Analysis of Power-aware Buffering Schemes in Wireless Sensor Networks

We study the power-aware buffering problem in battery-powered sensor networks, focusing on the fixed-size and fixed-interval buffering schemes. The main motivation is to address the yet poorly understood size variation-induced effect on power-aware buffering schemes. Our theoretical analysis elucidates the fundamental differences between the fixed-size and fixed-interval buffering schemes in the presence of data size variation. It shows that data size variation has detrimental effects on the power expenditure of the fixed-size buffering in general, and reveals that the size variation induced effects can be either mitigated by a positive skewness or promoted by a negative skewness in size distribution. By contrast, the fixed-interval buffering scheme has an obvious advantage of being eminently immune to the data-size variation. Hence the fixed-interval buffering scheme is a risk-averse strategy for its robustness in a variety of operational environments. In addition, based on the fixed-interval buffering scheme, we establish the power consumption relationship between child nodes and parent node in a static data collection tree, and give an in-depth analysis of the impact of child bandwidth distribution on parent's power consumption. This study is of practical significance: it sheds new light on the relationship among power consumption of buffering schemes, power parameters of radio module and memory bank, data arrival rate and data size variation, thereby providing well-informed guidance in determining an optimal buffer size (interval) to maximize the operational lifespan of sensor networks

Interference Mitigation in Multi-Hop Wireless Networks with Advanced Physical-Layer Techniques

In my dissertation, we focus on the wireless network coexistence problem with advanced physical-layer techniques. For the first part, we study the problem of Wireless Body Area Networks (WBAN)s coexisting with cross-technology interference (CTI). WBANs face the RF cross-technology interference (CTI) from non-protocol-compliant wireless devices. Werst experimentally characterize the adverse effect on BAN caused by the CTI sources. Then we formulate a joint routing and power control (JRPC) problem, which aims at minimizing energy consumption while satisfying node reachability and delay constraints. We reformulate our problem into a mixed integer linear programing problem (MILP) and then derive the optimal results. A practical JRPC protocol is then proposed. For the second part, we study the coexistence of heterogeneous multi-hop networks with wireless MIMO. We propose a new paradigm, called cooperative interference mitigation (CIM), which makes it possible for disparate networks to cooperatively mitigate the interference to/from each other to enhance everyone\u27s performance. We establish two tractable models to characterize the CIM behaviors of both networks by using full IC (FIC) and receiver-side IC (RIC) only. We propose two bi-criteria optimization problems aiming at maximizing both networks\u27 throughput, while cooperatively canceling the interference between them based on our two models. In the third and fourth parts, we study the coexistence problem with MIMO from a different point of view: the incentive of cooperation. We propose a novel two-round game framework, based on which we derive two networks\u27 equilibrium strategies and the corresponding closed-form utilities. We then extend our game-theoretical analysis to a general multi-hop case, specifically the coexistence problem between primary network and multi-hop secondary network in the cognitive radio networks domain. In the final part, we study the benefits brought by reconfigurable antennas (RA). We systematically exploit the pattern diversity and fast reconfigurability of RAs to enhance the throughput of MWNs. Werst propose a novel link-layer model that captures the dynamic relations between antenna pattern, link coverage and interference. Based on our model, a throughput optimization framework is proposed by jointly considering pattern selection and link scheduling, which is formulated as a mixed integer non-linear programming problem

Sofie: Smart Operating System For Internet Of Everything

The proliferation of Internet of Things and the success of rich cloud services have pushed the horizon of a new computing paradigm, Edge computing, which calls for processing the data at the edge of the network. Applications such as cloud offloading, smart home, and smart city are idea area for Edge computing to achieve better performance than cloud computing. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. However, there are still some challenges for applying Edge computing in our daily life. The missing of the specialized operating system for Edge computing is holding back the flourish of Edge computing applications. Service management, device management, component selection as well as data privacy and security is also not well supported yet in the current computing structure. To address the challenges for Edge computing systems and applications in these aspects, we have planned a series of empirical and theoretical research. We propose SOFIE: Smart Operating System For Internet Of Everything. SOFIE is the operating system specialized for Edge computing running on the Edge gateway. SOFIE could establish and maintain a reliable connection between cloud and Edge device to handle the data transportation between gateway and Edge devices; to provide service management and data management for Edge applications; to protect data privacy and security for Edge users; to guarantee the wellness of the Edge devices. Moreover, SOFIE also provide a naming mechanism to connect Edge device more efficiently. To solve the component selection problem in Edge computing paradigm, SOFIE also include our previous work, SURF, as a model to optimize the performance of the system. Finally, we deployed the design of SOFIE on an IoT/M2M system and support semantics with access control

Footfall energy harvesting : footfall energy harvesting conversion mechanisms

Ubiquitous computing and pervasive networks are prevailing to impact almost every part of our daily lives. Convergence of technologies has allowed electronic devices to become untethered. Cutting of the power-cord and communications link has provided many benefits, mobility and convenience being the most advantageous, however, an important but lagging technology in this vision is the power source. The trend in power density of batteries has not tracked the advancements in electronic systems development. This has provided opportunity for a bridging technology which uses a more integrated approach with the power source to emerge, where a device has an onboard self sustaining energy supply. This approach promises to close the gap between the increased miniaturisation of electronics systems and the physically constrained battery technology by tapping into the ambient energy available in the surrounding location of an application. Energy harvesting allows some of the costly maintenance and environmentally damaging issues of battery powered systems to be reduced.This work considers the characteristics and energy requirements of wireless sensor and actuator networks. It outlines a range of sources from which the energy can be extracted and then considers the conversion methods which could be employed in such schemes. This research looks at the methods and techniques for harvesting/scavenging energy from ambient sources, in particular from the motion of human traffic on raised flooring and stairwells for the purpose of powering wireless sensor and actuator networks. Mechanisms for the conversion of mechanical energy to electrical energy are evaluated for their benefits in footfall harvesting, from which, two conversion mechanisms are chosen for prototyping.The thesis presents two stair-mounted generator designs. Conversion that extends the intermittent pulses of energy in footfall is shown to be the beneficial. A flyback generator is designed which converts the linear motion of footfall to rotational torque is presented. Secondly, a cantilever design which converts the linear motion to vibration is shown. Both designs are mathematically modelled and the behaviour validated with experimental results & analysis. Power, energy and efficiency characteristics for both mechanisms are compared. Cost of manufacture and reliability are also discussed

Value-of-Information based Data Collection in Underwater Sensor Networks

Underwater sensor networks are deployed in marine environments, presenting specific challenges compared to sensor networks deployed in terrestrial settings. Among the major issues that underwater sensor networks face is communication medium limitations that result in low bandwidth and long latency. This creates problems when these networks need to transmit large amounts of data over long distances. A possible solution to address this issue is to use mobile sinks such as autonomous underwater vehicles (AUVs) to offload these large quantities of data. Such mobile sinks are called data mules. Often it is the case that a sensor network is deployed to report events that require immediate attention. Delays in reporting such events can have catastrophic consequences. In this dissertation, we present path planning algorithms that help in prioritizing data retrieval from sensor nodes in such a manner that nodes that require more immediate attention would be dealt with at the earliest. In other words, the goal is to improve the Quality of Information (QoI) retrieved. The path planning algorithms proposed in this dissertation are based on heuristics meant to improve the Value of Information (VoI) retrieved from a system. Value of information is a construct that helps in encoding the valuation of an information segment i.e. it is the price an optimal player would pay to obtain a segment of information in a game theoretic setting. Quality of information and value of information are complementary concepts. In this thesis, we formulate a value of information model for sensor networks and then consider the constraints that arise in underwater settings. On the basis of this, we develop a VoI-based path planning problem statement and propose heuristics that solve the path planning problem. We show through simulation studies that the proposed strategies improve the value, and hence, quality of the information retrieved. It is important to note that these path planning strategies can be applied equally well in terrestrial settings that deploy mobile sinks for data collection

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