Howard E. Michel

Power management in SMAC-based energy-harvesting wireless sensor networks using queuing analysis

One of the most important constraints in traditional wireless sensor networks is the limited amount of energy available at each sensor node. The energy consumption is mainly determined by the choice of media access mechanism. SMAC is a typical access mechanism that has drawn much attention in recent years. In WSNs, sensors are usually equipped with capacity-limited battery sources that can sustain longer or shorter period, depending on the energy usage pattern and the activeness level of sensor nodes. To extend the lifetime of the sensor networks, ambient energy resources have been recently exploited in WSNs. Even though solar radiation is known as the superior candidate, its density varies over time depending on many factors such as solar intensity and cloud states, which makes it difficult to predict and utilize the energy efficiently. As a result, how to design an efficient MAC in a solar energy harvesting based WSN becomes a challenging problem. In this paper, we first incorporate a solar energy-harvesting model into SMAC and conduct its performance analysis from a theoretical aspect. Our research works provide a fundamental guideline to design efficient MAC for energy harvesting based WSNs. Our major contribution includes three folders: firstly, we model solar energy harvesting in a photovoltaic cell and then derive the throughput of SMAC in the energy-harvesting based WSNs. Second, we develop a new model based on queuing theory to calculate the average number of energy packets in battery in terms of both duty cycle and throughput. Finally, we form an optimization problem to find a suitable range for the duty cycle to satisfy both quality of service (QoS) and network lifetime requirements.

A New Mechanism for Achieving Secure and Reliable Data Transmission in Wireless Sensor Networks

Wireless sensor networks must be designed to be both secure and reliable, especially when they are used in national security and safety-critical applications. In this paper, we propose a new mechanism, called MVMP (multi-version multi-path), which integrates data segmentation, Forward Error Correction coding, multiple paths, and multiple versions of cryptographic algorithms for achieving both secure and reliable data transmission in sensor networks. The MVMP is also energy-aware in that each piece of the original data will be encrypted by a single encryption algorithm once, and it will be transmitted only once. The proposed MVMP mechanism is compared with existing mechanisms, in particular, the secret sharing mechanism using performance criteria including redundancy level, self-healing capability, and security.

Artificial neural networks using complex numbers and phase encoded weights

The model of a simple perceptron using phase-encoded inputs and complex-valued weights is proposed. The aggregation function, activation function, and learning rule for the proposed neuron are derived and applied to Boolean logic functions and simple computer vision tasks. The complex-valued neuron (CVN) is shown to be superior to traditional perceptrons. An improvement of 135% over the theoretical maximum of 104 linearly separable problems (of three variables) solvable by conventional perceptrons is achieved without additional logic, neuron stages, or higher order terms such as those required in polynomial logic gates. The application of CVN in distortion invariant character recognition and image segmentation is demonstrated. Implementation details are discussed, and the CVN is shown to be very attractive for optical implementation since optical computations are naturally complex. The cost of the CVN is less in all cases than the traditional neuron when implemented optically. Therefore, all the benefits of the CVN can be obtained without additional cost. However, on those implementations dependent on standard serial computers, CVN will be more cost effective only in those applications where its increased power can offset the requirement for additional neurons.

Fault-Intrusion Tolerant Techniques in Wireless Sensor Networks

Wireless sensor networks (WSN) consist of a large number of tiny sensor devices that have limited power and limited sensing, computation, and wireless communications capabilities. Sensor nodes usually operate in unattended and even harsh environments, and as a result, sensor nodes are prone to failures and are vulnerable to malicious attacks. Therefore, for reliable and secure computation and communication in WSN, fault tolerance and intrusion tolerance become two essential attributes that should be designed into WSN. In this paper, we study state-of-the-art fault tolerance and intrusion tolerance techniques for WSN and propose a new fault-intrusion tolerant routing mechanism called MVMP (multi-version multi-path) for WSN that will support highly reliable and secure sensor networks. Follow-up work on the MVMP to be carried out in the near future is also discussed

Enhanced artificial neural networks using complex numbers

The model of a simple perceptron using phase-encoded input and complex-valued weights is proposed. The aggregation function, activation function, and learning rule for the proposed neuron are derived and applied to two and three input Boolean logic functions. An improvement of 135% over the theoretical maximum of 104 linearly separable problems (of three variables) solvable by conventional perceptrons is achieved without additional logic, neuron stages, or higher order terms such as those required in polynomial logic gates. Such a network is very attractive for optical implementation since optical computations are naturally complex.

How to train a phase-only filter

This paper establishes the equivalence of the phase only filter and a complex-valued neural network, and then shows how neural network learning can be utilized to design completely novel phase-only-filter based system. By incorporating the neural network based learning, the pattern recognition capability of the phase only type of filter under various transformation and distortions can be enhanced.

Middleware/API and data fusion in Wireless Sensor Networks

This paper reviews the state of the art of two important aspects of Wireless Sensor Networks (WSNs), namely: 1. Models or Implementations of middleware and/or Application Programmers Interfaces (APIs) to abstract the inner workings of heterogeneous WSNs, and 2. Methods and techniques in reducing pervasive data generated by thousands or tens of thousands of sensor nodes within a WSN in order to reduce transmission load of individual sensor nodes and thereby, the energy consumption of the overall WSN. These two aspects of WSN are inter-related in that together they contribute to a unifying model of vastly heterogeneous WSNs composed of many sensor nodes. A unifying model for WSN will serve as a reference model based on which application specific WSNs can be designed using a universal API for ease of programming, and using a library of data fusion capabilities built into the API of the reference model.

Hierarchical Agglomerative Clustering Based T-outlier Detection

Diversification is a technique to reduce portfolio volatility. In traditional financial domains, the correlation coefficient has been used as a basis for diversification. However, it is problematic in reality since it only captures a single dimension. This research introduces a unique similarity based framework to identify outliers among high dimensional time series objects in financial markets. As the similarity between two assets decreases in the portfolio, the benefits of diversification increase. The paper proposes an efficient hierarchical agglomerative clustering (HAC) algorithm based on vertical and horizontal dimension reduction algorithms. Finally, this paper proposes a unique similarity measurement definition/calculation based on the time-value function. This paper discloses a series of experiment results illustrating the effectiveness of the framework. The detected outliers can be used to monitor portfolio diversification and therefore mitigate risk

A proposed API for the control plane of the WSN Integrated Technical Reference Model (I-TRM)

The Integrated Technical Reference Model (I-TRM) for an autonomous Wireless Sensor Network (WSN) has been developed to be used as a guideline to develop a unified and standardized architecture for a diverse array of multi-platform WSNs. Based on the I-TRM proposed by Michel and Fortier, there are three planes to this reference model: The Information Plane, the Control Plane and the Behavior Plane. This reference model lays out a detailed layered model with functional description of each layer described in general terms. The Control Plane puts forward the goal setting and control of the system. The main focus is on the details about the control organization of the system including hierarchical control and task distribution, in coherence to the initial work done in the field of control architecture, authentication of the semantic correctness of the goal, decomposition of valid goals into functional tasks based on knowledge about the lower layers, and organization of system tasks for goal-achievement in accordance with spatial and temporal information by decomposing the task groups into sub-tasks and assigning priorities to them. This paper presents the follow up research performed on this I-TRM, by providing a platform independent API to aid designers of WSNs to develop a codified implementation of WSNs. The API has been implemented using C in a Windows™ platform running on a standard PC/laptop, as well as portions of it in NesC in a TinyOS environment, running on the Berkeley Motes.

Linear cryptanalysis of a survivable data transmission mechanism for sensor networks

Wireless sensor networks (WSN) are being used in a wide range of application areas, such as national security, attack and disaster preparation and response, military surveillance, and medical care. All these applications require a certain level of reliability and security during data transmission. Data loss or corruption in WSN may be due to hardware failures, wireless channel noises, or malicious attacks. To overcome all these inevitable factors, we have recently presented a selective hybrid cipher (SHC) based algorithm that integrates selective encryption and forward error correction codes for achieving simultaneously secure and reliable data transmission in resource-constrained WSN. In this work, we implement an instance of the SHC-based data transmission mechanism using AES and Reed-Solomon codes, and analyze its security property using the linear cryptanalysis (LC) technique. The LC analysis results of the SHC-based algorithm are compared with those of the traditional DES and AES algorithms. Comparison results show that the SHC design has inherent LC attack resistance and is stronger than DES. The results also show that the proposed SHC-based mechanism can provide security-level that is comparable to AES with the improvement in computational complexity and reduction in energy consumption.