Age Matters: Efficient Route Discovery in Mobile Ad Hoc Networks Using Encounter Ages

We propose FResher Encounter SearcH (FRESH), a simple algorithm for efficient route discovery in mobile ad hoc networks. Nodes keep a record of their most recent encounter times with all other nodes. Instead of searching for the destination, the source node searches for any intermediate node that encountered the destination {\em more recently than did the source node itself}. The intermediate node then searches for a node that encountered the destination yet more recently, and the procedure iterates until the destination is reached. Therefore, FRESH replaces the single network-wide search of current proposals with a succession of smaller searches, resulting in a cheaper route discovery. Routes obtained are loop-free. The performance of such a scheme will depend on the nodes' mobility processes. Under standard mobility processes our simulations show that route discovery cost can be decreased by an order of magnitude, a significant gain given that route discovery is a major source of routing overhead in ad hoc networks

TinyNode: A Comprehensive Platform for Wireless Sensor Network Applications

We introduce the TinyNode platform for wireless sensor networks. Supporting both research and industrial deployments, the platform offers communication ranges that exceed current platforms by a factor of 3 to 5, while consuming similar energy. It comes with a rich, practical set of hardware extensions and full TinyOS support. We describe the design choices of the TinyNode, the accompanying hardware modules, and the MAC layer implementation

Last Encounter Routing under Random Waypoint Mobility

Last Encounter Routing (LER) algorithms for mobile ad hoc networks rely only on encounter histories at every node to route packets, and therefore do not need control traffic to track topology changes due to node mobility. LER exploits the fact that past information about a node`s mobility helps to locate that node in the future. As we have pointed out in earlier work \cite{mg}, the performance of LER algorithms depends on the mobility processes of nodes. In this paper, we ask whether LER can work under the random waypoint (RWP) mobility model. This question is important for several reasons. First, as shown in \cite{mg}, a good performance for the RWP model is harder to achieve than for another prominent mobility model, the random walk. This is because the RWP model has a much shorter relaxation time, i.e., a time-horizon over which past information is still useful. Also, the RWP model has a much less favorable ratio of number of encounters between nodes and the traveled distance. Second, in contrast to the random walk, the RWP model is predictable. This provides us with an opportunity to exploit additional information collected in an encounter (such as speed, direction, etc.) to improve routing. We formally define the RWP model, and compute the optimal predictors for several observation sets, i.e., observed parameters of node mobility. We develop a new LER algorithm tuned to the RWP model called GREASE-RWP, and present simulation results that demonstrate that an efficient and scalable LER for the RWP model is possible

Detection of the Small Magellanic Cloud in gamma-rays with Fermi/LAT

The flux of gamma rays with energies >100MeV is dominated by diffuse emission from CRs illuminating the ISM of our Galaxy through the processes of Bremsstrahlung, pion production and decay, and inverse-Compton scattering. The study of this diffuse emission provides insight into the origin and transport of CRs. We searched for gamma-ray emission from the SMC in order to derive constraints on the CR population and transport in an external system with properties different from the Milky Way. We analysed the first 17 months of continuous all-sky observations by the Large Area Telescope of the Fermi mission to determine the spatial distribution, flux and spectrum of the gamma-ray emission from the SMC. We also used past radio synchrotron observations of the SMC to study the population of CR electrons specifically. We obtained the first detection of the SMC in high-energy gamma rays, with an integrated >100MeV flux of (3.7 +/-0.7) x10e-8 ph/cm2/s, with additional systematic uncertainty of <16%. The emission is steady and from an extended source ~3{\deg} in size. It is not clearly correlated with the distribution of massive stars or neutral gas, nor with known pulsars or SNRs, but a certain correlation with supergiant shells is observed. The observed flux implies an upper limit on the average CR nuclei density in the SMC of ~15% of the value measured locally in the Milky Way. The population of high-energy pulsars of the SMC may account for a substantial fraction of the gamma-ray flux, which would make the inferred CR nuclei density even lower. The average density of CR electrons derived from radio synchrotron observations is consistent with the same reduction factor but the uncertainties are large. From our current knowledge of the SMC, such a low CR density does not seem to be due to a lower rate of CR injection and rather indicates a smaller CR confinement volume characteristic size.Comment: 14 pages, 6 figures, accepted for publication in A&

Large scale galactic turbulence: can self-gravity drive the observed HI velocity dispersions?

Observations of turbulent velocity dispersions in the HI component of galactic disks show a characteristic floor in galaxies with low star formation rates and within individual galaxies the dispersion profiles decline with radius. We carry out several high resolution adaptive mesh simulations of gaseous disks embedded within dark matter haloes to explore the roles of cooling, star-formation, feedback, shearing motions and baryon fraction in driving turbulent motions. In all simulations the disk slowly cools until gravitational and thermal instabilities give rise to a multi-phase medium in which a large population of dense self-gravitating cold clouds are embedded within a warm gaseous phase that forms through shock heating. The diffuse gas is highly turbulent and is an outcome of large scale driving of global non-axisymmetric modes as well as cloud-cloud tidal interactions and merging. At low star-formation rates these processes alone can explain the observed HI velocity dispersion profiles and the characteristic value of ~10 km/s observed within a wide range of disk galaxies. Supernovae feedback creates a significant hot gaseous phase and is an important driver of turbulence in galaxies with a star-formation rate per unit area >10^-3 M_sun/yr/kpc^2.Comment: 18 pages, 23 figures, MNRAS accepted. Typos and minor errors corrected. A version with high-resolution figures can be found at http://www-theorie.physik.unizh.ch/~agertz/DISK

Valuable Detours: Least-Cost Anypath Routing

In many networks, it is less costly to transmit a packet to any node in a set of neighbors than to one specific neighbor. This observation was previously exploited by opportunistic routing protocols, by using single-path routing metrics to assign to each node a group of candidate relays for a particular destination. This paper addresses the least-cost anypath routing (LCAR) problem: how to assign a set of candidate relays at each node for a given destination such that the expected cost of forwarding a packet to the destination is minimized. The key is the following tradeoff: on one hand, increasing the number of candidate relays decreases the forwarding cost, but on the other, it increases the likelihood of “veering ” away from the shortest-path route. Prior proposals based on single-path routing metrics or geographic coordinates do not explicitly consider this tradeoff, and as a result do not always make optimal choices. The LCAR algorithm and its framework are general and can be applied to a variety of networks and cost models. We show how LCAR can incorporate different aspects of underlying coordination protocols, for example a link-layer protocol that randomly selects which receiving node will forward a packet, or the possibility that multiple nodes mistakenly forward a packet. In either case, the LCAR algorithm finds the optimal choice of candidate relays that takes into account these properties of the link layer. Finally, we apply LCAR to low-power, low-rate wireless communication and introduce a new wireless link-layer technique to decrease energy transmission costs in conjunction with anypath routing. Simulations show significant reductions in transmission cost to opportunistic routing using single-path metrics. Furthermore LCAR routes are more robust and stable than those based on single-path distances, due to the integrative nature of the LCAR’s route cost metric