AIDA-Based Real-Time Fault-Tolerant Broadcast Disks

The proliferation of mobile computers and wireless networks requires the design of future distributed real-time applications to recognize and deal with the significant asymmetry between downstream and upstream communication capacities, and the significant disparity between server and client storage capacities. Recent research work proposed the use of Broadcast Disks as a scalable mechanism to deal with this problem. In this paper, we propose a new broadcast disks protocol, based on our Adaptive Information Dispersal Algorithm (AIDA). Our protocol is different from previous broadcast disks protocols in that it improves communication timeliness, fault-tolerance, and security, while allowing for a finer control of multiplexing of prioritized data (broadcast frequencies). We start with a general introduction of broadcast disks. Next, we propose broadcast disk organizations that are suitable for real-time applications. Next, we present AIDA and show its fault-tolerance and security properties. We conclude the paper with the description and analysis of AIDA-based broadcast disks organizations that achieve both timeliness and fault-tolerance, while preserving downstream communication capacity.

Open Issues on TCP for Mobile Computing

We discuss the design principles of TCP within the context of heterogeneous wired/wireless networks and mobile networking. We identify three shortcomings in TCP's behavior: (i) the protocol's error detection mechanism, which does not distinguish different types of errors and thus does not suffice for heterogeneous wired/wireless environments, (ii) the error recovery, which is not responsive to the distinctive characteristics of wireless networks such as transient or burst errors due to handoffs and fading channels, and (iii) the protocol strategy, which does not control the tradeoff between performance measures such as goodput and energy consumption, and often entails a wasteful effort of retransmission and energy expenditure. We discuss a solution-framework based on selected research proposals and the associated evaluation criteria for the suggested modifications. We highlight an important angle that did not attract the required attention so far: the need for new performance metrics, appropriate for evaluating the impact of protocol strategies on battery-powered devices.

Amorphous Placement and Informed Diffusion for Timely Monitoring by Autonomous, Resource-Constrained, Mobile Sensors

Personal communication devices are increasingly equipped with sensors for passive monitoring of encounters and surroundings. We envision the emergence of services that enable a community of mobile users carrying such resource-limited devices to query such information at remote locations in the field in which they collectively roam. One approach to implement such a service is directed placement and retrieval (DPR), whereby readings/queries about a specific location are routed to a node responsible for that location. In a mobile, potentially sparse setting, where end-to-end paths are unavailable, DPR is not an attractive solution as it would require the use of delay-tolerant (flooding-based store-carry-forward) routing of both readings and queries, which is inappropriate for applications with data freshness constraints, and which is incompatible with stringent device power/memory constraints. Alternatively, we propose the use of amorphous placement and retrieval (APR), in which routing and field monitoring are integrated through the use of a cache management scheme coupled with an informed exchange of cached samples to diffuse sensory data throughout the network, in such a way that a query answer is likely to be found close to the query origin. We argue that knowledge of the distribution of query targets could be used effectively by an informed cache management policy to maximize the utility of collective storage of all devices. Using a simple analytical model, we show that the use of informed cache management is particularly important when the mobility model results in a non-uniform distribution of users over the field. We present results from extensive simulations which show that in sparsely-connected networks, APR is more cost-effective than DPR, that it provides extra resilience to node failure and packet losses, and that its use of informed cache management yields superior performance.

An Information Theoretic Framework for Field Monitoring Using Autonomously Mobile Sensors

We consider a mobile sensor network monitoring a spatio-temporal field. Given limited cache sizes at the sensor nodes, the goal is to develop a distributed cache management algorithm to efficiently answer queries with a known probability distribution over the spatial dimension. First, we propose a novel distributed information theoretic approach in which the nodes locally update their caches based on full knowledge of the space-time distribution of the monitored phenomenon. At each time instant, local decisions are made at the mobile nodes concerning which samples to keep and whether or not a new sample should be acquired at the current location. These decisions account for minimizing an entropic utility function that captures the average amount of uncertainty in queries given the probability distribution of query locations. Second, we propose a different correlation-based technique, which only requires knowledge of the second-order statistics, thus relaxing the stringent constraint of having a priori knowledge of the query distribution, while significantly reducing the computational overhead. It is shown that the proposed approaches considerably improve the average field estimation error by maintaining efficient cache content. It is further shown that the correlation-based technique is robust to model mismatch in case of imperfect knowledge of the underlying generative correlation structure.

Detour-Based Mobility Coordination in DTNs

Commonly, research work in routing for delay tolerant networks (DTN) assumes that node encounters are predestined, in the sense that they are the result of unknown, exogenous processes that control the mobility of these nodes. In this paper, we argue that for many applications such an assumption is too restrictive: while the spatio-temporal coordinates of the start and end points of a node's journey are determined by exogenous processes, the specific path that a node may take in space-time, and hence the set of nodes it may encounter could be controlled in such a way so as to improve the performance of DTN routing. To that end, we consider a setting in which each mobile node is governed by a schedule consisting of a ist of locations that the node must visit at particular times. Typically, such schedules exhibit some level of slack, which could be leveraged for DTN message delivery purposes. We define the Mobility Coordination Problem (MCP) for DTNs as follows: Given a set of nodes, each with its own schedule, and a set of messages to be exchanged between these nodes, devise a set of node encounters that minimize message delivery delays while satisfying all node schedules. The MCP for DTNs is general enough that it allows us to model and evaluate some of the existing DTN schemes, including data mules and message ferries. In this paper, we show that MCP for DTNs is NP-hard and propose two detour-based approaches to solve the problem. The first (DMD) is a centralized heuristic that leverages knowledge of the message workload to suggest specific detours to optimize message delivery. The second (DNE) is a distributed heuristic that is oblivious to the message workload, and which selects detours so as to maximize node encounters. We evaluate the performance of these detour-based approaches using extensive simulations based on synthetic workloads as well as real schedules obtained from taxi logs in a major metropolitan area. Our evaluation shows that our centralized, workload-aware DMD approach yields the best performance, in terms of message delay and delivery success ratio, and that our distributed, workload-oblivious DNE approach yields favorable performance when compared to approaches that require the use of data mules and message ferries.

Forewarding in Mobile Opportunistic Networks

Recent advances in processor speeds, mobile communications and battery life have enabled computers to evolve from completely wired to completely mobile. In the most extreme case, all nodes are mobile and communication takes place at available opportunities – using both traditional communication infrastructure as well as the mobility of intermediate nodes. These are mobile opportunistic networks. Data communication in such networks is a difficult problem, because of the dynamic underlying topology, the scarcity of network resources and the lack of global information. Establishing end-to-end routes in such networks is usually not feasible. Instead a store-and-carry forwarding paradigm is better suited for such networks. This dissertation describes and analyzes algorithms for forwarding of messages in such networks. In order to design effective forwarding algorithms for mobile opportunistic networks, we start by first building an understanding of the set of all paths between nodes, which represent the available opportunities for any forwarding algorithm. Relying on real measurements, we enumerate paths between nodes and uncover what we refer to as the path explosion effect. The term path explosion refers to the fact that the number of paths between a randomly selected pair of nodes increases exponentially with time. We draw from the theory of epidemics to model and explain the path explosion effect. This is the first contribution of the thesis, and is a key observation that underlies subsequent results. Our second contribution is the study of forwarding algorithms. For this, we rely on trace driven simulations of different algorithms that span a range of design dimensions. We compare the performance (success rate and average delay) of these algorithms. We make the surprising observation that most algorithms we consider have roughly similar performance. We explain this result in light of the path explosion phenomenon. While the performance of most algorithms we studied was roughly the same, these algorithms differed in terms of cost. This prompted us to focus on designing algorithms with the explicit intent of reducing costs. For this, we cast the problem of forwarding as an optimal stopping problem. Our third main contribution is the design of strategies based on optimal stopping principles which we refer to as Delegation schemes. Our analysis shows that using a delegation scheme reduces cost over naive forwarding by a factor of O(√N), where N is the number of nodes in the network. We further validate this result on real traces, where the cost reduction observed is even greater. Our results so far include a key assumption, which is unbounded buffers on nodes. Next, we relax this assumption, so that the problem shifts to one of prioritization of messages for transmission and dropping. Our fourth contribution is the study of message prioritization schemes, combined with forwarding. Our main result is that one achieves higher performance by assigning higher priorities to young messages in the network. We again interpret this result in light of the path explosion effect.

Preferential Field Coverage Through Detour-Based Mobility Coordination

Controlling the mobility pattern of mobile nodes (e.g., robots) to monitor a given field is a well-studied problem in sensor networks. In this setup, absolute control over the nodes’ mobility is assumed. Apart from the physical ones, no other constraints are imposed on planning mobility of these nodes. In this paper, we address a more general version of the problem. Specifically, we consider a setting in which mobility of each node is externally constrained by a schedule consisting of a list of locations that the node must visit at particular times. Typically, such schedules exhibit some level of slack, which could be leveraged to achieve a specific coverage distribution of a field. Such a distribution defines the relative importance of different field locations. We define the Constrained Mobility Coordination problem for Preferential Coverage (CMC-PC) as follows: given a field with a desired monitoring distribution, and a number of nodes n, each with its own schedule, we need to coordinate the mobility of the nodes in order to achieve the following two goals: 1) satisfy the schedules of all nodes, and 2) attain the required coverage of the given field. We show that the CMC-PC problem is NP-complete (by reduction to the Hamiltonian Cycle problem). Then we propose TFM, a distributed heuristic to achieve field coverage that is as close as possible to the required coverage distribution. We verify the premise of TFM using extensive simulations, as well as taxi logs from a major metropolitan area. We compare TFM to the random mobility strategy—the latter provides a lower bound on performance. Our results show that TFM is very successful in matching the required field coverage distribution, and that it provides, at least, two-fold query success ratio for queries that follow the target coverage distribution of the field.

An Active Pattern Recognition Architecture for Mobile Robots

An active, attentionally-modulated recognition architecture is proposed for object recognition and scene analysis. The proposed architecture forms part of navigation and trajectory planning modules for mobile robots. Key characteristics of the system include movement planning and execution based on environmental factors and internal goal definitions. Real-time implementation of the system is based on space-variant representation of the visual field, as well as an optimal visual processing scheme utilizing separate and parallel channels for the extraction of boundaries and stimulus qualities. A spatial and temporal grouping module (VWM) allows for scene scanning, multi-object segmentation, and featural/object priming. VWM is used to modulate a tn~ectory formation module capable of redirecting the focus of spatial attention. Finally, an object recognition module based on adaptive resonance theory is interfaced through VWM to the visual processing module. The system is capable of using information from different modalities to disambiguate sensory input.

Navite: A Neural Network System For Sensory-Based Robot Navigation

A neural network system, NAVITE, for incremental trajectory generation and obstacle avoidance is presented. Unlike other approaches, the system is effective in unstructured environments. Multimodal inforrnation from visual and range data is used for obstacle detection and to eliminate uncertainty in the measurements. Optimal paths are computed without explicitly optimizing cost functions, therefore reducing computational expenses. Simulations of a planar mobile robot (including the dynamic characteristics of the plant) in obstacle-free and object avoidance trajectories are presented. The system can be extended to incorporate global map information into the local decision-making process.