Mark A. Perillo

Middleware to support sensor network applications

Current trends in computing include increases in both distribution and wireless connectivity, leading to highly dynamic, complex environments on top of which applications must be built. The task of designing and ensuring the correctness of applications in these environments is similarly becoming more complex. The unified goal of much of the research in distributed wireless systems is to provide higher-level abstractions of complex low-level concepts to application programmers, easing the design and implementation of applications. A new and growing class of applications for wireless sensor networks require similar complexity encapsulation. However, sensor networks have some unique characteristics, including dynamic availability of data sources and application quality of service requirements, that are not common to other types of applications. These unique features, combined with the inherent distribution of sensors, and limited energy and bandwidth resources, dictate the need for network functionality and the individual sensors to be controlled to best serve the application requirements. In this article, we describe different types of sensor network applications and discuss existing techniques for managing these types of networks. We also overview a variety of related middleware and argue that no existing approach provides all the management tools required by sensor network applications. To meet this need, we have developed a new middleware called MiLAN. MiLAN allows applications to specify a policy for managing the network and sensors, but the actual implementation of this policy is effected within MiLAN. We describe MiLAN and show its effectiveness through the design of a sensor-based personal health monitor.

General Network Lifetime and Cost Models for Evaluating Sensor Network Deployment Strategies

In multihop wireless sensor networks that are often characterized by many-to-one (convergecast) traffic patterns, problems related to energy imbalance among sensors often appear. Sensors closer to a data sink are usually required to forward a large amount of traffic for sensors farther from the data sink. Therefore, these sensors tend to die early, leaving areas of the network completely unmonitored and reducing the functional network lifetime. In our study, we explore possible sensor network deployment strategies that maximize sensor network lifetime by mitigating the problem of the hot spot around the data sink. Strategies such as variable-range transmission power control with optimal traffic distribution, mobile-data-sink deployment, multiple-data-sink deployment, nonuniform initial energy assignment, and intelligent sensor/relay deployment are investigated. We suggest a general model to analyze and evaluate these strategies. In this model, we not only discover how to maximize the network lifetime given certain network constraints but also consider the factor of extra costs involved in more complex deployment strategies. This paper presents a comprehensive analysis on the maximum achievable sensor network lifetime for different deployment strategies, and it also provides practical cost-efficient sensor network deployment guidelines.

On the problem of unbalanced load distribution in wireless sensor networks

In multi-hop wireless sensor networks that are characterized by many-to-one traffic patterns, problems related to energy imbalance among sensors often appear. When each node has a fixed transmission range, the amount of traffic that the sensor nodes are required to forward increases dramatically as the distance to the data sink becomes smaller. Thus, sensors closest to the data sink tend to die early, leaving areas of the network completely unmonitored and causing network partitions. Alternatively, if all sensors transmit directly to the data sink, the furthest nodes from the data sink dies much more quickly than those close to the sink. While it may seem that network lifetime could be improved by use of a more intelligent transmission power control policy that balances the energy used in each node by requiring nodes further from the data sink to transmit over longer distances (although not directly to the data sink), such a policy can only have a limited effect. In fact, this energy balancing can be achieved only at the expense of gross energy inefficiencies. In this paper, we investigate the transmission range distribution optimization problem and show where these inefficiencies exist when trying to maximize the lifetime of many-to-one wireless sensor networks.

Optimal sensor management under energy and reliability constraints

In this work, we address the problem of maximizing the lifetime of an application that requires a minimum level of quality of service from a network of energy-constrained wireless sensors. The problem is formulated as a generalized maximum flow graph problem with additional constraints and an optimal solution is found through linear programming. The result of the optimization is a schedule that determines the mode that all sensors should operate in and allows redundant sensors to turn off and conserve energy whenever possible. We show through simulations of typical sensing applications that by intelligently managing sensors, application lifetime can be extended by up to a factor of 2 in some cases.

An analysis of strategies for mitigating the sensor network hot spot problem

In multi-hop wireless sensor networks that are characterized by many-to-one (converge-east) traffic patterns, problems related to energy imbalance among sensors often appear. When the transmission range is fixed for nodes throughout the network, the amount of traffic that sensors are required to forward increases dramatically as the distance to the data sink becomes smaller. Thus, sensors closest to the data sink tend to die early. Network lifetime can be improved to a limited extent by the use of a more intelligent transmission power control policy that balances the energy used in each node by requiring nodes further from the data sink to transmit over longer distances (although not directly to the data sink). Alternatively, policies such as data aggregation allow the network to operate in a more energy efficient manner. Since the deployment of an aggregator node may be significantly more expensive than the deployment of an ordinary microsensor node, there is a cost tradeoff involved in this approach. This paper provides an analysis of these policies for mitigating the sensor network hot spot problem, considering energy efficiency as well as cost efficiency.

Providing application QoS through intelligent sensor management

Wireless sensor networks are uniquely characterized by tight energy and bandwidth constraints. These networks should be designed to provide enough data to their application so that a reliable description of the environment can be derived, while operating as energy-efficiently as possible and at the same time meeting bandwidth constraints. These goals are typically contradicting and must be balanced at the point where the application is best satisfied. In this paper, we address the problem of maximizing lifetime for a wireless sensor network while meeting a minimum level of reliability. This maximization is achieved by jointly scheduling active sensor sets and finding paths for data routing. Simulation results show that network lifetime can be significantly increased through such methods.

An energy conservation method for wireless sensor networks employing a blue noise spatial sampling technique

In this work, we present a method for the selection of a subset of nodes in a wireless sensor network whose application is to reconstruct the image of a (spatially) bandlimited physical value (e.g., temperature). The selection method creates a sampling pattern based on blue noise masking and guarantees a near minimal number of activated sensors for a given signal-to-noise ratio. The selection method is further enhanced to guarantee that the sensor nodes with the least residual energy are the primary candidates for deselection, while enabling a tradeoff between sensor selection optimality and balanced load distribution. Simulation results show the effectiveness of these selection methods in improving signal-to-noise ratio and reducing the necessary number of active sensors compared with simpler selection approaches.

Sensor management policies to provide application QoS

Abstract Wireless sensor networks are uniquely characterized by tight energy and bandwidth constraints. These networks should be designed to provide enough data to their application so that a reliable description of the environment can be derived, while operating as energy-efficiently as possible and at the same time meeting bandwidth constraints. These goals are typically contradicting and must be balanced at the point where the application is best satisfied. In this paper, we address the problem of maximizing lifetime for a wireless sensor network while meeting a minimum level of application quality of service. This maximization is achieved by jointly scheduling active sensor sets and finding paths for data routing. significantly increased through optimized several heuristic methods. Simulation results show that several heuristic policies can achieve near optimal network lifetime.

An Integrated Approach to Sensor Role Selection

Many sensor network applications require consistent coverage of the region in which they are deployed over the course of the network lifetime. However, because sensor networks may be deployed randomly, node distribution and data redundancy in some regions of the network may be lower than in others. The sensors in the sparsest regions should be considered more critical to the sensor network application since their removal would likely result in unmonitored regions in the environment. For this reason, sensors in the more densely deployed regions should be considered more favorable as candidates to route the traffic of other nodes in the network. In this work, we propose several coverage-aware routing costs that allow traffic to be routed around the sparsely deployed regions so that the coverage of the environment can remain high for a long lifetime. We also propose an integrated route discovery and sensor selection protocol called DAPR that further lengthens network lifetime by jointly selecting routers and active sensors, again with the goal of minimizing the use of sensors in sparsely covered areas. Simulation results show the effectiveness of our approach in extending network lifetime nearly to the extent that can be reached using a centralized approach based on global network knowledge.

Protocols for local data delivery in wireless microsensor networks

Sensor networks are becoming increasingly important as tools for monitoring remote environments. As sensors are typically battery-operated, it is important to efficiently use the limited energy of the nodes to extend the lifetime of the sensor network. Two factors can greatly influence the performance of protocols for these networks: the data delivery model, which describes how the end user wants to access the data; and the network dynamics, which include sensor mobility as well as changes in sensor data rates throughout the lifetime of the network. In this paper, we look at several media access control protocols for sending data from sensors to a local data collector. Comparing these protocols shows that there is an inherent tradeoff in energy efficiency with adaptability of the protocol.