Collaborative Delivery with Energy-Constrained Mobile Robots

We consider the problem of collectively delivering some message from a specified source to a designated target location in a graph, using multiple mobile agents. Each agent has a limited energy which constrains the distance it can move. Hence multiple agents need to collaborate to move the message, each agent handing over the message to the next agent to carry it forward. Given the positions of the agents in the graph and their respective budgets, the problem of finding a feasible movement schedule for the agents can be challenging. We consider two variants of the problem: in non-returning delivery, the agents can stop anywhere; whereas in returning delivery, each agent needs to return to its starting location, a variant which has not been studied before. We first provide a polynomial-time algorithm for returning delivery on trees, which is in contrast to the known (weak) NP-hardness of the non-returning version. In addition, we give resource-augmented algorithms for returning delivery in general graphs. Finally, we give tight lower bounds on the required resource augmentation for both variants of the problem. In this sense, our results close the gap left by previous research.Comment: 19 pages. An extended abstract of this paper was published at the 23rd International Colloquium on Structural Information and Communication Complexity 2016, SIROCCO'1

Real-Time and Energy-Efficient Routing for Industrial Wireless Sensor-Actuator Networks

With the emergence of industrial standards such as WirelessHART, process industries are adopting Wireless Sensor-Actuator Networks (WSANs) that enable sensors and actuators to communicate through low-power wireless mesh networks. Industrial monitoring and control applications require real-time communication among sensors, controllers and actuators within end-to-end deadlines. Deadline misses may lead to production inefficiency, equipment destruction to irreparable financial and environmental impacts. Moreover, due to the large geographic area and harsh conditions of many industrial plants, it is labor-intensive or dan- gerous to change batteries of field devices. It is therefore important to achieve long network lifetime with battery-powered devices. This dissertation tackles these challenges and make a series of contributions. (1) We present a new end-to-end delay analysis for feedback control loops whose transmissions are scheduled based on the Earliest Deadline First policy. (2) We propose a new real-time routing algorithm that increases the real-time capacity of WSANs by exploiting the insights of the delay analysis. (3) We develop an energy-efficient routing algorithm to improve the network lifetime while maintaining path diversity for reliable communication. (4) Finally, we design a distributed game-theoretic algorithm to allocate sensing applications with near-optimal quality of sensing

Self-stabilizing leader election in dynamic networks

The leader election problem is one of the fundamental problems in distributed computing. It has applications in almost every domain. In dynamic networks, topology is expected to change frequently. An algorithm A is self-stabilizing if, starting from a completely arbitrary configuration, the network will eventually reach a legitimate configuration. Note that any self-stabilizing algorithm for the leader election problem is also an algorithm for the dynamic leader election problem, since when the topology of the network changes, we can consider that the algorithm is starting over again from an arbitrary state. There are a number of such algorithms in the literature which require large memory in each process, or which take O(n) time to converge, where n is size of the network. Given the need to conserve time, and possibly space, these algorithms may not be practical for the dynamic leader election problem. In this thesis, three silent self-stabilizing asynchronous distributed algorithms are given for the leader election problem in a dynamic network with unique IDs, using the composite model of computation. If topological changes to the network pause, a leader is elected for each component. A BFS tree is also constructed in each component, rooted at the leader. When another topological change occurs, leaders are then elected for the new components. This election takes O (Diam) rounds, where Diam is the maximum diameter of any component. The three algorithms differ in their leadership stability. The first algorithm, which is the fastest in the worst case, chooses an arbitrary process as the leader. The second algorithm chooses the process of highest priority in each component, where priority can be defined in a variety of ways. The third algorithm has the strictest leadership stability; if a component contains processes that were leaders before the topological change, one of those must be elected to be the new leader. Formal algorithms and their correctness proofs will be given

Broadcasting with Mobile Agents in Dynamic Networks

We study the standard communication problem of broadcast for mobile agents moving in a network. The agents move autonomously in the network and can communicate with other agents only when they meet at a node. In this model, broadcast is a communication primitive for information transfer from one agent, the source, to all other agents. Previous studies of this problem were restricted to static networks while, in this paper, we consider the problem in dynamic networks modelled as an evolving graph. The dynamicity of the graph is unknown to the agents; in each round an adversary selects which edges of the graph are available, and an agent can choose to traverse one of the available edges adjacent to its current location. The only restriction on the adversary is that the subgraph of available edges in each round must span all nodes; in other words the evolving graph is constantly connected. The agents have global visibility allowing them to see the location of other agents in the graph and move accordingly. Depending on the topology of the underlying graph, we determine how many agents are necessary and sufficient to solve the broadcast problem in dynamic networks. While two agents plus the source are sufficient for ring networks, much larger teams of agents are necessary for denser graphs such as grid graphs and hypercubes, and finally for complete graphs of n nodes at least n-2 agents plus the source are necessary and sufficient. We show lower bounds on the number of agents and provide some algorithms for solving broadcast using the minimum number of agents, for various topologies

Byzantine Resilient Computing with the Cloud

We study a framework for modeling distributed network systems assisted by a reliable and powerful cloud service. Our framework aims at capturing hybrid systems based on a point to point message passing network of machines, with the additional capability of being able to access the services of a trusted high-performance external entity (the cloud). We focus on one concrete aspect that was not studied before, namely, ways of utilizing the cloud assistance in order to attain increased resilience against Byzantine behavior of machines in the network. Our network is modeled as a congested clique comprising $k$ machines that are completely connected to form a clique and can communicate with each other by passing small messages. In every execution, up to $\beta k$ machines (for suitable values of $\beta \in [0, 1)$) are allowed to be Byzantine, i.e., behave maliciously including colluding with each other, with the remaining $\gamma k$ or more machines being \emph{honest} (for $\gamma=1-\beta$). Additionally, the machines in our congested clique can access data through a trusted cloud via queries. This externality of the data captures many real-world distributed computing scenarios and provides a natural context for exploring Byzantine resilience for essentially all conceivable problems. Moreover, we are no longer bound by the usual limits of $\beta < 1/3$ or even $\beta < 1/2$ that are typically seen in Byzantine Agreement. We focus on a few fundamental problems. We start with the ${\textsf{Download}}$ problem, wherein the cloud stores $n$ bits and these $n$ bits must be downloaded to all of the $k$ machines. In addition to ${\textsf{Download}}$, we also consider the problem of computing the ${\textsf{Disjunction}}$ and ${\textsf{Parity}}$ of the bits in the cloud. We study these problems under several settings comprising various $\beta$ values and adversarial capabilities.Comment: 54 page

Brief Announcement: Energy Constrained Depth First Search

Depth first search is a natural algorithmic technique for constructing a closed route that visits all vertices of a graph. The length of such route equals, in an edge-weighted tree, twice the total weight of all edges of the tree and this is asymptotically optimal over all exploration strategies. This paper considers a variant of such search strategies where the length of each route is bounded by a positive integer B (e.g. due to limited energy resources of the searcher). The objective is to cover all the edges of a tree T using the minimum number of routes, each starting and ending at the root and each being of length at most B. To this end, we analyze the following natural greedy tree traversal process that is based on decomposing a depth first search traversal into a sequence of limited length routes. Given any arbitrary depth first search traversal R of the tree T, we cover R with routes R_1,...,R_l, each of length at most B such that: R_i starts at the root, reaches directly the farthest point of R visited by R_{i-1}, then R_i continues along the path R as far as possible, and finally R_i returns to the root. We call the above algorithm piecemeal-DFS and we prove that it achieves the asymptotically minimal number of routes l, regardless of the choice of R. Our analysis also shows that the total length of the traversal (and thus the traversal time) of piecemeal-DFS is asymptotically minimum over all energy-constrained exploration strategies. The fact that R can be chosen arbitrarily means that the exploration strategy can be constructed in an online fashion when the input tree T is not known in advance. Each route R_i can be constructed without any knowledge of the yet unvisited part of T. Surprisingly, our results show that depth first search is efficient for energy constrained exploration of trees, even though it is known that the same does not hold for energy constrained exploration of arbitrary graphs

Challenges and Solutions for Location-based Routing in Wireless Sensor Networks with Complex Network Topology

Complex Network Topologies (CNTs)–network holes and cuts–often occur in practical WSN deployments. Many researchers have acknowledged that CNTs adversely affect the performance of location-based routing and proposed various CNT- aware location-based routing protocols. However, although they aim to address practical issues caused by CNTs, many proposed protocols are either based on idealistic assumptions, require too much resources, or have poor performance. Additionally, proposed protocols are designed only for a single routing primitive–either unicast, multicast, or convergecast. However, as recent WSN applications require diverse traffic patterns, the need for an unified routing framework has ever increased. In this dissertation, we address these main weaknesses in the research on location- based routing. We first propose efficient algorithms for detecting and abstracting CNTs in the network. Using these algorithms, we present our CNT-aware location- based unicast routing protocol that achieves the guaranteed small path stretch with significantly reduced communication overhead. We then present our location-based multicast routing protocol that finds near optimal routing paths from a source node to multicast member nodes, with efficient mechanisms for controllable packet header size and energy-efficient recovery from packet losses. Our CNT-aware convergecast routing protocol improves the network lifetime by identifying network regions with concentrated network traffic and distributing the traffic by using the novel concept of virtual boundaries. Finally, we present the design and implementation details of our unified routing framework that seamlessly integrates proposed unicast, multicast, and convergecast routing protocols. Specifically, we discuss the issues regarding the implementation of our routing protocols on real hardware, and the design of the framework that significantly reduces the code and memory size to fit in a resource constrained sensor mote. We conclude with a proactive solution designed to cope with CNTs, where mobile nodes are used for “patching” CNTs to restore the network connectivity and to optimize the network performance

Technical Report: Energy Evaluation of preamble Sampling MAC Protocols for Wireless Sensor Networks

The paper presents a simple probabilistic analysis of the energy consumption in preamble sampling MAC protocols. We validate the analytical results with simulations. We compare the classical MAC protocols (B-MAC and X-MAC) with LAMAC, a method proposed in a companion paper. Our analysis highlights the energy savings achievable with LA-MAC with respect to B-MAC and X-MAC. It also shows that LA-MAC provides the best performance in the considered case of high density networks under traffic congestion