Abstract: Wireless Sensor Networks are being increasingly used to measure and sense the environment. A fundamental problem in these sensing applications is the collection of sensed data at a central location (base station). Typically the data is communicated to the base station using multihop routing, where the nodes near the base station relay the data of nodes that are farther away. However, energy being a scarce resource (due to impracticality of replacing batteries post-deployment), and a significant portion of the energy expenditure being attributed to communications, the nodes close to the base station (gateway nodes) suffer a larger overhead, and are the first to run out of energy. This drastically reduces the lifetime of the network. This thesis explores the possibility of using mobility as a solution to this problem of data collection. A mobile entity acting as a base station traverses the network, and collects data from the static sensor nodes as and when it is in their range. This avoids the relaying overhead of the gateway nodes, consequently increasing the network lifetime. We focus on a particular form of mobility where the mobile entity is part of the network infrastructure, and can be controlled. We begin by quantifying the advantage due to mobility. Then we describe the controlling aspect in time domain, where the path of the mobile is fixed, but its speed profile can be varied for maximizing the performance. We present the system prototype that was built. As part of the prototyping effort we experienced several interesting design choices and trade-offs that affect system capabilities and performance. We describe these design challenges and discuss the algorithms developed for addressing these. In particular we focus on network protocols and motion control strategies. We then look at the scalability of this approach. We present the need for multiple mobiles, and give the corresponding algorithms. The second part of the dissertation presents the control in space, where the path of the mobile can be controlled. We formulate this as a scheduling problem, prove that it is NP-complete, and investigate heuristic approaches to solve the problem. This part is evaluated in simulation. We also look at the scalability of these approaches. We end with a case study of a practical system (Networked Info Mechanical Systems, or NIMS) that is deployed for sensing the environment, and demonstrate the applicability of our approaches on it.