The Minimum Backlog Problem

We study the minimum backlog problem (MBP). This online problem arises, e.g., in the context of sensor networks. We focus on two main variants of MBP. The discrete MBP is a 2-person game played on a graph $G=(V,E)$. The player is initially located at a vertex of the graph. In each time step, the adversary pours a total of one unit of water into cups that are located on the vertices of the graph, arbitrarily distributing the water among the cups. The player then moves from her current vertex to an adjacent vertex and empties the cup at that vertex. The player's objective is to minimize the backlog, i.e., the maximum amount of water in any cup at any time. The geometric MBP is a continuous-time version of the MBP: the cups are points in the two-dimensional plane, the adversary pours water continuously at a constant rate, and the player moves in the plane with unit speed. Again, the player's objective is to minimize the backlog. We show that the competitive ratio of any algorithm for the MBP has a lower bound of $\Omega(D)$, where $D$ is the diameter of the graph (for the discrete MBP) or the diameter of the point set (for the geometric MBP). Therefore we focus on determining a strategy for the player that guarantees a uniform upper bound on the absolute value of the backlog. For the absolute value of the backlog there is a trivial lower bound of $\Omega(D)$, and the deamortization analysis of Dietz and Sleator gives an upper bound of $O(D\log N)$ for $N$ cups. Our main result is a tight upper bound for the geometric MBP: we show that there is a strategy for the player that guarantees a backlog of $O(D)$, independently of the number of cups.Comment: 1+16 pages, 3 figure

On-demand fuzzy clustering and ant-colony optimisation based mobile data collection in wireless sensor network

In a wireless sensor network (WSN), sensor nodes collect data from the environment and transfer this data to an end user through multi-hop communication. This results in high energy dissipation of the devices. Thus, balancing of energy consumption is a major concern in such kind of network. Appropriate cluster head (CH) selection may provide to be an efficient way to reduce the energy dissipation and prolonging the network lifetime in WSN. This paper has adopted the concept of fuzzy if-then rules to choose the cluster head based on certain fuzzy descriptors. To optimise the fuzzy membership functions, Particle Swarm Optimisation (PSO) has been used to improve their ranges. Moreover, recent study has confirmed that the introduction of a mobile collector in a network which collects data through short-range communications also aids in high energy conservation. In this work, the network is divided into clusters and a mobile collector starts from the static sink or base station and moves through each of these clusters and collect data from the chosen cluster heads in a single-hop fashion. Mobility based on Ant-Colony Optimisation (ACO) has already proven to be an efficient method which is utilised in this work. Additionally, instead of performing clustering in every round, CH is selected on demand. The performance of the proposed algorithm has been compared with some existing clustering algorithms. Simulation results show that the proposed protocol is more energy-efficient and provides better packet delivery ratio as compared to the existing protocols for data collection obtained through Matlab Simulations

The molecular basis of mammary gland development and epithelial differentiation

Our understanding of the molecular events underpinning the development of mammalian organ systems has been increasing rapidly in recent years. With the advent of new and improved next-generation sequencing methods, we are now able to dig deeper than ever before into the genomic and epigenomic events that play critical roles in determining the fates of stem and progenitor cells during the development of an embryo into an adult. In this review, we detail and discuss the genes and pathways that are involved in mammary gland development, from embryogenesis, through maturation into an adult gland, to the role of pregnancy signals in directing the terminal maturation of the mammary gland into a milk producing organ that can nurture the offspring. We also provide an overview of the latest research in the single-cell genomics of mammary gland development, which may help us to understand the lineage commitment of mammary stem cells (MaSCs) into luminal or basal epithelial cells that constitute the mammary gland. Finally, we summarize the use of 3D organoid cultures as a model system to study the molecular events during mammary gland development. Our increased investigation of the molecular requirements for normal mammary gland development will advance the discovery of targets to predict breast cancer risk and the development of new breast cancer therapies

A Mobility Management Framework for Optimizing the Trajectory of a Mobile Base-station

We describe a software framework for prescribing the trajetory path of a mobile sink in a wireless sensor network under an extensible set of optimization criteria. The framework relies on an integrated mobility manager that continuously advises the sink using application-specific network statistics. We focus on a reference mplementation for TinyOS.Through extensive physical experimentation, we show that the mobility manager significantly improves network performance under a range of optimization scenarios

HUMS: An Autonomous Moving Strategy for Mobile Sinks in Data-Gathering Sensor Networks

Sink mobility has attracted much research interest in recent years because it can improve network performance such as energy efficiency and throughput. An energy-unconscious moving strategy is potentially harmful to the balance of the energy consumption among sensor nodes so as to aggravate the hotspot problem of sensor networks. In this paper, we propose an autonomous moving strategy for the mobile sinks in data-gathering applications. In our solution, a mobile sink approaches the nodes with high residual energy to force them to forward data for other nodes and tries to avoid passing by the nodes with low energy. We performed simulation experiments to compare our solution with other three data-gathering schemes. The simulation results show that our strategy cannot only extend network lifetime notably but also provides scalability and topology adaptability