Towards Real-time Wireless Sensor Networks

Wireless sensor networks are poised to change the way computer systems interact with the physical world. We plan on entrusting sensor systems to collect medical data from patients, monitor the safety of our infrastructure, and control manufacturing processes in our factories. To date, the focus of the sensor network community has been on developing best-effort services. This approach is insufficient for many applications since it does not enable developers to determine if a system\u27s requirements in terms of communication latency, bandwidth utilization, reliability, or energy consumption are met. The focus of this thesis is to develop real-time network support for such critical applications. The first part of the thesis focuses on developing a power management solution for the radio subsystem which addresses both the problem of idle-listening and power control. In contrast to traditional power management solutions which focus solely on reducing energy consumption, the distinguishing feature of our approach is that it achieves both energy efficiency and real-time communication. A solution to the idle-listening problem is proposed in Energy Efficient Sleep Scheduling based on Application Semantics: ESSAT). The novelty of ESSAT lies in that it takes advantage of the common features of data collection applications to determine when to turn on and off a node\u27s radio without affecting real-time performance. A solution to the power control problem is proposed in Real-time Power Aware-Routing: RPAR). RPAR tunes the transmission power for each packet based on its deadline such that energy is saved without missing packet deadlines. The main theoretical contribution of this thesis is the development of novel transmission scheduling techniques optimized for data collection applications. This work bridges the gap between wireless sensor networks and real-time scheduling theory, which have traditionally been applied to processor scheduling. The proposed approach has significant advantages over existing design methodologies:: 1) it provides predictable performance allowing for the performance of a system to be estimated upon its deployment,: 2) it is possible to detect and handle overload conditions through simple rate control mechanisms, and: 3) it easily accommodates workload changes. I developed this framework under a realistic interference model by coordinating the activities at the MAC, link, and routing layers. The last component of this thesis focuses on the development of a real-time patient monitoring system for general hospital units. The system is designed to facilitate the detection of clinical deterioration, which is a key factor in saving lives and reducing healthcare costs. Since patients in general hospital wards are often ambulatory, a key challenge is to achieve high reliability even in the presence of mobility. To support patient mobility, I developed the Dynamic Relay Association Protocol -- a simple and effective mechanism for dynamically discovering the right relays for forwarding patient data -- and a Radio Mapping Tool -- a practical tool for ensuring network coverage in 802.15.4 networks. We show that it is feasible to use low-power and low-cost wireless sensor networks for clinical monitoring through an in-depth clinical study. The study was performed in a step-down cardiac care unit at Barnes-Jewish Hospital. This is the first long-term study of such a patient monitoring system

Planning the deployment of fault-tolerant wireless sensor networks

Since Wireless Sensor Networks (WSNs) are subject to failures, fault-tolerance becomes an important requirement for many WSN applications. Fault-tolerance can be enabled in different areas of WSN design and operation, including the Medium Access Control (MAC) layer and the initial topology design. To be robust to failures, a MAC protocol must be able to adapt to traffic fluctuations and topology dynamics. We design ER-MAC that can switch from energy-efficient operation in normal monitoring to reliable and fast delivery for emergency monitoring, and vice versa. It also can prioritise high priority packets and guarantee fair packet deliveries from all sensor nodes. Topology design supports fault-tolerance by ensuring that there are alternative acceptable routes to data sinks when failures occur. We provide solutions for four topology planning problems: Additional Relay Placement (ARP), Additional Backup Placement (ABP), Multiple Sink Placement (MSP), and Multiple Sink and Relay Placement (MSRP). Our solutions use a local search technique based on Greedy Randomized Adaptive Search Procedures (GRASP). GRASP-ARP deploys relays for (k,l)-sink-connectivity, where each sensor node must have k vertex-disjoint paths of length ≤ l. To count how many disjoint paths a node has, we propose Counting-Paths. GRASP-ABP deploys fewer relays than GRASP-ARP by focusing only on the most important nodes – those whose failure has the worst effect. To identify such nodes, we define Length-constrained Connectivity and Rerouting Centrality (l-CRC). Greedy-MSP and GRASP-MSP place minimal cost sinks to ensure that each sensor node in the network is double-covered, i.e. has two length-bounded paths to two sinks. Greedy-MSRP and GRASP-MSRP deploy sinks and relays with minimal cost to make the network double-covered and non-critical, i.e. all sensor nodes must have length-bounded alternative paths to sinks when an arbitrary sensor node fails. We then evaluate the fault-tolerance of each topology in data gathering simulations using ER-MAC

A Framework for Service Differentiation and Optimization in Multi-hop Wireless Networks

In resource-constrained networks such as multi-hop wireless networks (MHWNs), service differentiation algorithms designed to address end users' interests (e.g. user satisfaction, QoS, etc.) should also consider efficient utilization of the scarce network resources in order to maximize the network's interests (e.g. revenue). For this very reason, service differentiation in MHWNs is quite different from the wired network scenario. We propose a service differentiation tool called the ``Investment Function'', which essentially captures the network's cumulative resource investment in a given packet at a given time. This investment value can be used by the network algorithm to implement specific service differentiation principles. As proof-of-concept, we use the investment function to improve fairness among simultaneous flows that traverse varying number of hops in a MHWN (multihop flow fairness). However, to attain the optimal value of a specific service differentiation objective, optimal service differentiation and investment function parameters may need to be computed. The optimal parameters can be computed by casting the service differentiation problem as a network flow problem in MHWNs, with the goal of optimizing the service differentiation objective. The capacity constraints for these problems require knowledge of the adjacent-node interference values, and constructing these constraints could be very expensive based on the transmission scheduling scheme used. As a result, even formulating the optimization problem may take unacceptable computational effort or memory or both. Under optimal scheduling, the adjacent node interference values (and thus the capacity constraints) are not only very expensive to compute, but also cannot be expressed in polynomial form. Therefore, existing optimization techniques cannot be directly applied to solve optimization problems in MHWNs. To develop an efficient optimization framework, we first model the MHWN as a Unit Disk Graph (UDG). The optimal transmission schedule in the MHWN is related to the chromatic number of the UDG, which is very expensive to compute. However, the clique number, which is a lower bound on the chromatic number, can be computed in polynomial time in UDGs. Through an empirical study, we obtain tighter bounds on the ratio of the chromatic number to clique number in UDGs, which enables us to leverage existing polynomial time clique-discovery algorithms to compute very close approximations to the chromatic number value. This approximation not only allows us to quickly formulate the capacity constraints in polynomial form, but also allows us to significantly deviate from the traditional approach of discovering all or most of the constraints \textit{a priori}; instead, we can discover the constraints as needed. We have integrated this approach of constraint-discovery into an active-set optimization algorithm (Gradient Projection method) to solve network flow problems in multi-hop wireless networks. Our results show significant memory and computational savings when compared to existing methods

Advanced flight control system study

The architecture, requirements, and system elements of an ultrareliable, advanced flight control system are described. The basic criteria are functional reliability of 10 to the minus 10 power/hour of flight and only 6 month scheduled maintenance. A distributed system architecture is described, including a multiplexed communication system, reliable bus controller, the use of skewed sensor arrays, and actuator interfaces. Test bed and flight evaluation program are proposed