Grounded Design: An approach to design for underserved communities

Dr. Dhaval Vyas, Senior Lecturer in the Co-Innovation Group, School of Information Technology and Electrical Engineering, at the University of Queensland

 

Abstract:

Working and designing ‘with’ underserved communities requires ethical, sensitive and long-term engagement with people from those communities. Over the past 10 years, I have developed a design approach – called Grounded Design that enables developing technologies appropriate for marginalized and underserved communities. Grounded Design is a bottom-up approach that focuses on people’s needs, values and everyday practices and informs technology design by working ‘with’ communities over different phases of design. In this talk, I will discuss this approach using examples from three distinct communities: 1) a Brisbane-based low socioeconomic status community; 2) a Sri Lankan under-banked community, and 3) a Logan-based e-waste maker community.

I contend that inclusion and empowerment are central to designing technologies for underserved communities. By using strengths-based approaches (where the focus is given to the positive aspects of people’s lives) over deficit-based approaches, researchers can enable agency in community members during the design process as well as bring about positive social impact, among other outcomes

Prof. Mahendran Velauthapillai, McBride Professor of Computer Science, Computer Science Department, Georgetown University, Washington, DC

Abstract:

Co-operative computations in a network of sensor nodes rely on an established, interference free and repetitive communication between adjacent sensors. This talk analyzes a simple randomized and distributed protocol to establish a periodic communication schedule S where each sensor broadcasts once to communicate to all of its neighbors during each period of S. The result obtained holds for any bounded degree network. The existence of such randomized protocols is not new. Our protocol reduces the number of random bits and the number of transmissions by individual sensors from

? (log2 n)  to O (log n) where n is the number of sensor nodes. These reductions conserve power which is a critical resource. Both protocols assume upper bound on the number of nodes n and the maximum number of neighbor’s b. For a small multiplicative (i.e., a factor ? (1)) increase in the resources, our algorithm can operate without an upper bound on b.

Next we consider a sensor network for monitoring an environment. Assume that there is hidden value under each node of the sensor network covering the environment. The true value could represent a crack, material failure, temperature, intensity of vibration etc.  However, when a measurement is taken at a node, the sensor measurement results in a readout which is a function of the true value of the node and its immediate neighbors. Now, given the sensor measurements, retrieving the true values is the inverse problem that we consider in this talk.