Abstract: We study the problem of data propagation in sensor networks,
comprised of a large number of very small and low-cost nodes,
capable of sensing, communicating and computing. The distributed
co-operation of such nodes may lead to the accomplishment of large
sensing tasks, having useful applications in practice. We present
a new protocol for data propagation towards a control center
(``sink") that avoids flooding by probabilistically favoring
certain (``close to optimal") data transmissions.
This protocol is very simple to implement in sensor devices
and operates under total absence
of co-ordination between sensors. We consider a network model of randomly deployed sensors of sufficient density.
As shown by a geometry analysis,
the protocol is correct, since it always propagates data
to the sink, under ideal network conditions (no failures). Using
stochastic processes, we show that the protocol is very energy efficient. Also, when part of the network is inoperative, the
protocol manages to propagate data very close to the sink, thus in
this sense it is robust. We finally present and discuss
large-scale experimental findings validating the analytical
results.
Abstract: We study the problem of data propagation in sensor networks,
comprised of a large number of very small and low-cost nodes,
capable of sensing, communicating and computing. The distributed
co-operation of such nodes may lead to the accomplishment of large
sensing tasks, having useful applications in practice. We present a new protocol for data propagation towards a control center ("sink") that avoids flooding by probabilistically favoring certain ("close to optimal") data transmissions. Motivated by certain applications and also as a starting point for a rigorous analysis, we study here lattice-shaped sensor networks. We however show that this lattice shape emerges even in randomly deployed sensor networks of sufficient sensor density. Our work is inspired and builds upon the directed diffusion paradigm.
This protocol is very simple to implement in sensor devices, uses only local information and operates under total absence of co-ordination between sensors. We consider a network model of randomly deployed sensors of sufficient density. As shown by a geometry analysis, the protocol is correct, since it always propagates data to the sink, under ideal network conditions (no failures). Using stochastic processes, we show that the protocol is very energy efficient. Also, when part of the network is inoperative, the protocol manages to propagate data very close to the sink, thus in this sense it is robust. We finally present and discuss large-scale experimental findings validating the analytical results.
Abstract: In this paper we study the problem of basic communication
in ad-hoc mobile networks where the deployment area changes in a
highly dynamic way and is unknown. We call such networks
highly changing ad-hoc mobile networks.
For such networks we investigate an efficient communication protocol which extends
the idea (introduced in [WAE01,POMC01]) of exploiting the co-ordinated
motion of a small part of an ad-hoc mobile
network (the ``runners support") to achieve
very fast communication between any two mobile users of the network.
The basic idea of the new protocol presented here is, instead
of using a fixed sized support for the whole duration of the protocol,
to employ a support of some initial (small) size which
adapts (given some time which can be made fast enough) to the
actual levels of traffic and the
(unknown and possibly rapidly changing) network area by
changing its size in order to converge to an optimal size,
thus satisfying certain Quality of Service criteria.
We provide here some proofs of correctness and fault tolerance
of this adaptive approach and we also provide analytical results
using Markov Chains and random walk techniques to show that such
an adaptive approach is, for this class of ad-hoc mobile networks, significantly more efficient than a simple non-adaptive
implementation of the basic ``runners support" idea.
Abstract: Wireless Sensor Networks are complex systems consisting of a number of relatively simple autonomous sensing devices spread on a geographical area. The peculiarity of these devices lies on the constraints they face in relation to their energy reserves and their computational, storage and communication capabilities. The utility of these sensors is to measure certain environmental conditions and to detect critical events in relation to these measurements. Those events thereupon have to be reported to a specific central station namely the “sink”. This data propagation generally has the form of a hop-by-hop transmission. In this framework we work on distributed data propagation protocols which are taking into account the energy reserves of the sensors. In particular following the work of Chatzigiannakis et al. on the Probabilistic Forwarding Protocol (PFR) we present the distributed probabilistic protocol EFPFR, which favors transmission from the less depleted sensors in addition to favor transmissions close to the “optimal line”. This protocol is simple and relies only on local information for propagation decisions. Its main goal is to limit the total amount of energy dissipated per event and therefore to extend the network’s operation duration.
Abstract: In this work, we study the propagation of influence and computation in dynamic networks that are possibly disconnected at every instant. We focus on a synchronous message passing communication model with broadcast and bidirectional links. To allow for bounded end-to-end communication we propose a set of minimal temporal connectivity conditions that bound from the above the time it takes for information to make progress in the network. We show that even in dynamic networks that are disconnected at every instant information may spread as fast as in networks that are connected at every instant. Further, we investigate termination criteria when the nodes know some upper bound on each of the temporal connectivity conditions. We exploit our termination criteria to provide efficient protocols (optimal in some cases) that solve the fundamental counting and all-to-all token dissemination (or gossip) problems. Finally, we show that any protocol that is correct in instantaneous connectivity networks can be adapted to work in temporally connected networks.
Abstract: In this work, we study the propagation of influence and computation in dynamic distributed computing systems that are possibly disconnected at every instant. We focus on a synchronous message-passing communication model with broadcast and bidirectional links. Our network dynamicity assumption is a worst-case dynamicity controlled by an adversary scheduler, which has received much attention recently. We replace the usual (in worst-case dynamic networks) assumption that the network is connected at every instant by minimal temporal connectivity conditions. Our conditions only require that another causal influence occurs within every time window of some given length. Based on this basic idea, we define several novel metrics for capturing the speed of information spreading in a dynamic network. We present several results that correlate these metrics. Moreover, we investigate termination criteria in networks in which an upper bound on any of these metrics is known. We exploit our termination criteria to provide efficient (and optimal in some cases) protocols that solve the fundamental counting and all-to-all token dissemination (or gossip) problems.
Abstract: We study the problem of energy-balanced data propagation in wireless sensor networks. The energy balance property is crucial for maximizing the time the network is functional, by avoiding early energy depletion of a large portion of sensors. We propose a distributed, adaptive data propagation algorithm that exploits limited, local network density information for achieving energy-balance while at the same time
minimizing energy dissipation.
We investigate both uniform and heterogeneous sensor placement distributions. By a detailed experimental evaluation and comparison with well-known energy-balanced protocols, we show that our density-based protocol improves energy efficiency signi cantly while also having better energy balance properties.
Furthermore, we compare the performance of our protocol with a centralized, o -line optimum solution derived by a linear program which maximizes the network lifetime and show that it achieves near-optimal performance for uniform sensor deployments.
Abstract: In this chapter, our focus is on computational network analysis from a theoretical point of view. In particular, we study the \emph{propagation of influence and computation in dynamic distributed computing systems}. We focus on a \emph{synchronous message passing} communication model with bidirectional links. Our network dynamicity assumption is a \emph{worst-case dynamicity} controlled by an adversary scheduler, which has received much attention recently. We first study the fundamental \emph{naming} and \emph{counting} problems (and some variations) in
networks that are \emph{anonymous}, \emph{unknown}, and possibly dynamic. Network dynamicity is modeled here by the \emph{1-interval connectivity model}, in which communication is synchronous and a (worst-case) adversary
chooses the edges of every round subject to the condition that each instance is connected. We then replace this quite strong assumption by minimal \emph{temporal connectivity} conditions. These conditions only require that \emph{another causal influence occurs within every time-window of some given length}. Based on this basic idea we define several novel metrics for capturing the speed of information spreading in a dynamic network. We present several results that correlate these metrics. Moreover, we investigate \emph{termination criteria} in networks in which an upper bound on any of these metrics is known. We exploit these termination criteria to provide efficient (and optimal in some cases) protocols that solve the fundamental \emph{counting} and \emph{all-to-all token dissemination} (or \emph{gossip}) problems. Finally, we propose another model of worst-case temporal connectivity, called \emph{local
communication windows}, that assumes a fixed underlying communication network and restricts the adversary to allow communication between local neighborhoods in every time-window of some fixed length. We prove some basic properties and provide a protocol for counting in this model.
Abstract: In this work we introduce two practical and interesting models of ad-hoc mobile networks: (a) hierarchical ad-hoc networks, comprised of dense subnetworks of mobile users interconnected by a very fast yet limited backbone infrastructure, (b) highly changing ad-hoc networks, where the deployment area changes in a highly dynamic way and is unknown to the protocol. In such networks, we study the problem of basic communication, i.e., sending messages from a sender node to a receiver node. For highly changing networks, we investigate an efficient communication protocol exploiting the coordinated motion of a small part of an ad-hoc mobile network (the ldquorunners supportrdquo) to achieve fast communication. This protocol instead of using a fixed sized support for the whole duration of the protocol, employs a support of some initial (small) size which adapts (given some time which can be made fast enough) to the actual levels of traffic and the (unknown and possibly rapidly changing) network area, by changing its size in order to converge to an optimal size, thus satisfying certain Quality of Service criteria. Using random walks theory, we show that such an adaptive approach is, for this class of ad-hoc mobile networks, significantly more efficient than a simple non-adaptive implementation of the basic ldquorunners supportrdquo idea, introduced in [9,10]. For hierarchical ad-hoc networks, we establish communication by using a ldquorunnersrdquo support in each lower level of the hierarchy (i.e., in each dense subnetwork), while the fast backbone provides interconnections at the upper level (i.e., between the various subnetworks). We analyze the time efficiency of this hierarchical approach. This analysis indicates that the hierarchical implementation of the support approach significantly outperforms a simple implementation of it in hierarchical ad-hoc networks. Finally, we discuss a possible combination of the two approaches above (the hierarchical and the adaptive ones) that can be useful in ad-hoc networks that are both hierarchical and highly changing. Indeed, in such cases the hierarchical nature of these networks further supports the possibility of adaptation.
Abstract: We study here the problem of determining the majority type in an arbitrary connected network, each vertex of which has initially two possible types. The vertices may have a few additional possible states and can interact in pairs only if they share an edge. Any (population) protocol is required to stabilize in the initial majority. We first present and analyze a protocol with 4 states per vertex that always computes the initial majority value, under any fair scheduler. As we prove, this protocol is optimal, in the sense that there is no population protocol that always computes majority with fewer than 4 states per vertex. However this does not rule out the existence of a protocol with 3 states per vertex that is correct with high probability. To this end, we examine a very natural majority protocol with 3 states per vertex, introduced in [Angluin et al. 2008] where its performance has been analyzed for the clique graph. We study the performance of this protocol in arbitrary networks. We prove that, when the two initial states are put uniformly at random on the vertices, this protocol converges to the initial majority with probability higher than the probability of converging to the initial minority. In contrast, we present an infinite family of graphs, on which the protocol can fail whp, even when the difference between the initial majority and the initial minority is n−Θ(lnn). We also present another infinite family of graphs in which the protocol of Angluin et al. takes an expected exponential time to converge. These two negative results build upon a very positive result concerning the robustness of the protocol on the clique. Surprisingly, the resistance of the clique to failure causes the failure in general graphs. Our techniques use new domination and coupling arguments for suitably defined processes whose dynamics capture the antagonism between the states involved.
Abstract: We consider the following distributed optimization problem: three agents i =
1; 2; 3 are each presented with a load drawn independently from the same known prior distribution.
Then each agent decides on which of two available bins to put her load. Each bin has
capacity �, and the objective is to find a distributed protocol that minimizes the probability
that an overflow occurs (or, equivalently, maximizes the winning probability).
In this work, we focus on the cases of full information and local information, depending on
whether each agent knows the loads of both other agents or not. Furthermore, we distinguish
between the cases where the agents are allowed to follow different decision rules (eponymous
model) or not (anonymous model ). We assume no communication among agents.
First, we present optimalprotocols for the full information case, for both the anonymous and
the eponymous model.
For the local information, anonymous case, we show that the winning probability is upper
bounded by 0.622 in the case where the input loads are drawn from the uniform distribution.
Motivated by [3], we present a general method for computing the optimal single-threshold protocol
for any continuous distribution, and we apply this method to the case of the exponential
distribution.
Finally, we show how to compute, in exponential time, an optimalprotocol for the local
information, eponymous model for the case where the input loads are drawn from a discretevalued,
bounded distribution.
Abstract: Geographic routing is becoming the protocol of choice for many sensor network applications. Some very efficient geographic routing algorithms exist, however they require a preliminary planarization of the communication graph. Planarization induces overhead which makes this approach not optimal when lightweight protocols are required. On the other hand, georouting algorithms which do not rely on planarization have fairly low success rates and either fail to route messages around all but the simplest obstacles or have a high topology control overhead (e.g. contour detection algorithms). In this entry we describe the GRIC algorithm which was designed to overcome some of those limitations. The GRIC algorithm was proposed in [PN07a]. It is the first lightweight and efficient on demand (i.e. all-to-all) geographic routing algorithm which does not require planarization, has almost 100% delivery rates (when no obstacles are added), and behaves well in the presence of large communication blocking obstacles.
Abstract: We propose local mechanisms for efficiently marking the broader network region around obstacles, for data propagation to early enough avoid them towards near-optimal routing paths. In particular, our methods perform an online identification of sensors lying near obstacle boundaries,which then appropriately emit beacon messages in the network towards establishing efficient obstacle avoidance paths. We provide a variety of beacon dissemination schemes that satisfy different trade-offs between protocol overhead and performance. Compared to greedy, face routing and trustbased methods in the state of the art, our methods achieve significantly shorter propagation paths, while introducing much lower overhead and converging faster to near-optimality.
Abstract: Geographic routing scales well in sensor networks, mainly
due to its stateless nature. Still, most of the algorithms are
concerned with finding some path, while the optimality of
the path is difficult to achieve. In this paper we are presenting
a novel geographic routing algorithm with obstacle
avoidance properties. It aims at finding the optimal path
from a source to a destination when some areas of the network
are unavailable for routing due to low local density or
obstacle presence. It locally and gradually with time (but,
as we show, quite fast) evaluates and updates the suitability
of the previously used paths and ignores non optimal paths
for further routing. By means of extensive simulations, we
are comparing its performance to existing state of the art
protocols, showing that it performs much better in terms of
path length thus minimizing latency, space, overall traffic
and energy consumption.
Abstract: We propose local mechanisms for efficiently marking the
broader network region around obstacles, for data propagation
to early enough avoid them towards near-optimal
routing paths. In particular, our methods perform an online
identification of sensors lying near obstacle boundaries,
which then appropriately emit beacon messages in the network
towards establishing efficient obstacle avoidance paths.
We provide a variety of beacon dissemination schemes that
satisfy different trade-offs between protocol overhead and
performance. Compared to greedy, face routing and trustbased
methods in the state of the art, our methods achieve
significantly shorter propagation paths, while introducing
much lower overhead and converging faster to near-optimality.
Abstract: We investigate the problem of how to achieve energy balanced data propagation in distributed wireless sensor networks. The energy balance property guarantees that the average per sensor energy dissipation is the same for all sensors in the network, throughout the execution of the data propagation protocol. This property is crucial for prolonging the network lifetime, by avoiding early energy depletion of sensors.
We survey representative solutions from the state of the art. We first present a basic algorithm that in each step probabilistically decides whether to propagate data one-hop towards the final destination (the sink), or to send it directly to the sink. This randomized choice trades-off the (cheap, but slow) one-hop transmissions with the direct transmissions to the sink, which are more expensive but bypass the bottleneck region around the sink and propagate data fast. By a detailed analysis using properties of stochastic processes and recurrence relations we precisely estimate (even in closed form) the probability for each propagation option necessary for energy balance.
The fact (shown by our analysis) that direct (expensive) transmissions to the sink are needed only rarely, shows that our protocol, besides energy balanced, is also energy efficient. We then enhance this basic result by surveying some recent findings including a generalized algorithm and demonstrating the optimality of this two-way probabilistic data propagation, as well as providing formal proofs of the energy optimality of the energy balance property.
Abstract: We propose, implement and evaluate new energy conservation schemes for efficient data propagation in wireless sensor networks. Our protocols are adaptive, i.e. locally monitor the network conditions and accordingly adjust towards optimal operation choices. This dynamic feature is particularly beneficial in heterogeneous settings and in cases of redeployment of sensor devices in the network area. We implement our protocols and evaluate their performance through a detailed simulation study using our extended version of ns-2. In particular we combine our schemes with known communication paradigms. The simulation findings demonstrate significant gains and good trade-offs in terms of delivery success, delay and energy dissipation.
Abstract: In this work, we study protocols so that populations of distributed processes can construct networks. In order to highlight the basic principles of distributed network construction, we keep the model minimal in all respects. In particular, we assume finite-state processes that all begin from the same initial state and all execute the same protocol. Moreover, we assume pairwise interactions between the processes that are scheduled by a fair adversary. In order to allow processes to construct networks, we let them activate and deactivate their pairwise connections. When two processes interact, the protocol takes as input the states of the processes and the state of their connection and updates all of them. Initially all connections are inactive and the goal is for the processes, after interacting and activating/deactivating connections for a while, to end up with a desired stable network. We give protocols (optimal in some cases) and lower bounds for several basic network construction problems such as spanning line, spanning ring, spanning star, and regular network. The expected time to convergence of our protocols is analyzed under a uniform random scheduler. Finally, we prove several universality results by presenting generic protocols that are capable of simulating a Turing Machine (TM) and exploiting it in order to construct a large class of networks. We additionally show how to partition the population into k supernodes, each being a line of log k nodes, for the largest such k. This amount of local memory is sufficient for the supernodes to obtain unique names and exploit their names and their memory to realize nontrivial constructions.
Abstract: In this work, we study protocols (i.e. distributed algorithms) so that populations of distributed processes can construct networks. In order to highlight the basic principles of distributed network construction we keep the model minimal in all respects. In particular, we assume finite-state processes that all begin from the same initial state and all execute the same protocol (i.e. the system is homogeneous). Moreover, we assume pairwise interactions between the processes that are scheduled by an adversary. The only constraint on the adversary scheduler is that it must be fair, intuitively meaning that it must assign to every reachable configuration of the system a non-zero probability to occur. In order to allow processes to construct networks, we let them activate and deactivate their pairwise connections. When two processes interact, the protocol takes as input the states of the processes and the state of their connection and updates all of them. In particular, in every interaction, the protocol may activate an inactive connection, deactivate an active one, or leave the state of a connection unchanged. Initially all connections are inactive and the goal is for the processes, after interacting and activating/deactivating connections for a while, to end up with a desired stable network (i.e. one that does not change any more). We give protocols (optimal in some cases) and lower bounds for several basic network construction problems such as spanning line, spanning ring, spanning star, and regular network. We provide proofs of correctness for all of our protocols and analyze the expected time to convergence of most of them under a uniform random scheduler that selects the next pair of interacting processes uniformly at random from all such pairs. Finally, we prove several universality results by presenting generic protocols that are capable of simulating a Turing Machine (TM) and exploiting it in order to construct a large class of networks. Our universality protocols use a subset of the population (waste) in order to distributedly construct there a TM able to decide a graph class in some given space. Then, the protocols repeatedly construct in the rest of the population (useful space) a graph equiprobably drawn from all possible graphs. The TM works on this and accepts if the presented graph is in the class. We additionally show how to partition the population into k supernodes, each being a line of log k nodes, for the largest such k. This amount of local memory is sufficient for the supernodes to obtain unique names and exploit their names and their memory to realize nontrivial constructions. Delicate composition and reinitialization issues have to be solved for these general constructions to work.
Abstract: One of the most eminent problems in sensor networks is the
routing of data to a central destination in a robust and e±cient manner.
In this work we propose a new scalable protocol for propagating infor-
mation about a sensed event towards a receiving center. Using only local
information and total absence of coordination between sensors our pro-
tocol achieves to propagate the sensed data to a receiving center by ac-
tivating only those nodes that lie very close to the optimal path between
the source of the event and the destination, resulting in low activation of
the network's sensors. Thus the protocol is very energy e±cient. Further-
more, our protocol is robust as it manages to propagate the information
even when sensors fail with certain probability.
Abstract: We consider the important problem of energy balanced data propagation in wireless sensor networks and we extend and generalize
previous works by allowing adaptive energy assignment. We consider the data gathering problem where data are generated by the sensors and
must be routed toward a unique sink. Sensors route data by either sending the data directly to the sink or in a multi-hop fashion by delivering
the data to a neighbouring sensor. Direct and neighbouring transmissions require different levels of energy consumption. Basically, the protocols balance the energy consumption among the sensors by computing the adequate ratios of direct and neighbouring transmissions. An abstract model of energy dissipation as a random walk is proposed, along with rigorous performance analysis techniques. Two efficient distributed algorithms are presented and analysed, by both rigorous means and simulation.
The first one is easy to implement and fast to execute. The protocol assumes that sensors know a-priori the rate of data they generate.
The sink collects and processes all these information in order to compute the relevant value of the protocol parameter. This value is transmitted
to the sensors which individually compute their optimal ratios of direct and neighbouring transmissions. The second protocol avoids the necessary a-priori knowledge of the data rate generated by sensors by inferring the relevant information from the observation of the data paths.
Furthermore, this algorithm is based on stochastic estimation methods and is adaptive to environmental changes.
