RESOURCE AND ENVIRONMENT AWARE SENSOR COMMUNICATIONS: FRAMEWORK, OPTIMIZATION, AND APPLICATIONS

Recent advances in low power integrated circuit devices, micro-electro-mechanical system (MEMS) technologies, and communications technologies have made possible the deployment of low-cost, low power sensors that can be integrated to form wireless sensor networks (WSN). These wireless sensor networks have vast important applications, i.e.: from battlefield surveillance system to modern highway and industry monitoring system; from the emergency rescue system to early forest fire detection and the very sophisticated earthquake early detection system. Having the broad range of applications, the sensor network is becoming an integral part of human lives. However, the success of sensor networks deployment depends on the reliability of the network itself. There are many challenging problems to make the deployed network more reliable. These problems include but not limited to extending network lifetime, increasing each sensor node throughput, efficient collection of information, enforcing nodes to collaboratively accomplish certain network tasks, etc. One important aspect in designing the algorithm is that the algorithm should be completely distributed and scalable. This aspect has posed a tremendous challenge in designing optimal algorithm in sensor networks. This thesis addresses various challenging issues encountered in wireless sensor networks. The most important characteristic in sensor networks is to prolong the network lifetime. However, due to the stringent energy requirement, the network requires highly energy efficient resource allocation. This highly energy-efficient resource allocation requires the application of an energy awareness system. In fact, we envision a broader resource and environment aware optimization in the sensor networks. This framework reconfigures the parameters from different communication layers according to its environment and resource. We first investigate the application of online reinforcement learning in solving the modulation and transmit power selection. We analyze the effectiveness of the learning algorithm by comparing the effective good throughput that is successfully delivered per unit energy as a metric. This metric shows how efficient the energy usage in sensor communication is. In many practical sensor scenarios, maximizing the energy efficient in a single sensor node may not be sufficient. Therefore, we continue to work on the routing problem to maximize the number of delivered packet before the network becomes useless. The useless network is characterized by the disintegrated remaining network. We design a class of energy efficient routing algorithms that explicitly takes the connectivity condition of the remaining network in to account. We also present the distributed asynchronous routing implementation based on reinforcement learning algorithm. This work can be viewed as distributed connectivity-aware energy efficient routing. We then explore the advantages obtained by doing cooperative routing for network lifetime maximization. We propose a power allocation in the cooperative routing called the maximum lifetime power allocation. The proposed allocation takes into account the residual energy in the nodes when doing the cooperation. In fact, our criterion lets the nodes with more energy to help more compared to the nodes with less energy. We continue to look at the problem of cooperation enforcement in ad-hoc network. We show that by combining the repeated game and self learning algorithm, a better cooperation point can be obtained. Finally, we demonstrate an example of channel-aware application for multimedia communication. In all case studies, we employ optimization scheme that is equipped with the resource and environment awareness. We hope that the proposed resource and environment aware optimization framework will serve as the first step towards the realization of intelligent sensor communications

Unified clustering and communication protocol for wireless sensor networks

In this paper we present an energy-efficient cross layer protocol for providing application specific reservations in wireless senor networks called the “Unified Clustering and Communication Protocol ” (UCCP). Our modular cross layered framework satisfies three wireless sensor network requirements, namely, the QoS requirement of heterogeneous applications, energy aware clustering and data forwarding by relay sensor nodes. Our unified design approach is motivated by providing an integrated and viable solution for self organization and end-to-end communication is wireless sensor networks. Dynamic QoS based reservation guarantees are provided using a reservation-based TDMA approach. Our novel energy-efficient clustering approach employs a multi-objective optimization technique based on OR (operations research) practices. We adopt a simple hierarchy in which relay nodes forward data messages from cluster head to the sink, thus eliminating the overheads needed to maintain a routing protocol. Simulation results demonstrate that UCCP provides an energy-efficient and scalable solution to meet the application specific QoS demands in resource constrained sensor nodes. Index Terms — wireless sensor networks, unified communication, optimization, clustering and quality of service

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

A Structured Hardware/Software Architecture for Embedded Sensor Nodes

Owing to the limited requirement for sensor processing in early networked sensor nodes, embedded software was generally built around the communication stack. Modern sensor nodes have evolved to contain significant on-board functionality in addition to communications, including sensor processing, energy management, actuation and locationing. The embedded software for this functionality, however, is often implemented in the application layer of the communications stack, resulting in an unstructured, top-heavy and complex stack. In this paper, we propose an embedded system architecture to formally specify multiple interfaces on a sensor node. This architecture differs from existing solutions by providing a sensor node with multiple stacks (each stack implements a separate node function), all linked by a shared application layer. This establishes a structured platform for the formal design, specification and implementation of modern sensor and wireless sensor nodes. We describe a practical prototype of an intelligent sensing, energy-aware, sensor node that has been developed using this architecture, implementing stacks for communications, sensing and energy management. The structure and operation of the intelligent sensing and energy management stacks are described in detail. The proposed architecture promotes structured and modular design, allowing for efficient code reuse and being suitable for future generations of sensor nodes featuring interchangeable components

A QoS-Aware Routing Protocol for Real-time Applications in Wireless Sensor Networks

The paper presents a quality of service aware routing protocol which provides low latency for high priority packets. Packets are differentiated based on their priority by applying queuing theory. Low priority packets are transferred through less energy paths. The sensor nodes interact with the pivot nodes which in turn communicate with the sink node. This protocol can be applied in monitoring context aware physical environments for critical applications.Comment: 10 pages. arXiv admin note: text overlap with arXiv:1001.5339 by other author

Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

A network-aware framework for energy-efficient data acquisition in wireless sensor networks

Wireless sensor networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energy-efficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot+, a distributed network-aware framework that optimizes network efficiency by combining three components: (i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; (ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and (iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot+ in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot+ using real datasets and show that KSpot+ provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN