How far are the sources of IceCube neutrinos? Constraints from the diffuse TeV gamma-ray background

The nearly isotropic distribution of the TeV-PeV neutrinos recently detected by IceCube suggests that they come from sources at distance beyond our Galaxy, but how far they are is largely unknown due to lack of any associations with known sources. In this paper, we propose that the cumulative TeV gamma-ray emission accompanying the production of neutrinos can be used to constrain the distance of these neutrino sources, since the opacity of TeV gamma rays due to absorption by the extragalactic background light (EBL) depends on the distance that these TeV gamma rays have travelled. As the diffuse extragalactic TeV background measured by \emph{Fermi} is much weaker than the expected cumulative flux associated with IceCube neutrinos, the majority of IceCube neutrinos, if their sources are transparent to TeV gamma rays, must come from distances larger than the horizon of TeV gamma rays. We find that above 80\% of the IceCube neutrinos should come from sources at redshift $z>0.5$. Thus, the chance for finding nearby sources correlated with IceCube neutrinos would be small. We also find that, to explain the flux of neutrinos under the TeV gamma-ray emission constraint, the redshift evolution of neutrino source density must be at least as fast as the the cosmic star-formation rate.Comment: Accepted by ApJ, some minor changes made, 8 pages, 5 figure

Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks

This paper investigates the use of deep reinforcement learning (DRL) in a MAC protocol for heterogeneous wireless networking referred to as Deep-reinforcement Learning Multiple Access (DLMA). The thrust of this work is partially inspired by the vision of DARPA SC2, a 3-year competition whereby competitors are to come up with a clean-slate design that "best share spectrum with any network(s), in any environment, without prior knowledge, leveraging on machine-learning technique". Specifically, this paper considers the problem of sharing time slots among a multiple of time-slotted networks that adopt different MAC protocols. One of the MAC protocols is DLMA. The other two are TDMA and ALOHA. The nodes operating DLMA do not know that the other two MAC protocols are TDMA and ALOHA. Yet, by a series of observations of the environment, its own actions, and the resulting rewards, a DLMA node can learn an optimal MAC strategy to coexist harmoniously with the TDMA and ALOHA nodes according to a specified objective (e.g., the objective could be the sum throughput of all networks, or a general alpha-fairness objective)

Optimal coverage multi-path scheduling scheme with multiple mobile sinks for WSNs

Wireless Sensor Networks (WSNs) are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring. These sensors can transmit their monitored data to the sink in a multi-hop communication manner. However, the ‘hot spots’ problem will be caused since nodes near sink will consume more energy during forwarding. Recently, mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission. Even though it is difficult to consider many network metrics such as sensor position, residual energy and coverage rate etc., it is still very important to schedule a reasonable moving trajectory for the mobile sink. In this paper, a novel trajectory scheduling method based on coverage rate for multiple mobile sinks (TSCR-M) is presented especially for large-scale WSNs. An improved particle swarm optimization (PSO) combined with mutation operator is introduced to search the parking positions with optimal coverage rate. Then the genetic algorithm (GA) is adopted to schedule the moving trajectory for multiple mobile sinks. Extensive simulations are performed to validate the performance of our proposed method