One of the main design issues for a sensor network is conservation of the energy available at each sensor node. We propose to deploy multiple, mobile base stations to prolong the lifetime of the sensor network. We split the lifetime of the sensor network into equal periods of time known as rounds. Base stations are relocated at the start of a round. Our method uses an integer linear program to determine new locations for the base stations and a flow-based routing protocol to ensure energy efficient routing during each round. We propose four metrics and evaluate our solution using these metrics. Based on the simulation results we show that employing multiple, mobile base stations in accordance with the solution given by our schemes would significantly increase the lifetime of the sensor network.

We consider the problem of link scheduling in a sensor network employing a TDMA MAC protocol. Our link scheduling algorithm involves two phases. In the first phase, we assign a color to each edge in the network such that no two edges incident on the same node are assigned the same color. We propose a distributed edge coloring algorithm that needs at most (δ+1) colors, where δ is the maximum degree of the graph. To the best of our knowledge, this is the first distributed algorithm that can edge color a graph with at most (δ + 1) colors. In the second phase, we map each color to a unique timeslot and attempt to identify a direction of transmission along each edge such that the hidden terminal and the exposed terminal problems are avoided. Next, considering topologies for which a feasible solution does not exist, we obtain a direction of transmission for each edge using additional timeslots, if necessary. Finally, we show that reversing the direction of transmission along every edge leads to another feasible direction of transmission. Using both the transmission assignments we obtain a TDMA MAC schedule which enables two-way communication between every pair of neighbors. For acyclic topologies, we show that at most 2(δ + 1) timeslots are required. Through simulations we show that for sparse graphs with cycles the number of timeslots assigned is close to 2(δ + 1).

Abstract-We consider the issue of designing a generic transport layer protocol for energy-constrained sensor networks. We present the requirements for such a transport protocol and propose Sensor Transmission Control Protocol (STCP). STCP is a generic, scalable and reliable transport layer protocol where a majority of the functionalities are implemented at the base station. STCP offers controlled variable reliability, congestion detection and avoidance, and supports multiple applications in the same network. We present the design and implementation of STCP and evaluate the protocol with different scenarios and network characteristics.I. REQUIREMENTS OF A GENERIC TRANSPORT LAYER Sensor networks are deployed for a wide range of applications in the military, health, environment, agriculture and office domain. Every application has different characteristics and requirements such as sensed data type, rate of data transmission and reliability. Existing transport layer protocols for sensor networks are either tailored for certain applications or assume that the nodes employ a particular network layer or MAC layer protocol. As a result, their approaches may not be applicable across many sensor network deployments.Heterogenity: Sensor nodes may have multiple sensors (light, temperature, seismic, etc.) with different transmission characteristics. Packets from a sensor for an application constitute its data flow. Each flow can be either continuous or eventdriven. In continuous flow applications, nodes transmit packets periodically to a base station. In event-driven applications, nodes transmit data only when a pertinent event occurs. Both types of flows may exist in the same network. The transport layer protocol should support multiple heterogeneous applications in the same network.Reliability: Every application may require different reliability. For example, in military surveillance, data transmitted by the sensor nodes must always reach the base station. While in temperature monitoring, a few packets may be lost. The transport protocol should exploit this variable reliability model and save network resources.Congestion Control: Packets from all the nodes in the network converge at nodes located near the base station. These nodes forward more packets and hence, there is a possibility of congestion close to the base station. High data rates, sudden burst of data and collisions are other reasons of congestion in sensor networks [13]. Nodes might drop packets due to memory limitations and energy spent in forwarding these dropped packets is effectively wasted. Congestion also increases latency. Hence, the transport layer should support congestion detection and avoidance.It is desirable to design a transport layer protocol that can support multiple applications in the same network, provide controlled variable reliability, address congestion issues, reduce latency and maximize throughput. We propose Sensor Transmission Control Protocol (STCP) and show, through simulations, that it meets these requirements.We summarize the requi...

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Abstract-Many applications of sensor networks require the base station to collect all the data generated by sensor nodes. As a consequence many-to-one communication pattern, referred to as convergecast, is prevalent in sensor networks. In this paper, we address the challenge of fast and reliable convergecast on top of the collision-prone CSMA MAC layer. More specifically, we extend previous work by considering the following two situations:(1) the length of the packets generated by nodes is much smaller than the maximum length of a data frame that can be transmitted in one time slot and (2) not every node in the network has data to transmit and for those that have, may have lots of data that require more than one packet. The first situation leads to the possibility of improvement by data piggybacking/aggregation; the second scenario arises in networks where nodes locally store the data and serves query request on-demand. We present distributed minimal time scheduling algorithms for both the cases. Simulation results have shown significant performance improvements of our new approaches over existing solutions.