Comment: 17 pages, 16 figure

Decentralised Control of Adaptive Sampling in Wireless Sensor Networks

The efficient allocation of the limited energy resources of a wireless sensor network in a way that maximises the information value of the data collected is a significant research challenge. Within this context, this paper concentrates on adaptive sampling as a means of focusing a sensor’s energy consumption on obtaining the most important data. Specifically, we develop a principled information metric based upon Fisher information and Gaussian process regression that allows the information content of a sensor’s observations to be expressed. We then use this metric to derive three novel decentralised control algorithms for information-based adaptive sampling which represent a trade-off in computational cost and optimality. These algorithms are evaluated in the context of a deployed sensor network in the domain of flood monitoring. The most computationally efficient of the three is shown to increase the value of information gathered by approximately 83%, 27%, and 8% per day compared to benchmarks that sample in a naive non-adaptive manner, in a uniform non-adaptive manner, and using a state-of-the-art adaptive sampling heuristic (USAC) correspondingly. Moreover, our algorithm collects information whose total value is approximately 75% of the optimal solution (which requires an exponential, and thus impractical, amount of time to compute)

Adaptive Duty Cycling MAC Protocols Using Closed-Loop Control for Wireless Sensor Networks

The fundamental design goal of wireless sensor MAC protocols is to minimize unnecessary power consumption of the sensor nodes, because of its stringent resource constraints and ultra-power limitation. In existing MAC protocols in wireless sensor networks (WSNs), duty cycling, in which each node periodically cycles between the active and sleep states, has been introduced to reduce unnecessary energy consumption. Existing MAC schemes, however, use a fixed duty cycling regardless of multi-hop communication and traffic fluctuations. On the other hand, there is a tradeoff between energy efficiency and delay caused by duty cycling mechanism in multi-hop communication and existing MAC approaches only tend to improve energy efficiency with sacrificing data delivery delay. In this paper, we propose two different MAC schemes (ADS-MAC and ELA-MAC) using closed-loop control in order to achieve both energy savings and minimal delay in wireless sensor networks. The two proposed MAC schemes, which are synchronous and asynchronous approaches, respectively, utilize an adaptive timer and a successive preload frame with closed-loop control for adaptive duty cycling. As a result, the analysis and the simulation results show that our schemes outperform existing schemes in terms of energy efficiency and delivery delay

Cross-Layer Adaptive Feedback Scheduling of Wireless Control Systems

There is a trend towards using wireless technologies in networked control systems. However, the adverse properties of the radio channels make it difficult to design and implement control systems in wireless environments. To attack the uncertainty in available communication resources in wireless control systems closed over WLAN, a cross-layer adaptive feedback scheduling (CLAFS) scheme is developed, which takes advantage of the co-design of control and wireless communications. By exploiting cross-layer design, CLAFS adjusts the sampling periods of control systems at the application layer based on information about deadline miss ratio and transmission rate from the physical layer. Within the framework of feedback scheduling, the control performance is maximized through controlling the deadline miss ratio. Key design parameters of the feedback scheduler are adapted to dynamic changes in the channel condition. An event-driven invocation mechanism for the feedback scheduler is also developed. Simulation results show that the proposed approach is efficient in dealing with channel capacity variations and noise interference, thus providing an enabling technology for control over WLAN.Comment: 17 pages, 12 figures; Open Access at http://www.mdpi.org/sensors/papers/s8074265.pd