Identity Aware Sensor Networks

In a significant class of sensor-network applications, the identities of the reporting sensors are an essential part of the reported information. For instance, in environmental monitoring, the goal is to reconstruct physical quantities over space and time; these quantities are sampled by the sensors, and the source identity associated with each measurement is necessary for the spatial and temporal reconstruction. In many practical scenarios, source identities constitute the bulk of the communicated data, whereas the message itself can be as small as a single bit. In these scenarios, the traditional network-protocol paradigm of separately specifying the source identity and the message in distinct fields leads to inefficient communication. In this paper, we re-examine this traditional data separation and propose a scheme for joint identity-message encoding; we use this scheme to design a new efficient collection protocol for identity-aware sensor networks. Compared to conventional data collection, our protocol reduces the amount of traffic in the network at least by a factor of two (up to an order of magnitude, in lossy environments), while its performance scales better with network complexity; we show these results both through theoretical analysis and extensive simulations.

Joint Identity-Message Coding for Sensor Networks

Suhas Diggavi, Christina Fragouli, Mahdi Jafari Siavoshani, Lorenzo Keller

In a significant class of sensor-network applications, the identities of the reporting sensors constitute the bulk of the communicated data, whereas the message itself can be as small as a single bit—for instance, in many cases, sensors are used to detect whether and where a certain interesting condition occurred, or to track incremental environmental changes at fixed locations. In such scenarios, the traditional network-protocol paradigm of separately specifying the source identity and the message in distinct fields leads to inefficient communication. This work addresses the question of how communication should happen in such identity-aware sensor networks. We cal- culate theoretical performance bounds for this type of communi- cation, where “performance” refers to the number of transmitted bits. We propose a communication protocol, where the identity and message of each source are specified jointly using subspace coding. We show through analysis and simulation that our protocol’s performance is close to optimal and compare it to the performance of a traditional protocol, where identity and message are specified separately.

Institute of Electrical and Electronics Engineers2010

Reliable and Real-Time Distributed Abstractions

David Kozhaya

The celebrated distributed computing approach for building systems and services using multiple machines continues to expand to new domains. Computation devices nowadays have additional sensing and communication capabilities, while becoming, at the same time, cheaper, faster and more pervasive. Consequently, areas like industrial control, smart grids and sensor networks are increasingly using such devices to control and coordinate system operations. However, compared to classic distributed systems, such real-world physical systems have different needs, e.g., real-time and energy efficiency requirements. Moreover, constraints that govern communication are also different. Networks become susceptible to inevitable random losses, especially when utilizing wireless and power line communication. This thesis investigates how to build various fundamental distributed computing abstractions (services) given the limitations, the performance and the application requirements and constraints of real-world control, smart grid and sensor systems. In quest of completeness, we discuss four distributed abstractions starting from the level of network links all the way up to the application level. At the link level, we show how to build an energy-efficient reliable communication service. This is especially important for devices with battery-powered wireless adapters where recharging might be unfeasible. We establish transmission policies that can be used by processes to decide when to transmit over the network in order to avoid losses and minimize re-transmissions. These policies allow messages to be reliably transmitted with minimum transmission energy. One level higher than links is failure detection, a software abstraction that relies on communication for identifying process crashes. We prove impossibility results concerning implementing classic eventual failure detectors in networks with probabilistic losses. We define a new implementable type of failure detectors, which preserves modularity. This means that existing deterministic algorithms using eventual failure detectors can still be used to solve certain distributed problems in lossy networks: we simply replace the existing failure detector with the one we define. Using failure detectors, processes might get information about failures at different times. However, to ensure dependability, environments such as distributed control systems (DCSs), require a membership service where processes agree about failures in real time. We prove that the necessary properties of this membership cannot be implemented deterministically, given probabilistic losses. We propose an algorithm that satisfies these properties, with high probability. We show analytically, as well as experimentally (within an industrial DCS), that our technique significantly enhances the DCS dependability, compared to classic membership services, at low additional cost. Finally, we investigate a real-time shared memory abstraction, which vastly simplifies programming control applications. We study the feasibility of implementing such an abstraction within DCSs, showing the impossibility of this task using traditional algorithms that are built on top of existing software blocks like failure detectors. We propose an approach that circumvents this impossibility by attaching information to the failure detection messages, analyze the performance of our technique and showcase ways of adapting it to various application needs and workloads.

EPFL2016

Identity Aware Sensor Networks

Suhas Diggavi, Christina Fragouli, Mahdi Jafari Siavoshani, Lorenzo Keller

In a significant class of sensor-network applications, the identities of the reporting sensors constitute the bulk of the communicated data, whereas the message itself can be as small as a single bit---for instance, in many cases, sensors are used to detect whether and where a certain interesting condition occured, or to track incremental environmental changes at fixed locations. In such scenarios, the traditional network-protocol paradigm of separately specifying the source identity and the message in distinct fields leads to inefficient communication. This work addresses the question of how should communication happen in such identity-aware sensor networks. We re-examine the traditional source-identity/message separation and propose a scheme for jointly encoding the two. We use this to develop a communication method for identity-aware sensor networks and show it to be energy efficient, simple to implement, and gracefully adaptable to scenarios frequently encountered in sensor networks---for instance, node failures, or large numbers of nodes where only few are active during each reporting round.

2009

Reliable and Real-Time Distributed Abstractions

David Kozhaya

EPFL2016