Abstract: We here present the Forward Planning Situated Protocol (FPSP), for scalable, energy efficient and fault tolerant data propagation in situated wireless sensor networks. To deal with the increased complexity of such deeply networked sensor systems, instead of emphasizing on a particular aspect of the services provided, i.e. either for low-energy periodic, or low-latency event-driven, or high-success query-based sensing, FPSP uses two novel mechanisms that allow the network operator to adjust the performance of the protocol in terms of energy, latency and success rate on a per-task basis. We emphasize on distributedness, direct or indirect interactions among relatively simple agents, flexibility and robustness.
The protocol operates by employing a series of plan & forward phases through which devices self-organize into forwarding groups that propagate data over discovered paths. FPSP performs a limited number of long range, high power data transmissions to collect information regarding the neighboring devices. The acquired information, allows to plan a (parameterizable long by {\"e}) sequence of short range, low power transmissions between nearby particles, based on certain optimization criteria. All particles that decide to respond (based on local criteria) to these long range transmissions enter the forwarding phase during which information is propagated via the acquired plan. Clearly, the duration of the forwarding phases is characterized by the parameter {\"e}, the transmission medium and the processing speed of the devices. In fact the parameter {\"e} provides a mechanism to adjust the protocol performance in terms of the latency--energy trade-off. By reducing {\"e} the latency is reduced at the cost of spending extra energy, while by increasing {\"e}, the energy dissipation is reduced but the latency is increased.
To control the success rate--energy trade-off, particles react locally on environment and context changes by using a set of rules that are based on response thresholds that relate individual-level plasticity with network-level resiliency, motivated by the nature-inspired method for dividing labor, a metaphor of social insect behavior for solving problems [1]. Each particle has an individual response threshold {\`E} that is related to the "local" density (as observed by the particle, [2]); particles engage in propagation of events when the level of the task-associated stimuli exceeds their thresholds. Let s be the intensity of a stimulus associated with a particular sensing task, set by the human authorities. We adopt the response function T{\`e}(s) = snover sn + {\`e}n, the probability of performing the task as a function of s, where n > 1 determines the steepness of the threshold. Thus, when {\`e} is small (i.e. the network is sparse) then the response probability increases; when s increases (i.e. for critical sensing tasks) the response probability increases as well.
This role-based approach where a selective number of devices do the high cost planning and the rest of the network operates in a low cost state leads to systems that have increased energy efficiency and high fault-tolerance since these long range planning phases allow to bypass obstacles (where no sensors are available) or faulty sensors (that have been disabled due to power failure or other natural events).
Abstract: The technological as well as software advances in
microelectronics and embedded component design have led to the
development of low cost, small-sized devices capable of forming
wireless, ad-hoc networks and sensing a number of qualities of
their environment, while performing computations that depend
on the sensed qualities as well as information received by their
peers. These sensor networks rely on the collective power of
the separate devices as well as their computational and sensing
capabilities to understand "global" environmental states through
locally sampled information and local sensor interactions. Due
to the locality of the sensor networks, that naturally arises due
to the locality of their communications capabilities, a number
of interesting connections exist between these networks and
geometrical concepts and problems. In this paper we study two
simple problems that pertain to the formation of low power
and low interference communication patterns in fixed topology
sensor networks. We study the problem of using multihop
communication links instead of direct ones as well as the problem
of forming a communication ring of sensor networks so as to
reduce power consumption as well as interference from other
nodes. Our focus is on the connection between sensor networks
and geometrical concepts, rather than on practicality, so as to
highlight their interrelationship.
Abstract: Internet of Things technologies are considered the next big
step in Smart Building installations. Although such technologies have
been widely studied in simulation and experimental scenarios it is not so
obvious how problems of real world installations should be dealt with. In
this work we deploy IoT devices for sensing and control in a multi-office
space and employ technologies such as CoAP, RESTful interfaces and
Semantic Descriptions to integrate them with the Web. We report our
research goals, the challenges we faced, the decisions we made and the
experience gained from the design, deployment and operation of all the
hardware and software components that compose our system.
Abstract: Recent rapid developments in micro-electro-mechanical systems
(MEMS), wireless communications and digital electronics have already
led to the development of tiny, low-power, low-cost sensor devices.
Such devices integrate sensing, limited data processing and restricted
communication capabilities.
Each sensor device individually might have small utility, however the
effective distributed co-ordination of large numbers of such devices can
lead to the efficient accomplishment of large sensing tasks. Large numbers
of sensors can be deployed in areas of interest (such as inaccessible
terrains or disaster places) and use self-organization and collaborative
methods to form an ad-hoc network.
We note however that the efficient and robust realization of such large,
highly-dynamic, complex, non-conventional networking environments is
a challenging technological and algorithmic task, because of the unique
characteristics and severe limitations of these devices.
This talk will present and discuss several important aspects of the
design, deployment and operation of sensor networks. In particular, we
provide a brief description of the technical specifications of state-of-theart
sensor, a discussion of possible models used to abstract such networks,
a discussion of some key algorithmic design techniques (like randomization,
adaptation and hybrid schemes), a presentation of representative
protocols for sensor networks, for important problems including data
propagation, collision avoidance and energy balance and an evaluation
of crucial performance properties (correctness, efficiency, fault-tolerance)
of these protocols, both with analytic and simulation means.
Abstract: In emerging pervasive scenarios, data is collected by sensing devices in streams that occur at several distributed points of observation. The size of the data typically far exceeds the storage and computational capabilities of the tiny devices that have to collect and process them. A general and challenging task is to allow (some of) the nodes of a pervasive network to collectively perform monitoring of a neighbourhood of interest by issuing continuous aggregate queries on the streams observed in its vicinity. This class of algorithms is fully decentralized and diffusive in nature: collecting all the data at a few central nodes of the network is unfeasible in networks of low capability devices or in the presence of massive data sets. Two main problems arise in this scenario: (i) the intrinsic complexity of maintaining statistics over a data stream whose size greatly exceeds the capabilities of the device that performs the computation; (ii) composing the partial outcomes computed at different points of observation into an accurate, global statistic over a neighbourhood of interest, which entails coping with several problems, last but not least the receipt of duplicate information along multiple paths of diffusion.
Streaming techniques have emerged as powerful tools to achieve the general goals described above, in the first place because they assume a computational model in which computational and storage resources are assumed to be far exceeded by the amount of data on which computation occurs. In this contribution, we review the main streaming techniques and provide a classification of the computational problems and the applications they effectively address, with an emphasis on decentralized scenarios, which are of particular interest in pervasive networks