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The goal here is that of determining the schedules of the collectors visits to the nodes that minimize transmission energy consumption, data latency, and nodal buffer requirements A rst discussion on how to include controllable mobile relays into the network has been presented in reference 32 In the paper, an implementation of a sensor network with an autonomous mobile relay (a robot) is presented The robot visits the (static) sensors, collects their data (single-hop exchange), and delivers them to the sink, similarly to what happens for the MULEs However, in this case the movements of the robot adapt to the network application priorities, which dictate data collection performance parameters In other words, the robot is part of the system, and it is the system that controls its mobility The testbed-based experimental results concern the evaluation of methods for controlling the speed of the robot for optimizing data collection.

Large contributions have separate plug-ins for their core functionality and their user interface This allows running the "headless" core functionality without having to load the UI plug-ins Our plug-in isn't ready for this treatment We can't imagine any of its functionality being useful when running headless However, considering this split is always useful Forcing yourself to separate the core from the user interface often illuminates design problems as you try to minimize the dependencies between plug-ins No class in a core package should refer to a class in a UI package For now, we don't separate core and UI functionality into separate plug-ins, but we separate them into different packages We come up with the package structure shown in Figure 291

The robot traverses networks with different densities following a straight trail and collects the data The data are then delivered to the sink Methods are de ned for routing the sensed data to nodes that are one hop from the robot route when the robot itself does not pass suf ciently close to some sensors Further development of this work with multiple controlled mobile elements (here called explicitly data MULEs) has been presented in reference 33 This work considers the two cases where nodes are deployed uniformly and randomly in a given geographic area and when, more realistically, they are distributed differently In the rst case, criteria are given for the choice of the number of MULEs and for dealing with nodes that can be served by multiple mules.

In the case of nonuniform nodal distribution, a load balancing algorithm is introduced for distributing the number of sensor nodes to the various MULEs so that each MULE serves approximately the same number of sensors MULEs roam through the network in straight lines and gather information about the nodes they can reach Then the MULEs, which can wirelessly talk to each other directly, elect a leader and send the information they gather to it Based on this information, the leader executes the load balancing algorithm and associate nodes to MULEs Data collection is nally performed by the MULEs that travel through the network (in straight lines) and explicitly poll the assigned sensor nodes for collecting their data.

A reader can infer from this package structure that the only published API is ITestRunListener and JUnitCore The non-UI-specific implementation of the functionality is in internalcore, and the user interface is ininternalui

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The problem of scheduling the visit to the sensor nodes of a single relay (called ME, for Mobile Element) so that there is no data loss (due to buffer over ow) is tackled in reference 34 The corresponding Mobile Element Scheduling (MES) problem is proven to be NP-complete, and centralized analytical model (ILP) and algorithms are given for solving the problem In particular, given as input the data generation patterns.

[ Team LiB ]

31 T He, C Huang, B M Blum, J A Stankovic, and T Abdelzaher Range-free localization schemes for large scale sensor networks In Proceedings of the 9th Annual International Conference on Mobile Computing and Networking (Mobicom), 2003, pp 81 95 32 Y Kim, J-J Lee, and A Helmy Modeling and analyzing the impact of location inconsistencies on geographic routing in wireless networks SIGMOBILE Mobile Computer and Communication Review (MC2R), 8(1):48 60, 2004 33 K Seada, A Helmy, and R Govindan On the effect of localization errors on geographic face routing in sensor networks In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN), 2004, pp 71 80 34 B Karp Challenges in geographic routing: Sparse networks, obstacles, and traf c provisioning Presented at DIMACS Workshop on Pervasive Networking, May 2001 35 C Lochert, M Mauve, H Fler, and H Hartenstein Geographic routing in city scenarios.