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Wireless sensor networks (WSN) are composed of large numbers of small sensing selfpowered nodes which are densely deployed either inside the phenomenon or very close to it (Akyildiz et al., 2002; Culler et al., 2004). The capabilities of these inexpensive, low-power communication devices ensure serving in a wide range of application domains (Chong and Kumar, 2003; Haenggi, 2004). While their potential benefits are clear, a number of open problems must be solved in order for wireless sensor networks to become viable in practice. These problems include issues related to deployment, security, calibration, failure detection and power management. Recently, significant advances have been accomplished in the field of mobile robotics (Engelberger, 1999), and robots have become increasingly more feasible in practical system design. Therefore, a number of problems with wireless sensor networks can be solved by including a mobile robot as an integral part of the system. Specifically, the robot can be used to deploy and calibrate sensors, detect and react to sensor failure, deliver power to sensors, and otherwise maintain the overall health of the wireless sensor network. Energy consumption is one of the most important requirements for many WSN applications. Since energy resources are scarce and battery replacement is not an option for networks with thousands of physically embedded sensor nodes, energy optimization for individual nodes is required as well as the entire network. In static wireless sensor networks, it is observed that, as data traffic must be concentrated towards the sink (base station), the nodes around that sink have to forward data for other nodes whose number can be very large; this problem always exists, regardless of what energy conserving protocol is used for data transmission. As a result, those bottleneck nodes around the sink deplete their batteries much faster than other nodes and, therefore, their lifetime upper bounds the lifetime of the whole network. Mobile collectors (mobile robots) are utilized to act as mechanical data carriers taking advantage of mobility capacity (Grossglauser and Tse, 2002) and physically approaching the sensors for collecting their data using single hop communication. This approach trades data delivery latency for the reduction of energy consumption of sensors; however, it shows remarkable enhancement in the network lifetime. Still, the data delivery latency depends mainly on the mobility regime applied by the collector.

Effective Heuristics for Route Construction of Mobile Data Collectors