There exist several applications of sensor networks where reliability of data delivery can be critical. Examples include critical civilian and military applications such as detection of harmful gases and detection of a moving target as well as delivery of queries and control software to so called re-configurable sensors, which are sensors that can operate in one of the several possible modes of operation. While the redundancy inherent in a sensor network might increase the degree of reliability, it by no means can provide any strict reliability semantics. In this project, we focus on the problem of delivering information efficiently with strict reliability semantics. Our work focuses on the reliability from the sink to the sensors (downstream) in a sensor field. We identify different types of downstream reliability semantics that might be required by sensor applications, for delivery of both queries and larger pieces of information such as code segments for re-configurable sensors. We propose a generic framework called GARUDA that is highly scalable, dynamically self-configurable, and instantaneously constructible. It leverages the unique characteristics of sensor network environments effectively, thus incurring low overheads. We show that GARUDA can flexibly support the different classes of reliability semantics.
The problem of congestion in sensor networks is significantly different from conventional ad-hoc networks and has not been studied to any great extent thus far. In this paper, we focus on providing congestion control from the sink to the sensors in a sensor field. The need for congestion control in sensor network is clearly motivated by the nature of the application that it is used for. In critical environments such as military applications, it is necessary that the sink is able to transmit the data to the sensors in the least possible time. While one might be inclined to think that transmitting at a higher (data) rate may accomplish this, we identify that this is not advisable as it results in more number of collisions leading to several packets being lost. This translates to poor usage of network and node resources per packet delivered. We identify the different reasons for congestion from the sink to the sensors and show the uniqueness of the problem in sensor network environments. In this work, we clearly motivate the need for explicit downstream congestion control. We then propose an adaptive, explicit rate control approach, called CONgestion control from SInk to SEnsors (CONSISE), that adjusts the downstream sending rate at each of the sensor nodes to utilize the available network bandwidth depending on the congestion level in the local environment. The proposed approach is highly scalable and easily implementable and can provide large performance benefits and efficient usage of resources with minimal overheads.
Wireless sensor and actor networks (WSANs) refer to a group of sensors and actors linked by wireless medium to perform distributed sensing and acting tasks. In such a network, sensors gather information from the target environment, while actors take decisions and then perform appropriate actions on the environment, which allows a user to effectively sense and act at a distance. In order to provide effective sensing and acting, a distributed local coordination mechanism is required among sensors and actors. Moreover, so as to perform right and timely actions, sensor data must be valid at the time of acting. Thus, one of the main goals in WSANs is to decrease the delay between sensing and acting. This project describes these coordination and communication problems in WSANs. General differences between wireless sensor networks (WSNs) and WSANs are explored, and the physical architecture of WSANs is outlined. Furthermore, sensor-actor and actor-actor coordination problems are discussed and research challenges and requirements in communication protocols occurring due to the presence of actors are explored.
Hazards in WSANs
A typical wireless sensor network performs only one action: sensing the environment. Our requirement for intelligent interaction with the environment has led to the emergence of Wireless Sensor and Actor Networks (WSANs), where a group of sensors, actors and a central coordination entity (sink) linked by wireless medium perform distributed sensing and acting tasks. In WSANs, the sensors monitor the environment based on which the sink issues commands to the actors to act on the environment. In order to provide tight coupling between sensing and acting, an effective coordination mechanism is required among sensors and actors. In this context, we identify the problem of out-of-order execution of queries and commands, called hazards, due to a lack of coordination between sensors and actors. We identify four types of hazards in this project. We also identify and enumerate the associated challenges in addressing these hazards. In this context, we discuss the basic design needed to address this problem efficiently. We propose a distributed and fully localized approach that addresses the problem and the associated challenges based on the design. Through analytical studies and simulations we study the performance of the proposed solution and two basic strategies, and show that the proposed solution is efficient for a variety of network conditions.
Sensors-to-sink data in wireless sensor networks (WSNs) are typically characterized by correlation along the spatial, semantic, and/or temporal dimensions. Exploiting such correlation when performing data aggregation can result in considerable improvements in the bandwidth and energy performance of WSNs. In this paper, we first identify that most of the existing upstream routing approaches in WSNs can be translated to a correlation-unaware data aggregation structure - the shortest-path tree. Although by using a shortest-path tree, some implicit benefits due to correlation is possible, we show that explicitly constructing a correlation-aware structure can result in considerable performance improvement. Toward this end, we present a simple, scalable, and distributed approach called SCT (Semantic/Spatial Correlation-aware Tree) that does not require any centralized coordination while still achieving potential cost benefits due to efficient aggregation. The SCT structure is instantaneously constructed during the course of a single query delivery and is a fixed structure that is efficient for wide range of sources and source distributions. The SCT approach, with its highly manageable structure, ensures low maintenance overhead of the aggregation structure, while also addressing the other challenges. Through simulations and analysis, we show that SCT, though simple in its realization, can achieve substantial performance benefits.
This project discusses a GMRES (Geometric Mean RESonance) iterative solution of the Magnetic Field Integral Equation (MFIE) applied to frequency-domain scattering problems. First, the performance of the original MFIE is studied, for the perfectly electrically conducting (PEC) sphere. It is shown that the residual error and the solution error do not correlate with each other. Second, the MFIE is combined with the normal projection of the primary integral equation for the surface magnetic field. Such a technique does not increase the computational complexity of the MFIE. At the same time, it gives a termination criterion for GMRES iterations since the residual error of the combined equation has a typical saturation behavior. In the saturation zone, the residual error and the solution error have approximately the same small value (typical relative RMS error for the sphere is 1%).