Jlie HAM is a transaction-based sender for a hi/p'^ertext storage system. Vie sender is designed to Imndle nndtiptle users in a netivorked environment. Hie storage system consists of a collection of contexts, nodes, links, and attributes tJmt niake up a In/peiiext graph. Hie versatiliti/ of the HAA4. can be illustrated bi/ showing hoiv Guide buttons, Intennedm webs, and NoteCard FileBoxes can be implemented usmg its storage model.The Hypertext Abstract Machine (HAM) is a generalpurpose, transaction-based, multi-user server for a hypertext storage system. The HAM is based on the abstract machine Norm Delisle and Mayer Schwartz used in their Neptune system developed at Tektronix' Computer Research Laboratory [1]. Because the HAM is a low-level storage engine, it provides a general and flexible model that can be used in several different hypertext applications. For example, the HAM. combined with the Neptune user interface, provides a prototype system for a software engineering environment.The HAM stores all of the information it manages in graphs, or databases, on a host machine's file systems. Graphs are stored in a centralized area and can be accessed in a distributed environment. The HAM typically communicates with an application through a byte stream protocol (although it may alternately be statically linked with an application). The application may or may not run on the same machine as the HAM.Applications normally communicate with the outside world through a common user interface. The interface should be window-based and highly interactive to provide a suitable environment for a hypertext system. Figure 1 shows the typical organization of a system using the HAM.©1988 ACM OOni-0782/88/0700-0856 Sl,50 HAM FEATURES The HAM storage model is based on five objects: graphs, contexts, nodes, links, and attributes. The HAM maintains history for these objects, allows selective access through a filtering mechanism, and can allow for access restrictions through a data security mechanism. HAM ObjectsA graph contains contexts, nodes, links, and attributes. These objects are organized hierarchically and carry the following descriptions: A graph is the highest level HAM object. It normally contains all of the information regarding a general topic, such as the information for a software project. A graph contains one or more contexts.Contexts partition the data within a graph. Contexts can be used to support configurations, private workspaces, and version history trees [6], Each context has one parent context and zero or more child contexts. When a graph is created, a root context begins the tree. A context which contains zero or more nodes and links does not depend on information contained in its parent context.A }iode contains arbitrary data that can be stored as text or as fixed-length binary blocks. A node can be classified as archived, nonarchived, or append-only. When an archived node is updated, a new version of 856
The vision of an Internet of Things (IoT) has captured the imagination of the world and raised billions of dollars, all before we stopped to deeply consider how all these Things should connect to the Internet. The current state-of-the-art requires application-layer gateways both in software and hardware that provide applicationspecific connectivity to IoT devices. In much the same way that it would be difficult to imagine requiring a new web browser for each website, it is hard to imagine our current approach to IoT connectivity scaling to support the IoT vision. The IoT gateway problem exists in part because today's gateways conflate network connectivity, in-network processing, and user interface functions. We believe that disentangling these functions would improve the connectivity potential for IoT devices. To realize the broader vision, we propose an architecture that leverages the increasingly ubiquitous presence of Bluetooth Low Energy radios to connect IoT peripherals to the Internet. In much the same way that WiFi access points revolutionized laptop utility, we envision that a worldwide deployment of IoT gateways could revolutionize application-agnostic connectivity, thus breaking free from the stove-piped architectures now taking hold. In this paper, we present our proposed architecture, show example applications enabled by it, and explore research challenges in its implementation and deployment.
Low-power microcontrollers lack some of the hardware features and memory resources that enable multiprogrammable systems. Accordingly, microcontroller-based operating systems have not provided important features like fault isolation, dynamic memory allocation, and flexible concurrency. However, an emerging class of embedded applications are software platforms, rather than single purpose devices, and need these multiprogramming features. Tock, a new operating system for low-power platforms, takes advantage of limited hardwareprotection mechanisms as well as the type-safety features of the Rust programming language to provide a multiprogramming environment for microcontrollers. Tock isolates software faults, provides memory protection, and efficiently manages memory for dynamic application workloads written in any language. It achieves this while retaining the dependability requirements of long-running applications.
Understanding building usage patterns and resource consumption, particularly for existing buildings, requires a sensing infrastructure for the building. Often, deploying these sensors and obtaining real-time information is hindered by installation and maintenance difficulties resulting from scaling down and powering these devices. Devices that rely on batteries are limited by the scale of the batteries and the maintenance cost of replacing them while AC mains powered sensors incur high upfront installation costs. To mitigate these burdens, we present a new architecture for designing building-monitoring focused energy-harvesting sensors.The key to this architecture is masking the inevitable intermittency provided by energy-harvesting with a trigger abstraction that activates the device only when there is useful work to be done. In this paper, we describe our architecture and demonstrate how it supports existing energy-harvesting sensor designs. Further, we realize three additional design points within the architecture and demonstrate how the sensors are effective at building monitoring and event detection. The sensors, however, are classically disruptive: they improve ease of installation and maintenance, but to do so, they sacrifice some fidelity and reliability. Whether this tradeoff is acceptable remains to be explored, but the technology needed to do so is now here.
We study the problem of augmenting battery-powered sensornet trees with energy-harvesting leaf nodes. Our results show that leaf nodes that are smaller in size than today's typical battery-powered sensors can harvest enough energy from ambient sources to acquire and transmit sensor readings every minute, even under poor lighting conditions. However, achieving this functionality, especially as leaf nodes scale in size, requires new platforms, protocols, and programming. Platforms must be designed around low-leakage operation, offer a richer power supply control interface for system software, and employ an unconventional energy storage hierarchy. Protocols must not only be low-power, but they must also become low-energy, which affects initial and ongoing synchronization, and periodic communications. Systems programming, and especially bootup and communications, must become low-latency, by eliminating conservative timeouts and startup dependencies, and embracing high-concurrency. Applying these principles, we show that robust, indoor, perpetual sensing is viable using off-the-shelf technology.
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