Abstract: We survey here some recent computational models for networks of tiny artifacts. In particular, we focus on networks consisting of artifacts with sensing capabilities. We first imagine the artifacts moving passively, that is, being mobile but unable to control their own movement. This leads us to the population protocol model of Angluin et al. (2004) . We survey this model and some of its recent enhancements. In particular, we also present the mediated population protocol model in which the interaction links are capable of storing states and the passively mobile machines model in which the finite state nature of the agents is relaxed and the agents become multitape Turing machines that use a restricted space. We next survey the sensorfield model, a general model capturing some identifying characteristics of many sensor networkĘs settings. A sensorfield is composed of kinds of devices that can communicate one to the other and also to the environment through input/output data streams. We, finally, present simulation results between sensorfields and population protocols and analyze the capability of their variants to decide graph properties
Abstract: Here we survey various computational models for Wireless Sensor Networks (WSNs). The population protocol model (PP) considers networks of tiny mobile finite-state artifacts that can sense the environment and communicate in pairs to perform a computation. The mediated population protocol model (MPP) enhances the previous model by allowing the communication links to have a constant size buffer, providing more computational power. The graph decision MPP model (GDM) is a special case of MPP that focuses on the MPP's ability to decide graph properties of the network. Another direction towards enhancing the PP is followed by the PALOMA model in which the artifacts are no longer finite-state automata but Turing Machines of logarithmic memory in the population size. A different approach to modeling WSNs is the static synchronous sensorfield model (SSSF) which describes devices communicating through a fixed communication graph and interacting with their environment via input and output data streams. In this survey, we present the computational capabilities of each model and provide directions for further research.
Abstract: Wireless sensor networks are a recently introduced category of ad hoc computer networks, which are comprised by nodes of small size and limited computing and energy resources. Such nodes are able of measuring physical properties such as temperature, humidity, etc., wireless communication between each other and in some cases interaction with their surrounding environments (through the use of electromechanical parts).
As these networks have begun to be widely available (in terms of cost and commercial hardware availability), their field of application and philosophy of use is constantly evolving. We have numerous examples of their applications, ranging from monitoring the biodiversity of a specific outdoor area to structural health monitoring of bridges, and also networks ranging from few tens of nodes to even thousands of nodes.
In this PhD thesis we investigated the following basic research lines related to wireless sensor networks:
a) their simulation,
b) the development of data propagation protocols suited to such networks and their evaluation through simulation,
c) the modelling of ``hostile'' circumstances (obstacles) during their operation and evaluation of their impact through simulation,
d) the development of a sensor network management application.
Regarding simulation, we initially placed an emphasis to issues such as the effective simulation of networks of several thousands of nodes, and in that respect we developed a network simulator (simDust), which is extendable through the addition of new data propagation protocols and visualization capabilities. This simulator was used to evaluate the performance of a number of characteristic data propagation protocols for wireless sensor networks. Furthermore, we developed a new protocol (VRTP) and evaluated its performance against other similar protocols. Our studies show that the new protocol, that uses dynamic changes of the transmission range of the network nodes, performs better in certain cases than other related protocols, especially in networks containing obstacles and in the case of non-homogeneous placement of nodes.
Moreover, we emphasized on the addition of ``realistic'' conditions to the simulation of such protocols, that have an adversarial effect on their operation. Our goal was to introduce a model for obstacles that adds little computational overhead to a simulator, and also study the effect of the inclusion of such a model on data propagation protocols that use geographic information (absolute or relative). Such protocols are relatively sensitive to dynamic topology changes and network conditions. Through our experiments, we show that the inclusion of obstacles during simulation can have a significant effect on these protocols.
Finally, regarding applications, we initially proposed an architecture (WebDust/ShareSense), for the management of such networks, that would provide basic capabilities of managing such networks and developing applications above it. Features that set it apart are the capability of managing multiple heterogeneous sensor networks, openess, the use of a peer-to-peer architecture for the interconnection of multiple sensor network. A large part of the proposed architecture was implemented, while the overall architecture was extended to also include additional visualization capabilities.
Abstract: Wireless sensor networks are comprised of a vast number of devices, situated in an area of interest that self organize in a structureless network, in order to monitor/record/measure an environmental variable or phenomenon and subsequently to disseminate the data to the control center.
Here we present research focused on the development, simulation and evaluation of energy efficient algorithms, our basic goal is to minimize the energy consumption. Despite technology advances, the problem of energy use optimization remains valid since current and emerging hardware solutions fail to solve it.
We aim to reduce communication cost, by introducing novel techniques that facilitate the development of new algorithms. We investigated techniques of distributed adaptation of the operations of a protocol by using information available locally on every node, thus through local choices we improve overall performance. We propose techniques for collecting and exploiting limited local knowledge of the network conditions. In an energy efficient manner, we collect additional information which is used to achieve improvements such as forming energy efficient, low latency and fault tolerant paths to route data. We investigate techniques for managing mobility in networks where movement is a characteristic of the control center as well as the sensors. We examine methods for traversing and covering the network field based on probabilistic movement that uses local criteria to favor certain areas.
The algorithms we develop based on these techniques operate a) at low level managing devices, b) on the routing layer and c) network wide, achieving macroscopic behavior through local interactions. The algorithms are applied in network cases that differ in density, node distribution, available energy and also in fundamentally different models, such as under faults, with incremental node deployment and mobile nodes. In all these settings our techniques achieve significant gains, thus distinguishing their value as tools of algorithmic design.
Abstract: We present the NanoPeers architecture paradigm, a
peer-to-peer network of lightweight devices, lacking all or
most of the capabilities of their computer-world counterparts.
We identify the problems arising when we apply current
routing and searching methods to this nano-world, and
present some initial solutions, using a case study of a sensor
network instance; Smart Dust. Furthermore, we propose
the P2P Worlds framework as a hybrid P2P architecture
paradigm, consisting of cooperating layers of P2P
networks, populated by computing entities with escalating
capabilities. Our position is that (i) experience gained
through research and experimentation in the field of P2P
computing, can be indispensable when moving down the
stair of computing capabilities, and that (ii) the proposed
framework can be the basis of numerous real-world applications,
opening up several challenging research problems.
Abstract: In this work, we discuss various aspects of the application of pervasive technologies inside an urban setting. In the last decade we have seen the emergence of a multitude of closely-related pervasive technologies that have only recently started to materialize on a grand scale, such as wireless sensor networks, RFID and NFC. We discuss the arising research challenges associated with such converging fields and we also provide a survey of the state-of-the-art related application scenaria, which we believe set their near-future applied context. Finally, we provide a more analytic discussion on three discrete systems that belong to this category of applications and give insight to the current state-of-the-art work in this field.
Abstract: The Internet of Things (IoT) and smart cities are two of the most popular directions the research community is pursuing very actively. But although we have made great progress in many fields, we are still trying to figure out how we can utilize our smart city and IoT infrastructures, in order to produce reliable, economically sustainable solutions that create public value, and even more so in the field of education.
GAIA1, a Horizon2020 EC-funded project, has developed an IoT infrastructure across school buildings in Europe. Its primary aim has been to raise awareness about energy consumption and sustainability, based on real-world sensor data produced inside the school buildings where students and teachers live and work. Today's students are the citizens of tomorrow, and they should have the skills to understand and respond to challenges like climate change. Currently, 25 educational building sites participate in GAIA, located in Sweden, Italy, and Greece. An IoT infrastructure  is installed in these buildings, monitoring in real-time their power consumption, as well as several indoor and outdoor environmental parameters.