Project URL: http://www.bionets.org
Short Description: The motivation for
BIONETS comes from emerging trends towards pervasive computing and communication
environments, where myriads of networked devices with very different features
will enhance our five senses, our communication and tool manipulation
capabilities. The complexity of such environments will not be far from that of
biological organisms, ecosystems, and socio-economic communities. Traditional
communication approaches are ineffective in this context, since they fail to
address several new features: a huge number of nodes including low-cost
sensing/identifying devices, a wide heterogeneity in node capabilities, high
node mobility, the management complexity, the possibility of exploiting spare
node resources. BIONETS aims at a novel approach able to address these
challenges. Nature and society exhibit many instances of systems in which large
populations are able to reach efficient equilibrium states and to develop
effective collaboration and survival strategies, able to work in the absence of
central control and to exploit local interactions. We seek inspiration from
these systems to provide a fully integrated network and service environment that
scales to large amounts of heterogeneous devices, and that is able to adapt and
evolve in an autonomic way. BIONETS overcomes device heterogeneity and achieves
scalability via an autonomic and localized peer-to-peer communication paradigm.
Services in BIONETS are also autonomic, and evolve to adapt to the surrounding
environment, like living organisms evolve by natural selection.
Biologically-inspired concepts permeate the network and its services, blending
them together, so that the network moulds itself to the services it runs, and
services, in turn, become a mirror image of the social networks of users they
serve. This new paradigm breaks the barrier between service providers and users,
and sets up the opportunity for "mushrooming" of spontaneous services, therefore
paving the way to a service-centric ICT revolution.
Participating Organizations : Create-Net, Consiglio Nazionale delle Ricerche, University of Trento, Technion, University of Basel, Technische Universitaet Berlin, Hamburger Informatik Technologie-Center, RWTH Aachen University, Budapest University of Technologie and Economics, Nokia Corporation, Valtion Tenillinen Tuktimuskeskus, Institut National de Recherche en Informatique et Automatique, National and Kapodistrian University of Athens, Telecom Italia, London School of Economics and Political Science, Sun Microsystems Iberica SA.
Funding Agency : European Commission (Future and Emerging Technologies)
Period : 2006 - 2009
In the BIONETS architecture there are two different topologies; the one created by T-nodes (usually stationary) that generate data and the mobile U-nodes that collect this data, and the one configured only by the mobile U-nodes exchanging data between each other. T-nodes have limited capabilities and energy resources to spend and may be considered as the service value teller regarding a specific area of the network. Multiple services may be provided and different information for each one may be given based on the interest of the U-node asking for that specific information. The T-nodes are numerous covering the whole area of the network providing a wide range of information pieces to the U-nodes. The U-nodes after having read the information provided by the T-nodes undertake the task of information dissemination. The topological characteristics of the network and the mobility pattern of the nodes along with their communication capabilities and energy resources raise several restrictions on the achievable performance. Routing within the BIONETS framework should comply with the peculiarities of the novel architecture that may be assumed to share similarities with that of a Delay Tolerant Network. The routing mechanisms considered for Delay Tolerant Networks (DTNs), where typically no contemporaneous path between a potential source-destination pair exists, achieve message (data) delivery by carrying and opportunistically relaying it at each node encounter, either by using a single copy or multiple copies of the message. Some of the topics on which we have focused are the following:
Message spreading in Delay Tolerant Networks: We focus on the two-hop relay algorithm, where the source node relays a copy of the message to every node it encounters; the intermediate nodes that acquire a copy of the message are allowed to forward the copy only to the destination and, thus, one hop or, maximum, two hops of communication are employed for the message delivery. The performance of the two-hop relay algorithm has been studied in the past, for a homogeneous network, in terms of the expected value and variance of the delivery delay as well as of the expected number of transmissions until the delivery of the message. Here, a more general study of the performance of the algorithm is provided with respect to the setting that is considered as well as the metrics that are examined. The setting that is considered in this work allows for a differentiation between the source of the message and the intermediate nodes (in terms of e.g. transmission power or speed). In addition, instead of allowing the source to relay the message to all the intermediate nodes that it encounters, the number of copies allowed to be spread in the network is treated here as a design parameter. The delivery delay is fully characterized by extracting its cdf (cumulative distribution function). Moreover, an approximate approach is proposed that leads to a fairly accurate and much simpler expression for the cdf. In addition, the number of transmissions is considered not only upon the delivery of the message, but until the actual termination of the algorithm, which takes place when the source becomes aware of the delivery. (When the message is delivered to the destination by some intermediate node, the source continues to forward the rest of the copies of the message allowed to be spread until it becomes aware of the successful delivery and, thus, the number of transmissions until message delivery is only a fraction of the total number of transmissions that will eventually take place.) For all the above derivations, we assume that each message has a certain delay bound upon the expiration of which the message is dropped. This delay bound might be considered either as an application-specific characteristic (e.g. a calendar-related message or a meeting reminder that are expected to be delay-bounded) or as a design parameter of the algorithm (in order, for instance, to limit the spreading of copies by determining an appropriate delay bound for the messages within which their delivery is expected).
Cooperation in Delay Tolerant Networks: The performance of the various DTN routing algorithms proposed in the literature has been investigated so far with respect to the impact of the characteristics of the environment, e.g. the size of the area where the network is deployed and the node density, or the employed message spreading algorithm. Node behavior has been rarely considered, besides node mobility, and in all the studies it has been assumed that the nodes cooperate fully. The latter can be a fairly unrealistic assumption, as the participating nodes are autonomic, in the sense that they can decide on their own whether to implement or not the rules of a DTN routing algorithm. However, it is expected that the degree of node cooperation in a DTN would have a major impact on the performance of a DTN routing algorithm. It may be the case that if cooperation is not guaranteed, efficient DTN routing protocols almost collapse or yield a very poor performance while less efficient protocols are only marginally affected. Here, three representative routing algorithms are considered, ranging from a conservative scheme where only the source node is responsible for spreading the message copies within the network, to a fully-aggressive scheme that floods the network with message copies. We study the performance of these algorithms in terms of the induced delivery delay and the transmission overhead; transmissions are considered not only until message delivery but also until the actual message spreading is ended. Cooperation is captured in terms of probability that a node drops a message copy upon reception and/or forwards the message copy upon node encounter. By considering a simple strategy that takes into consideration the nodes cooperation degree, it is demonstrated how one can alleviate the effects of non-cooperative behavior in DTNs.
Cooperative Replication Schemes: In a Bionets environment, U-nodes are often interested in retrieving information objects from T-nodes that are far apart. This induces a high access cost, expressed in terms of energy and access delay. U-nodes may decrease their access costs by collaborating with each other and replicating objects not only in their own interest, but also in the interest of other U-nodes. Then they can take advantage of mutual encounters to retrieve objects that interest them at a much smaller cost. In this context, we study cooperative replication schemes with game-theoretic methods. Parameters that are involved in the problem are the mobility patterns of nodes and their request functions for objects. A crucial issue that needs to be captured is the depreciation of the information value of objects to users, both in time and space. That is, the older the information is, or the farther a U-node is heading from the source of the information, the less valuable the information is to it. Although communication and cooperation must exist to some degree in the background setting, we mainly study noncooperative and noncommunicative strategies, taking into account the interrupted nature of communications between mobile nodes, and hence the difficulty to establish reliable coalitions. The derived strategies must be easily implementable, and incur a smaller access cost to all nodes than their access cost if they act independently.