211 research outputs found

    Robotic Wireless Sensor Networks

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    In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

    Optimization driven multi-hop network design and experimentation: the approach of the FP7 project OPNEX

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    International audienceThe OPNEX project exemplifies system and optimization theory as the foundations for algorithms that provably maximize capacity of wireless networks. The algorithms termed in abstract network models have been converted to protocols and architectures practically applicable to wireless systems. A validation methodology through experimental protocol evaluation in real network testbeds has been proposed and used. OPNEX uses recent advances in system theoretic network control, including the Back-Pressure principle, max-weight scheduling, utility optimization, congestion control, and the primal-dual method for extracting network algorithms. These approaches exhibited vast potential for achieving high capacity and full exploitation of resources in abstract network models and found their way to reality in high performance architectures developed as a result of the research conducted within OPNEX

    Self-organized backpressure routing for the wireless mesh backhaul of small cells

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    The ever increasing demand for wireless data services has given a starring role to dense small cell (SC) deployments for mobile networks, as increasing frequency re-use by reducing cell size has historically been the most effective and simple way to increase capacity. Such densification entails challenges at the Transport Network Layer (TNL), which carries packets throughout the network, since hard-wired deployments of small cells prove to be cost-unfeasible and inflexible in some scenarios. The goal of this thesis is, precisely, to provide cost-effective and dynamic solutions for the TNL that drastically improve the performance of dense and semi-planned SC deployments. One approach to decrease costs and augment the dynamicity at the TNL is the creation of a wireless mesh backhaul amongst SCs to carry control and data plane traffic towards/from the core network. Unfortunately, these lowcost SC deployments preclude the use of current TNL routing approaches such as Multiprotocol Label Switching Traffic Profile (MPLS-TP), which was originally designed for hard-wired SC deployments. In particular, one of the main problems is that these schemes are unable to provide an even network resource consumption, which in wireless environments can lead to a substantial degradation of key network performance metrics for Mobile Network Operators. The equivalent of distributing load across resources in SC deployments is making better use of available paths, and so exploiting the capacity offered by the wireless mesh backhaul formed amongst SCs. To tackle such uneven consumption of network resources, this thesis presents the design, implementation, and extensive evaluation of a self-organized backpressure routing protocol explicitly designed for the wireless mesh backhaul formed amongst the wireless links of SCs. Whilst backpressure routing in theory promises throughput optimality, its implementation complexity introduces several concerns, such as scalability, large end-to-end latencies, and centralization of all the network state. To address these issues, we present a throughput suboptimal yet scalable, decentralized, low-overhead, and low-complexity backpressure routing scheme. More specifically, the contributions in this thesis can be summarized as follows: We formulate the routing problem for the wireless mesh backhaul from a stochastic network optimization perspective, and solve the network optimization problem using the Lyapunov-driftplus-penalty method. The Lyapunov drift refers to the difference of queue backlogs in the network between different time instants, whereas the penalty refers to the routing cost incurred by some network utility parameter to optimize. In our case, this parameter is based on minimizing the length of the path taken by packets to reach their intended destination. Rather than building routing tables, we leverage geolocation information as a key component to complement the minimization of the Lyapunov drift in a decentralized way. In fact, we observed that the combination of both components helps to mitigate backpressure limitations (e.g., scalability,centralization, and large end-to-end latencies). The drift-plus-penalty method uses a tunable optimization parameter that weight the relative importance of queue drift and routing cost. We find evidence that, in fact, this optimization parameter impacts the overall network performance. In light of this observation, we propose a self-organized controller based on locally available information and in the current packet being routed to tune such an optimization parameter under dynamic traffic demands. Thus, the goal of this heuristically built controller is to maintain the best trade-off between the Lyapunov drift and the penalty function to take into account the dynamic nature of semi-planned SC deployments. We propose low complexity heuristics to address problems that appear under different wireless mesh backhaul scenarios and conditions..

    Low Power, Low Delay: Opportunistic Routing meets Duty Cycling

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    Traditionally, routing in wireless sensor networks consists of two steps: First, the routing protocol selects a next hop, and, second, the MAC protocol waits for the intended destination to wake up and receive the data. This design makes it difficult to adapt to link dynamics and introduces delays while waiting for the next hop to wake up. In this paper we introduce ORW, a practical opportunistic routing scheme for wireless sensor networks. In a dutycycled setting, packets are addressed to sets of potential receivers and forwarded by the neighbor that wakes up first and successfully receives the packet. This reduces delay and energy consumption by utilizing all neighbors as potential forwarders. Furthermore, this increases resilience to wireless link dynamics by exploiting spatial diversity. Our results show that ORW reduces radio duty-cycles on average by 50% (up to 90% on individual nodes) and delays by 30% to 90% when compared to the state of the art

    Optimizing Sectorized Wireless Networks: Model, Analysis, and Algorithm

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    Future wireless networks need to support the increasing demands for high data rates and improved coverage. One promising solution is sectorization, where an infrastructure node (e.g., a base station) is equipped with multiple sectors employing directional communication. Although the concept of sectorization is not new, it is critical to fully understand the potential of sectorized networks, such as the rate gain achieved when multiple sectors can be simultaneously activated. In this paper, we focus on sectorized wireless networks, where sectorized infrastructure nodes with beam-steering capabilities form a multi-hop mesh network for data forwarding and routing. We present a sectorized node model and characterize the capacity region of these sectorized networks. We define the flow extension ratio and the corresponding sectorization gain, which quantitatively measure the performance gain introduced by node sectorization as a function of the network flow. Our objective is to find the optimal sectorization of each node that achieves the maximum flow extension ratio, and thus the sectorization gain. Towards this goal, we formulate the corresponding optimization problem and develop an efficient distributed algorithm that obtains the node sectorization under a given network flow with an approximation ratio of 2/3. Through extensive simulations, we evaluate the sectorization gain and the performance of the proposed algorithm in various network scenarios with varying network flows. The simulation results show that the approximate sectorization gain increases sublinearly as a function of the number of sectors per node

    Position-Based Multicast for Mobile Ad-hoc Networks

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    In general, routing protocols for mobile ad-hoc networks (MANETs) can be classified into topology-based protocols and position-based protocols. While for unicast routing many proposals for both classes exist, the existing approaches to multicast routing basically implement topology-based algorithms and only a few of them make use of the geographic positions of the network nodes. These have in common that the sending node has to precalculate the multicast tree over which the packets are distributed and store it in each packet header. This involves two main issues: (a) These approaches are not very flexible with regard to topological changes which abandons the advantages that position-based routing has against topology-based routing, and (b) they do not scale with the number of receivers, since every one of them has to be named in the packet header. This thesis solves these issues and further advances position-based multicast routing. Position-Based Multicast (PBM) enhances the flexibility of position-based multicast routing by following the forwarding principle of position-based unicast routing. It transfers the choice of the next hops in the tree from the sender to the forwarding nodes. Based on the positions of their neighboring nodes, these are able to determine the most suitable next hop(s) at the moment when the packet is being forwarded. The scalability with respect to the number of receiving nodes in a group is solved by Scalable Position-Based Multicast (SPBM). It includes a membership management fulfilling different tasks at once. First, it administers group memberships in order to provide multicast sources with information on whether nodes are subscribed to a specific group. Second, it implements a location service providing the multicast sources with the positions of the subscribed receiver nodes. And third, it geographically aggregates membership data in order to achieve the desired scalability. The group management features two modes of operation: The proactive variant produces a bounded overhead scaling well with the size of the network. The reactive alternative, in contrast, reaches low worst-case join delays but does not limit the overhead. Contention-Based Multicast Forwarding (CBMF) addresses the problems that appear in highly mobile networks induced by outdated position information. Instead of basing forwarding decisions on a perception that may no longer be up to date, the packets are addressed only to the final destination; no explicit next hops are specified. The receiving nodes, which are candidate next hops, then decide by means of contention which of them are the most suitable next hop(s) for a packet. Not only is the decision made based on the most currently available data, but this procedure also saves the regular sending of beacon messages, thus reducing the overhead. The lack of multicast congestion control is another unsolved problem obstructing high-bandwidth data transmission. Sending out more and more packets to a multicast group lets the performance decrease. Backpressure Multicast Congestion Control (BMCC) takes care that the network does not need to handle more packets than it is able to. It achieves this by limiting the packet queues on the intermediate hops. A forwarder may not forward the next packet of a stream before it has noticed---by overhearing the transmission of the next hop---that the previous packet has succeeded. If there is congestion in an area, backpressure is implicitly built up towards the source, which then stops sending out packets until the congestion is released. BMCC takes care that every receiving node will receive packets at the same rate. An alternative mode of operation, BMCC with Backpressure Pruning (BMCC-BP) allows the cutting of congested branches for single packets, permitting a higher rate for uncongested receivers. Besides presenting protocols for multicast communication in MANETs, this thesis also describes implementations of two of the above-mentioned protocols. The first one is an implementation of SPBM for the Linux kernel that allows IP applications to send data via UDP to a group of receivers in an ad-hoc network. The implementation resides between the MAC layer and the network/IP layer of the network stack. It is compatible with unmodified standard kernels of versions 2.4 and 2.6, and may be compiled for x86 or ARM processor architectures. The second implementation is an implementation of CBMF for the ScatterWeb MSB430 sensor nodes. Due to their low-level programmability they allow an integration of the routing protocol with the medium access control. The absence of periodic beacon messages makes the protocol especially suitable for energy-constrained sensor networks. Furthermore, other constraints like limited memory and computational power demand special consideration as well
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