From Boma to Boomtown: Extraction, Place, and Politics in Solwezi, Zambia

Mershon Center for International Security Studies Graduate Student Research 2007-08In 2003, the northwestern province of Zambia witnessed the opening of the Kanshanshi and Lumwana copper mines. These mines employ more than 7,000 workers, most of who live in the nearby town of Solwezi — a previously small boma, or administrative town. Rohit Negi's dissertation explores Solwezi's transformation as a mining town, providing a valuable case study of social and political change brought about by economic development

The Sensing Capacity of Sensor Networks

This paper demonstrates fundamental limits of sensor networks for detection problems where the number of hypotheses is exponentially large. Such problems characterize many important applications including detection and classification of targets in a geographical area using a network of sensors, and detecting complex substances with a chemical sensor array. We refer to such applications as largescale detection problems. Using the insight that these problems share fundamental similarities with the problem of communicating over a noisy channel, we define a quantity called the sensing capacity and lower bound it for a number of sensor network models. The sensing capacity expression differs significantly from the channel capacity due to the fact that a fixed sensor configuration encodes all states of the environment. As a result, codewords are dependent and non-identically distributed. The sensing capacity provides a bound on the minimal number of sensors required to detect the state of an environment to within a desired accuracy. The results differ significantly from classical detection theory, and provide an ntriguing connection between sensor networks and communications. In addition, we discuss the insight that sensing capacity provides for the problem of sensor selection.Comment: Submitted to IEEE Transactions on Information Theory, November 200

Sensing Capacity for Markov Random Fields

This paper computes the sensing capacity of a sensor network, with sensors of limited range, sensing a two-dimensional Markov random field, by modeling the sensing operation as an encoder. Sensor observations are dependent across sensors, and the sensor network output across different states of the environment is neither identically nor independently distributed. Using a random coding argument, based on the theory of types, we prove a lower bound on the sensing capacity of the network, which characterizes the ability of the sensor network to distinguish among environments with Markov structure, to within a desired accuracy.Comment: To appear in the proceedings of the 2005 IEEE International Symposium on Information Theory, Adelaide, Australia, September 4-9, 200

Latent Sentiment Detection in Online Social Networks: A Communications-oriented View

In this paper, we consider the problem of latent sentiment detection in Online Social Networks such as Twitter. We demonstrate the benefits of using the underlying social network as an Ising prior to perform network aided sentiment detection. We show that the use of the underlying network results in substantially lower detection error rates compared to strictly features-based detection. In doing so, we introduce a novel communications-oriented framework for characterizing the probability of error, based on information-theoretic analysis. We study the variation of the calculated error exponent for several stylized network topologies such as the complete network, the star network and the closed-chain network, and show the importance of the network structure in determining detection performance.Comment: 13 pages, 6 figures, Submitted to ICC 201

Distributed Throughput-optimal Scheduling in Ad Hoc Wireless Networks

In this paper, we propose a distributed throughput-optimal ad hoc wireless network scheduling algorithm, which is motivated by the celebrated simplex algorithm for solving linear programming (LP) problems. The scheduler stores a sparse set of basic schedules, and chooses the max-weight basic schedule for transmission in each time slot. At the same time, the scheduler tries to update the set of basic schedules by searching for a new basic schedule in a throughput increasing direction. We show that both of the above procedures can be achieved in a distributed manner. Specifically, we propose an average consensus based link contending algorithm to implement the distributed max weight scheduling. Further, we show that the basic schedule update can be implemented using CSMA mechanisms, which is similar to the one proposed by Jiang et al. Compared to the optimal distributed scheduler in Jiang's paper, where schedules change in a random walk fashion, our algorithm has a better delay performance by achieving faster schedule transitions in the steady state. The performance of the algorithm is finally confirmed by simulation results.Comment: 6 pages, 3 figures. A shorter version will appear in the proceedings of IEEE ICC 201