Survey of Inter-satellite Communication for Small Satellite Systems: Physical Layer to Network Layer View

Small satellite systems enable whole new class of missions for navigation, communications, remote sensing and scientific research for both civilian and military purposes. As individual spacecraft are limited by the size, mass and power constraints, mass-produced small satellites in large constellations or clusters could be useful in many science missions such as gravity mapping, tracking of forest fires, finding water resources, etc. Constellation of satellites provide improved spatial and temporal resolution of the target. Small satellite constellations contribute innovative applications by replacing a single asset with several very capable spacecraft which opens the door to new applications. With increasing levels of autonomy, there will be a need for remote communication networks to enable communication between spacecraft. These space based networks will need to configure and maintain dynamic routes, manage intermediate nodes, and reconfigure themselves to achieve mission objectives. Hence, inter-satellite communication is a key aspect when satellites fly in formation. In this paper, we present the various researches being conducted in the small satellite community for implementing inter-satellite communications based on the Open System Interconnection (OSI) model. This paper also reviews the various design parameters applicable to the first three layers of the OSI model, i.e., physical, data link and network layer. Based on the survey, we also present a comprehensive list of design parameters useful for achieving inter-satellite communications for multiple small satellite missions. Specific topics include proposed solutions for some of the challenges faced by small satellite systems, enabling operations using a network of small satellites, and some examples of small satellite missions involving formation flying aspects.Comment: 51 pages, 21 Figures, 11 Tables, accepted in IEEE Communications Surveys and Tutorial

An elementary proposition on the dynamic routing problem in wireless networks of sensors

The routing problem (finding an optimal route from one point in a computer network to another) is surrounded by impossibility results. These results are usually expressed as lower and upper bounds on the set of nodes (or the set of links) of a network and represent the complexity of a solution to the routing problem (a routing function). The routing problem dealt with here, in particular, is a dynamic one (it accounts for network dynamics) and concerns wireless networks of sensors. Sensors form wireless links of limited capacity and time-variable quality to route messages amongst themselves. It is desired that sensors self-organize ad hoc in order to successfully carry out a routing task, e.g. provide daily soil erosion reports for a monitored watershed, or provide immediate indications of an imminent volcanic eruption, in spite of network dynamics. Link dynamics are the first barrier to finding an optimal route between a node x and a node y in a sensor network. The uncertainty of the outcome (the best next hop) of a routing function lies partially with the quality fluctuations of wireless links. Take, for example, a static network. It is known that, given the set of nodes and their link weights (or costs), a node can compute optimal routes by running, say, Dijkstra's algorithm. Link dynamics however suggest that costs are not static. Hence, sensors need a metric (a measurable quantity of uncertainty) to monitor for fluctuations, either improvements or degradations of quality or load; when a fluctuation is sufficiently large (say, by Delta), sensors ought to update their costs and seek another route. Therein lies the other fundamental barrier to find an optimal route - complexity. A crude argument would suggest that sensors (and their links) have an upper bound on the number of messages they can transmit, receive and store due to resource constraints. Such messages can be application traffic, in which case it is desirable, or control traffic, in which case it should be kept minimal. The first type of traffic is demand, and a user should provision for it accordingly. The second type of traffic is overhead, and it is necessary if a routing system (or scheme) is to ensure its fidelity to the application requirements (policy). It is possible for a routing scheme to approximate optimal routes (by Delta) by reducing its message and/or memory complexity. The common denominator of the routing problem and the desire to minimize overhead while approximating optimal routes is Delta, the deviation (or stretch) of a computed route from an optimal one, as computed by a node that has instantaneous knowledge of the set of all nodes and their interaction costs (an oracle). This dissertation deals with both problems in unison. To do so, it needs to translate the policy space (the user objectives) into a metric space (routing objectives). It does so by means of a cost function that normalizes metrics into a number of hops. Then it proceeds to devise, design, and implement a scheme that computes minimum-hop-count routes with manageable complexity. The theory presented is founded on (well-ordered) sets with respect to an elementary proposition, that a route from a source x to a destination y can be computed either by y sending an advertisement to the set of all nodes, or by x sending a query to the set of all nodes; henceforth the proactive method (of y) and the reactive method (of x), respectively. The debate between proactive and reactive routing protocols appears in many instances of the routing problem (e.g. routing in mobile networks, routing in delay-tolerant networks, compact routing), and it is focussed on whether nodes should know a priori all routes and then select the best one (with the proactive method), or each node could simply search for a (hopefully best) route on demand (with the reactive method). The proactive method is stateful, as it requires the entire metric space - the set of nodes and their interaction costs - in memory (in a routing table). The routes computed by the proactive method are optimal and the lower and upper bounds of proactive schemes match those of an oracle. Any attempt to reduce the proactive overhead, e.g. by introducing hierarchies, will result in sub-optimal routes (of known stretch). The reactive method is stateless, as it requires no information whatsoever to compute a route. Reactive schemes - at least as they are presently understood - compute sub-optimal routes (and thus far, of unknown stretch). This dissertation attempts to answer the following question: "what is the least amount of state required to compute an optimal route from a source to a destination?" A hybrid routing scheme is used to investigate this question, one that uses the proactive method to compute routes to near destinations and the reactive method for distant destinations. It is shown that there are cases where hybrid schemes can converge to optimal routes, despite possessing incomplete routing state, and that the necessary and sufficient condition to compute optimal routes with local state alone is related neither to the size nor the density of a network; it is rather the circumference (the size of the largest cycle) of a network that matters. Counterexamples, where local state is insufficient, are discussed to derive the worst-case stretch. The theory is augmented with simulation results and a small experimental testbed to motivate the discussion on how policy space (user requirements) can translate into metric spaces and how different metrics affect performance. On the debate between proactive and reactive protocols, it is shown that the two classes are equivalent. The dissertation concludes with a discussion on the applicability of its results and poses some open problems

Survivability in Time-varying Networks

Time-varying graphs are a useful model for networks with dynamic connectivity such as vehicular networks, yet, despite their great modeling power, many important features of time-varying graphs are still poorly understood. In this paper, we study the survivability properties of time-varying networks against unpredictable interruptions. We first show that the traditional definition of survivability is not effective in time-varying networks, and propose a new survivability framework. To evaluate the survivability of time-varying networks under the new framework, we propose two metrics that are analogous to MaxFlow and MinCut in static networks. We show that some fundamental survivability-related results such as Menger's Theorem only conditionally hold in time-varying networks. Then we analyze the complexity of computing the proposed metrics and develop several approximation algorithms. Finally, we conduct trace-driven simulations to demonstrate the application of our survivability framework to the robust design of a real-world bus communication network

Wireless industrial monitoring and control networks: the journey so far and the road ahead

While traditional wired communication technologies have played a crucial role in industrial monitoring and control networks over the past few decades, they are increasingly proving to be inadequate to meet the highly dynamic and stringent demands of today’s industrial applications, primarily due to the very rigid nature of wired infrastructures. Wireless technology, however, through its increased pervasiveness, has the potential to revolutionize the industry, not only by mitigating the problems faced by wired solutions, but also by introducing a completely new class of applications. While present day wireless technologies made some preliminary inroads in the monitoring domain, they still have severe limitations especially when real-time, reliable distributed control operations are concerned. This article provides the reader with an overview of existing wireless technologies commonly used in the monitoring and control industry. It highlights the pros and cons of each technology and assesses the degree to which each technology is able to meet the stringent demands of industrial monitoring and control networks. Additionally, it summarizes mechanisms proposed by academia, especially serving critical applications by addressing the real-time and reliability requirements of industrial process automation. The article also describes certain key research problems from the physical layer communication for sensor networks and the wireless networking perspective that have yet to be addressed to allow the successful use of wireless technologies in industrial monitoring and control networks