Accelerated collection of sensor data by mobility-enabled topology ranks

We study the problem of fast and energy-efficient data collection of sensory data using a mobile sink, in wireless sensor networks in which both the sensors and the sink move. Motivated by relevant applications, we focus on dynamic sensory mobility and heterogeneous sensor placement. Our approach basically suggests to exploit the sensor motion to adaptively propagate information based on local conditions (such as high placement concentrations), so that the sink gradually “learns” the network and accordingly optimizes its motion. Compared to relevant solutions in the state of the art (such as the blind random walk, biased walks, and even optimized deterministic sink mobility), our method significantly reduces latency (the improvement ranges from 40% for uniform placements, to 800% for heterogeneous ones), while also improving the success rate and keeping the energy dissipation at very satisfactory levels.     

Direction-based adaptive data propagation for heterogeneous sensor mobility

We consider sensor networks where the sensor nodes are attached on entities that move in a highly dynamic, heterogeneous manner. To capture this mobility diversity we introduce a new network parameter, the direction-aware mobility level, which measures how fast and close each mobile node is expected to get to the data destination (the sink). We then provide local, distributed data dissemination protocols that adaptively exploit the node mobility to improve performance. In particular, “high” mobility is used as a low cost replacement for data dissemination (due to the ferrying of data), while in the case of “low” mobility either (a) data propagation redundancy is increased (when highly mobile neighbors exist) or (b) long-distance data transmissions are used (when the entire neighborhood is of low mobility) to accelerate data dissemination toward the sink. An extensive performance comparison to relevant methods from the state of the art demonstrates significant improvements, i.e. latency is reduced by even four times while keeping energy dissipation and delivery success at very satisfactory levels.     

The Impact of Network Structure on the Stability of Greedy Protocols

A packet-switching network is stable if the number of packets in the network remains bounded at all times. A very natural question that arises in the context of stability properties of such networks is how network structure precisely affects these properties. In this work we embark on a systematic study of this question in the context of Adversarial Queueing Theory, which assumes that packets are adversarially injected into the network. We consider size, diameter, maximum vertex degree, minimum number of disjoint paths that cover all edges of the network and network subgraphs as crucial structural parameters of the network, and we present a comprehensive collection of structural results, in the form of stability and instability bounds on injection rate of the adversary for various greedy protocols: —Increasing the size of a network may result in dropping its instability bound. This is shown through a novel, yet simple and natural, combinatorial construction of a size-parameterized network on which certain compositions of greedy protocols are running. The convergence of the drop to 0.5 is found to be fast with and proportional to the increase in size. —Maintaining the size of a network small may already suffice to drop its instability bound to a substantially low value. This is shown through a construction of a FIFO network with size 22, which becomes unstable at rate 0.704. This represents the current state-of-the-art trade-off between network size and instability bound. —The diameter, maximum vertex degree and minimum number of edge-disjoint paths that cover a network may be used as control parameters for the stability bound of the network. This is shown through an improved analysis of the stability bound of any arbitrary FIFO network, which takes these parameters into account. —How much can network subgraphs that are forbidden for stability affect the instability bound? Through improved combinatorial constructions of networks and executions, we improve the state-of-the-art instability bound induced by certain known forbidden subgraphs on networks running a certain greedy protocol. —Our results shed more light and contribute significantly to a finer understanding of the impact of structural parameters on stability and instability properties of networks.     

Design and Analysis of an Efficient Communication Strategy for Hierarchical and Highly Changing Ad-hoc Mobile Networks

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 runners support) 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 runners support idea, introduced in [9,10]. For hierarchical ad-hoc networks, we establish communication by using a runners 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.     

Optimal data gathering paths and energy-balance mechanisms in wireless networks

This paper studies the data gathering problem in wireless networks, where data generated at the nodes has to be collected at a single sink. We investigate the relationship between routing optimality and fair resource management. In particular, we prove that for energy-balanced data propagation, Pareto optimal routing and flow maximization are equivalent, and also prove that flow maximization is equivalent to maximizing the network lifetime. We algebraically characterize the network structures in which energy-balanced data flows are maximal. Moreover, we algebraically characterize communication links which are not used by an optimal flow. This leads to the characterization of minimal network structures supporting the maximal flows.We note that energy-balance, although implying global optimality, is a local property that can be computed efficiently and in a distributed manner. We suggest online distributed algorithms for energy-balance in different optimal network structures and numerically show their stability in particular setting. We remark that although the results obtained in this paper have a direct consequence in energy saving for wireless networks they do not limit themselves to this type of networks neither to energy as a resource. As a matter of fact, the results are much more general and can be used for any type of network and different types of resources.     

The SAINT observatory subsystem: an open-source intelligence tool for uncovering cybersecurity threats

International Journal of Information Security - Data from Online Social Networks, search engines, and the World Wide Web are forms of unstructured knowledge that are not regularly used in...     

Parameterized energy–latency trade-offs for data propagation in sensor networks

We study the problem of greedy, single path data propagation in wireless sensor networks, aiming mainly to minimize the energy dissipation. In particular, we first mathematically analyze and experimentally evaluate the energy efficiency and latency of three characteristic protocols, each one selecting the next hop node with respect to a different criterion (minimum projection, minimum angle and minimum distance to the destination). Our analytic and simulation findings suggest that any single criterion does not simultaneously satisfy both energy efficiency and low latency. Towards parameterized energy–latency trade-offs we provide as well hybrid combinations of the two criteria (direction and proximity to the sink). Our hybrid protocols achieve significant performance gains and allow fine-tuning of desired performance. Also, they have nice energy balance properties, and can prolong the network lifetime.     

Stochastic Models and Adaptive Algorithms for Energy Balance in Sensor Networks

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 analyzed, 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.     

Adaptive Energy Management for Incremental Deployment of Heterogeneous Wireless Sensors

We introduce a new modelling assumption for wireless sensor networks, that of node redeployment (addition of sensor devices during protocol evolution) and we extend the modelling assumption of heterogeneity (having sensor devices of various types). These two features further increase the highly dynamic nature of such networks and adaptation becomes a powerful technique for protocol design. Under these modelling assumptions, we design, implement and evaluate a new power conservation scheme for efficient data propagation. Our scheme is adaptive: it locally monitors the network conditions (density, energy) and accordingly adjusts the sleep-awake schedules of the nodes towards improved operation choices. The scheme is simple, distributed and does not require exchange of control messages between nodes. Implementing our protocol in software we combine it with two well-known data propagation protocols and evaluate the achieved performance through a detailed simulation study using our extended version of the network simulator ns-2. We focus on highly dynamic scenarios with respect to network density, traffic conditions and sensor node resources. We propose a new general and parameterized metric capturing the trade-offs between delivery rate, energy efficiency and latency. The simulation findings demonstrate significant gains (such as more than doubling the success rate of the well-known propagation protocol) and good trade-offs achieved. Furthermore, the redeployment of additional sensors during network evolution and/or the heterogeneous deployment of sensors, drastically improve (when compared to “equal total power” simultaneous deployment of identical sensors at the start) the protocol performance (i.e. the success rate increases up to four times while reducing energy dissipation and, interestingly, keeping latency low). This work has been partially supported by the IST Programme of the European Union under contract number IST-2005-15964 ( ), the Programme under the European Social Fund (ESF) and Operational Program for Educational and Vocational Training II (EPEAEK II) and the Programme of GSRT under contract number 03ED568. A preliminary version of this work has appeared in [13].