A new era of theoretical computer science addresses fundamental problems about auctions, networks, and human behavior.

http://www.maths.lse.ac.uk/Personal/stengel/TEXTE/agt-stengel.pdf

Algorithmic Game Theory

Chapter 1. Introduction and Overview of Wireless Sensor Networks. Chapter 2. Commercial and Scientific Applications of Wireless Sensor Networks. Chapter 3. Basic Wireless Sensor Technology. Chapter 4. Wireless Sensors Networks Protocols: Physical Layer. Chapter 5. Medium Access Control Protocols for Wireless Sensor Networks. Chapter 6. Sensors Network Protocols: Routing Protocols. Chapter 7. Transport Control Protocols for Wireless Sensors Networks. Chapter 8. Middleware for Sensor Networks. Chapter 9. Network Management for Wireless Sensor Networks. Chapter 10. Operating Systems for Sensor Networks. Chapter 11. Performance and Traffic Management Issues. Appendix A: Analysis. Appendix B: Discussions. Index.

/pdf/wireless-sensor-networks-technology-protocols-and-dh5el2590j.pdf

Wireless Sensor Networks: Technology, Protocols, and Applications

The initial vision of the Internet of Things was of a world in which all physical objects are tagged and uniquely identified by RFID transponders. However, the concept has grown into multiple dimensions, encompassing sensor networks able to provide real-world intelligence and goal-oriented collaboration of distributed smart objects via local networks or global interconnections such as the Internet. Despite significant technological advances, difficulties associated with the evaluation of IoT solutions under realistic conditions in real-world experimental deployments still hamper their maturation and significant rollout. In this article we identify requirements for the next generation of IoT experimental facilities. While providing a taxonomy, we also survey currently available research testbeds, identify existing gaps, and suggest new directions based on experience from recent efforts in this field.

https://hal.inria.fr/inria-00630092/document

A survey on facilities for experimental internet of things research

ConceptNet is a knowledge representation project, providing a large semantic graph that describes general human knowledge and how it is expressed in natural language. This paper presents the latest iteration, ConceptNet 5, including its fundamental design decisions, ways to use it, and evaluations of its coverage and accuracy.

/pdf/representing-general-relational-knowledge-in-conceptnet-5-3suk5fiav1.pdf

Representing General Relational Knowledge in ConceptNet 5

As new fabrication and integration technologies reduce the cost and size of micro-sensors and wireless interfaces, it becomes feasible to deploy densely distributed wireless networks of sensors and actuators. These systems promise to revolutionize biological, earth, and environmental monitoring applications, providing data at granularities unrealizable by other means. In addition to the challenges of miniaturization, new system architectures and new network algorithms must be developed to transform the vast quantity of raw sensor data into a manageable stream of high-level data. To address this, we propose a tiered system architecture in which data collected at numerous, inexpensive sensor nodes is filtered by local processing on its way through to larger, more capable and more expensive nodes.We briefly describe Habitat monitoring as our motivating application and introduce initial system building blocks designed to support this application. The remainder of the paper presents details of our experimental platform.

Habitat monitoring: Application driver for wireless communications technology

In this paper, we argue that a microeconomic resource allocation scheme, specifically the combinatorial auction, is well suited to testbed resource management. To demonstrate this, we present the Mirage resource allocation system. In Mirage, testbed resources are allocated using a repeated combinatorial auction within a closed virtual currency environment. Users compete for testbed resources by submitting bids which specify resource combinations of interest in space/time (e.g., "any 32 MICA2 motes for 8 hours anytime in the next three days") along with a maximum value amount the user is willing to pay. A combinatorial auction is then periodically run to determine the winning bids based on supply and demand while maximizing aggregate utility delivered to users. We have implemented a fully functional and secure prototype of Mirage and have been operating it in daily use for approximately four months on Intel Research Berkeley's 148-mote sensornet testbed.

/pdf/mirage-a-microeconomic-resource-allocation-system-for-1jljm53w7u.pdf

Mirage: a microeconomic resource allocation system for sensornet testbeds

Using market mechanisms for resource allocation in distributed systems is not a new idea, nor is it one that has caught on in practice or with a large body of computer science research. Yet, projects that use markets for distributed resource allocation recur every few years [1, 2, 3], and a new generation of research is exploring market-based resource allocation mechanisms [4, 5, 6, 7, 8] for distributed environments such as Planetlab, Netbed, and computational grids.

This paper has three goals. The first goal is to explore why markets can be appropriate to use for allocation, when simpler allocation mechanisms exist. The second goal is to demonstrate why a new look at markets for allocation could be timely, and not a re-hash of previous research. The third goal is to point out some of the thorny problems inherent in market deployment and to suggest action items both for market designers and for the greater research community. We are optimistic about the power of market design, but we also believe that key challenges exist for a markets/systems integration that must be overcome for market-based computer resource allocation systems to succeed.

https://www.usenix.org/events/hotos05/final_papers/full_papers/shneidman/shneidman.pdf

Why markets could (but don't currently) solve resource allocation problems in systems

Sensor network data has both historical and realtime value. Making historical sensor data useful, in particular, requires storage, naming, and indexing. Sensor data presents new challenges in these areas. Such data is location-specific but also distributed; it is collected in a particular physical location and may be most useful there, but it has additional value when combined with other sensor data collections in a larger distributed system. Thus, arranging location-sensitive peer-to-peer storage is one challenge. Sensor data sets do not have obvious names, so naming them in a globally useful fashion is another challenge. The last challenge arises from the need to index these sensor data sets to make them searchable. The key to sensor data identity is provenance, the full history or lineage of the data. We show how provenance addresses the naming and indexing issues and then present a research agenda for constructing distributed, indexed repositories of sensor data.

/pdf/provenance-aware-sensor-data-storage-2r7brv18g8.pdf

Provenance-Aware Sensor Data Storage

While market-based systems have long been proposed as solutions for distributed resource allocation, few have been deployed for production use in real computer systems. Towards this end, we present our initial experience using Mirage, a microeconomic resource allocation system based on a repeated combinatorial auction. Mirage allocates time on a heavily-used 148-node wireless sensor network testbed. In particular, we focus on observed strategic user behavior over a four-month period in which 312,148 node hours were allocated across 11 research projects. Based on these results, we present a set of key challenges for market-based resource allocation systems based on repeated combinatorial auctions. Finally, we propose refinements to the system's current auction scheme to mitigate the strategies observed to date and also comment on some initial steps toward building an approximately strategyproof repeated combinatorial auction.

/pdf/addressing-strategic-behavior-in-a-deployed-microeconomic-18jpxiqpwt.pdf

Addressing strategic behavior in a deployed microeconomic resource allocator

We consider the problem of designing fast and strategyproof exchanges for dynamic resource allocation problems in distributed systems. The exchange is implemented as a sequence of auctions, with dynamically arriving requests from agents matched with each auction. Each auction is associated with some consignment of the resources from a single seller. We provide a simple Virtual Worlds (VW) construction, that extends a fast and strategyproof mechanism for a single auction to apply to this sequence-of-auctions setting. Rather than match each buyer with a single auction, the VW mechanism allows buyers to be considered for multiple auctions while retaining strategyproofness.

/pdf/virtual-worlds-fast-and-strategyproof-auctions-for-dynamic-1gu3arbqna.pdf

Chaki Ng

Papers

Mirage: a microeconomic resource allocation system for sensornet testbeds

Why markets could (but don't currently) solve resource allocation problems in systems

Provenance-Aware Sensor Data Storage

Addressing strategic behavior in a deployed microeconomic resource allocator

Virtual worlds: fast and strategyproof auctions for dynamic resource allocation