In this paper we present a novel platform for underwater sensor
networks to be used for long-term monitoring of coral
reefs and �sheries The sensor network consists of static
and mobile underwater sensor nodes The nodes communicate
point-to-point using a novel high-speed optical communication
system integrated into the TinyOS stack, and
they broadcast using an acoustic protocol integrated in the
TinyOS stack The nodes have a variety of sensing capabilities,
including cameras, water temperature, and pressure
The mobile nodes can locate and hover above the
static nodes for data muling, and they can perform network
maintenance functions such as deployment, relocation, and
recovery In this paper we describe the hardware and software
architecture of this underwater sensor network We
then describe the optical and acoustic networking protocols
and present experimental networking and data collected in
a pool, in rivers, and in the ocean Finally, we describe our
experiments with mobility for data muling in this network

Data collection, storage and retrieval with an underwater sensor network

We study data preservation in intermittently connected sensor networks, wherein the sensor nodes do not always have connected paths to the base station. In such networks, the generated data is first stored inside the network before being uploaded to the base station when uploading opportunity arises. How to preserve the data inside the network is therefore an important problem. The problem becomes more challenging when sensor nodes have finite and unreplenishable battery energy. In this paper, we identify, formulate and study the data preservation problem in the intermittently connected sensor networks under energy constraints at sensor nodes. The problem aims to preserve the data inside the network for maximum possible time, by distributing the data items from low energy nodes to high energy nodes. We first show that this problem is NP-hard. We then design a centralized greedy heuristic and a distributed data distribution algorithm, and compare their performances using simulations.

Energy-efficient data preservation in intermittently connected sensor networks

The timing constraint of tasks in the mobile real-time computing systems plays the central role in deciding the task schedule as timely completion of the task is very important in such systems. These timing constraints are however completely unquantifiable during the time of system modeling and designing. Thus we consider type-2 fuzzy sets for modeling the timing constraints in mobile and time-critical computing systems and propose a new algorithm FT2EDF (Fuzzy Type-2 Earliest Deadline First) for task scheduling. On the other hand, because of the limitation of the storage power, power efficiency is another foremost design objective for designing mobile real-time computing systems. However, reduction of processor power pulls down the system performance. Timely task completion and power efficiency are therefore two mutually conflicting criteria. In this paper, we propose a heuristic based solution approach that with a modified version of the non-dominated sorting genetic algorithm-II (NSGA-II). Our approach allows that a processor dynamically switches between different voltage levels to ensure optimum reduction in the power requirements without compromising the timeliness of the task completion. The efficacy of our approach is demonstrated with two numerical examples. Comparison with the previous results show that our solution ensures approximately 44% of energy saving as compared to the around 25% of the earlier results.

Real-time power aware scheduling for tasks with type-2 fuzzy timing constraints

Many parallel applications require periodic redistribution of workloads and associated data. In a distributed memory computer, this redistribution can be difficult if limited memory is available for receiving messages. We propose a model for optimizing the exchange of messages under such circumstances which we call the minimum phase remapping problem. We first show that the problem is NP-Complete, and then analyze several methodologies for addressing it. First, we show how the problem can be phrased as an instance of multi-commodity flow. Next, we study a continuous approximation to the problem. We show that this continuous approximation has a solution which requires at most two more phases than the optimal discrete solution, but the question of how to consistently obtain a good discrete solution from the continuous problem remains open. We also devise simple and practical approximation algorithm for the problem with a bound of 1.5 times the optimal number of phases. We also present an empirical study of variations of our algorithms which indicate that our approaches are quite practical.

Interprocessor communication with limited memory

Data generated in sensor networks may have different importance and priority. Different types of data contribute differently for scientists to analyze the physical environment. In a challenging environment, wherein sensor nodes do not always have connected paths to the base station, and not all the data can be preserved inside the network due to severe energy constraints and storage constraints at sensor nodes, how to preserve data with maximum priority is a new and challenging problem. In this paper, we study how to preserve data that yield maximum total priorities, under the constraints that each sensor node has limited energy level and storage capacity. We design an efficient optimal algorithm and prove its optimality. The core of the problem is a maximum weighted flow problem, which is to maximize the total weight of flow in the network considering different flows have different weights. Maximum weighted flow is a generalization of the classic maximum flow problem, wherein each unit of flow has the same weight. To the best of our knowledge, our work is the first to study and solve the maximum weighted flow problem. We propose a more time efficient heuristic algorithm. Via simulation, we show that it performs comparably to the optimal algorithm and performs better than the classic maximum flow algorithm, which does not consider data priority. Finally we design a distributed data preservation algorithm based on push-relabel algorithm, analyze its time and message complexities, and empirically show that it outperforms the push-relabel distributed maximum flow algorithm in terms of the total preserved priorities.

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Data preservation in intermittently connected sensor networks with data priority

We address the energy-efficient data redistribution problem in data-intensive sensor networks (DISNs). In a DISN, a large volume of data gets generated, which is first stored in the network and is later collected for further analysis when the next uploading opportunity arises. The key concern in DISNs is to be able to redistribute the data from data-generating nodes into the network under limited storage and energy constraints at the sensor nodes. We formulate the data redistribution problem where the objective is to minimize the total energy consumption during this process while guaranteeing full utilization of the distributed storage capacity in the DISNs. We show that the problem is APX-hard for arbitrary data sizes; therefore, a polynomial time approximation algorithm is unlikely. For unit data sizes, we show that the problem is equivalent to the minimum cost flow problem, which can be solved optimally. However, the optimal solution's centralized nature makes it unsuitable for large-scale distributed sensor networks. Thus, we design a distributed algorithm for the data redistribution problem which performs very close to the optimal, and compare its performance with various intuitive heuristics. The distributed algorithm relies on potential function-based computations, incurs limited message and computational overhead at both the sensor nodes and data generator nodes, and is easily implementable in a distributed manner. We analytically study the convergence and performance of the proposed algorithm and demonstrate its near-optimal performance and scalability under various network scenarios. In addition, we implement the distributed algorithm in TinyOS, evaluate it using TOSSIM simulator, and show that it outperforms EnviroStore, the only existing scheme for data redistribution in sensor networks, in both solution quality and message overhead. Finally, we extend the proposed algorithm to avoid disproportionate energy consumption at different sensor nodes without compromising the solution quality.

Energy-efficient data redistribution in sensor networks

We address the energy-efficient data redistribution problem in data intensive sensor networks (DISNs) The key question in sensor networks with large volumes of sensory data is how to redistribute the data efficiently under limited storage and energy constraints at the sensor nodes The goal of the redistribution scheme is to minimize the energy consumption during the process, while guaranteeing full utilization of the distributed storage capacity in the DISNs We formulate this problem as a minimum cost flow problem, which can be solved optimally However, the optimal solution's centralized nature makes it unsuitable for large-scale distributed sensor networks We thus design a distributed algorithm for the data redistribution problem which performs very close to the optimal, and compare its performance with various intuitive heuristics Our proposed algorithm relies on potential function based computations, incurs limited message and computational overhead at both the sensor nodes and data generator nodes, and is easily implementable in a distributed manner We analytically show the convergence of our algorithm, and demonstrate its near-optimal performance and scalability under various network scenarios considered Finally, we implement our distributed algorithm in TinyOS and evaluate it using TOSSIM simulator, and show that it outperforms EnviroStore, the only existing scheme for data redistribution in sensor networks, in both solution quality and overhead messages

Rohini Kurkal

Papers

Energy-efficient data redistribution in sensor networks

Energy-efficient data redistribution in sensor networks