In-network processing is touted as a key technology to eliminate data redundancy and minimize data transmission, which are crucial to saving energy in wireless sensor networks (WSNs). Specifically, operators participating in in-network processing are mapped to nodes in a sensor network. They receive data from downstream operators, process them and route the output to either the upstream operator or the sink node. The objective of operator tree placement is to minimize the total energy consumed in performing in-network processing. Two types of placement algorithms, centralized and distributed, have been proposed. A problem with the centralized algorithm is that it does not scale to large WSN's, because each sensor node is required to know the complete topology of the network. A problem with the distributed algorithm is their high message complexity. In this paper, we propose a heuristic algorithm to place a treestructured operator graph, and present a distributed implementation to optimize in-network processing cost and reduce the communication overhead. We prove a tight upper bound on the minimum in-network processing cost, and show that the heuristic algorithm has better performance than a canonical greedy algorithm. Simulation-based evaluations demonstrate the superior performance of our heuristic algorithm. We also give an improved distributed implementation of our algorithm that has a message overhead of O(M) per node, which is much less than the O(√NM log2M) and O(√NM) complexities for two previously proposed algorithms, Sync and MCFA, respectively. Here, N is the number of network nodes and M is the size of the operator tree.

Psychological disorders, including emotion and behavioral disorders, are common in the modern society. For example, previous studies by the New Zealand Mental Health Survey and the US National Co-morbidity Surveys (NCS), have found that the incidence of depression from ages 18 to 32 to be around 18%. Thus, there is a need to effectively monitor the emotional or affective states of these patients with psychological illness. Whilst real-time and continuous psychological monitoring systems are still not prevalent, the monitoring of physiological signals are made easier with mobile sensors that can be attached to the human body. These physiological signals can then be processed using an embedded processor to provide an alternative means for automatic emotion recognition. However, for such a system to be developed, the relationship between physiological and psychological signals has to be understood. This paper aims to address this by investigating the relationship between the emotional experiences from multiple subjects and their physiological responses, including the skin conductance, heart rate, respiration and movements of the facial muscles. In summary, preliminary evaluations described in this paper demonstrated that the heart rate, respiration, blood volume pulse and electromyography signals have an impact on the recognition rate achievable by the proposed multi-user physiological response-based emotion detection system.

With the development of sensor and wireless communication technologies, the concept of body sensor networks (BSNs) is proposed in recent years. BSNs are used to measure a large number of vital signs of the human body. Body sensor devices are typically powered by batteries and difficult to replace, so energy efficient medium access control (MAC) protocols, which can perform medium access mechanism with less energy are necessary. In this paper, we propose a novel energy saving MAC protocol for mobile BSNs based on time division multiple access (TDMA). An uplink, downlink asymmetric network architecture is introduced. A changeable access and sleep frame format as well as a routing algorithm are proposed. Simulation results show that the proposed MAC protocol achieves lower energy consumption than IEEE 802.15.6.

In this paper, PAFusion, a power-aware framework for distributed data fusion applications in wireless sensor networks is proposed. It is designed for mapping distributed data fusion application into networks. We have evaluated the performances of PAFusion compared to DFuse and optimal solution through simulation. The simulation results show PAFusion has better performances than DFuse and PAFusion has much less overhead and causes only slightly more transmission cost than optimal solution.

Active Networks paradigm integrated with distributed data fusion has the potential to significantly reduce energy dissipation in WSNs, where energy conservation is the most challenging issue. This work aims to minimize energy cost when distributed data fusion is deployed for Active Networks computing paradigm. An approximate solution with much less computational complexity than optimal solution is proposed for large networks called P2lace, which includes two phases, task graph partition and task graph placement. An extensive experimental evaluation compares approximate solution with optimal solution. The results show that our approximate solution is scalable to different task graph characteristics and network size and only causes little more transmission cost than optimal solution.

In this paper, we present the analog circuit design and implementation of an adaptive neuromorphic olfaction chip. An analog VLSI device with on-chip chemosensor array, on-chip sensor interface circuitry and on-chip learning neuromorphic olfactory model has been fabricated in a single chip using Austria Microsystems 0.6 mum CMOS technology. Drawing inspiration from biological olfactory systems, the neuromorphic analog circuits used to process signals from the on-chip odour sensors make use of temporal "spiking" signals to act as carriers of odour information. An on-chip spike time dependent learning circuit is integrated to dynamically adapt weights for odour detection and classification. All the component subsystems implemented on chip have been successfully tested in silicon