combine both communication and mobility capabilities. With mobility in devices, we envision a new class of proactive networks that are able to adapt themselves, via physical movement, to meet the needs of applications. To fully realize these opportunities, effective control of device mobility and the interaction between devices is needed. In this paper, we consider the Message Ferrying (MF) scheme which exploits controlled mobility to transport data in delay-tolerant networks, where end-to-end paths may not exist between nodes. In the MF scheme, a set of special mobile nodes called message ferries are responsible for carrying data for nodes in the network. We study the use of multiple ferries in such networks, which may be necessary to address performance and robustness concerns. We focus on the design of ferry routes. With the possibilities of interaction between ferries, the route design problem is challenging. We present algorithms to calculate routes such that the traffic demand is met and the data delivery delay is minimized. We evaluate these algorithms under a variety of network conditions via simulations. Our goal is to guide the design of MF systems and understand the tradeoff between the incurred cost of multiple ferries and the improved performance. We show that the performance scales well with the number of ferries in terms of throughput, delay and resource requirements in both ferries and nodes. Index Terms — System design, Simulations

Abstract — Although many energy efficient/conserving routing protocols have been proposed for wireless sensor networks, the concentration of data traffic towards a small number of base stations remains a major threat to the network lifetime. The main reason is that the sensor nodes located near a base station have to relay data for a large part of the network and thus deplete their batteries very quickly. The solution we propose in this paper suggests that the base station be mobile; in this way, the nodes located close to it change over time. Data collection protocols can then be optimized by taking both base station mobility and multi-hop routing into account. We first study the former, and conclude that the best mobility strategy consists in following the periphery of the network (we assume that the sensors are deployed within a circle). We then consider jointly mobility and routing algorithms in this case, and show that a better routing strategy uses a combination of round routes and short paths. We provide a detailed analytical model for each of our statements, and corroborate it with simulation results. We show that the obtained improvement in terms of network lifetime is in the order of 500%.

Abstract. Recent research has shown that using a mobile element to collect and carry data mechanically from a sensor network has many advantages over static multihop routing. We have an implementation as well employing a single mobile element. But the network scalability and traffic may make a single mobile element insufficient. In this paper we investigate the use of multiple mobile elements. In particular, we present load balancing algorithm which tries to balance the number of sensor nodes each mobile element services. We show by simulation the benefits of load balancing. 1

Abstract—We discuss the use of mobility to enhance network performance for a certain class of applications in sensor networks. A major performance bottleneck in sensor networks is energy since it is impractical to replace the batteries in embedded sensor nodes post-deployment. A significant portion of the energy expenditure is attributed to communications and, in particular, the nodes close to the sensor network gateways used for data collection typically suffer a large overhead as these nodes must relay data from the remaining network. Even with compression and in-network processing to reduce the amount of communicated data, all the processed data must still traverse these nodes to reach the gateway. We discuss a network infrastructure based on the use of controllably mobile elements to reduce the communication energy consumption at the energy constrained nodes and, thus, increase useful network lifetime. In addition, our approach yields advantages in delay-tolerant networks and sparsely deployed networks. We first show how our approach helps reduce energy consumption at battery constrained nodes. Second, we describe our system prototype which utilizes our proposed approach to improve the energy performance. As part of the prototyping effort, we experienced several interesting design choices and trade-offs that affect system capabilities and performance. We describe many of these design challenges and discuss the algorithms developed for addressing these. In particular, we focus on network protocols and motion control strategies. Our methods are tested using a practical system and do not assume idealistic radio range models or operation in unobstructed environments. Index Terms—Mobile networking, controlled mobility, wireless sensor networks. 1

In this paper, we describe the design and implementation of a distributed operating system for ad hoc networks. Our system simplifies the programming of ad hoc networks and extends total system lifetime by making the entire network appear as a single virtual machine. It automatically and transparently partitions applications into components and dynamically finds them a placement on nodes within the network to reduce energy consumption and to increase system longevity. This paper describes our programming model, outlines the design and implementation of our system and examines the energy efficiency of our approach through extensive simulations as well as validation of a deployment on a physical testbed. We evaluate practical, power-aware, general-purpose algorithms for component placement and migration, and demonstrate that they can significantly increase system longevity by effectively distributing energy consumption and avoiding hotspots. 1

Recent efforts in Disruption Tolerant Networks (DTNs) have shown that mobility can be a powerful means for delivering messages in highly-challenged environments. DTNs are wireless mobile networks that are particularly useful in sparse environments where the density of nodes is insufficient to support direct end-to-end communication. Unfortunately, many mobility scenarios depend on untethered devices with limited energy supplies. Without careful management depleted energy supplies will degrade network connectivity and counteract the robustness gained by mobility. A primary concern is the energy consumed by wireless communication, and in particular the energy consumed in searching for other nodes to communicate with. In this architecture, energy can be conserved by using the low-power radio to discover communication opportunities with other nodes and waking the high-power radio to undertake the data transmission. We develop a generalized power

Motion may be used in sensor networks to change the network configuration for improving the sensing performance. We consider the problem of controlling motion in a distributed manner for a mobile sensor network for a specific form of motion capability. Mobility itself may have a high resource overhead, hence we exploit motility, a constrained form of mobility, which has very low overheads but provides significant reconfiguration potential. We present an architecture which allows each node in the network to learn the medium and phenomenon characteristics. We describe a quantitative metric for sensing performance which is concretely tied to real sensor and medium characteristics, rather than assuming an abstract range based model. The problem of determining the desirable network configuration is expressed as an optimization of this metric. We present a distributed optimization algorithm which computes a desirable network configuration, and adapts it to environmental changes. The relationship of the proposed algorithm to simulated annealing and incremental sub-gradient descent based methods is discussed. A key property of our algorithm is that convergence to a desirable configuration can be proved even though no global coordination is involved. A network protocol to implement this algorithm is discussed, followed by simulations and experiments on a laboratory test-bed.

We describe how a virtual node abstraction layer can be used to coordinate the motion of real mobile nodes in a region of 2-space. In particular, we consider how nodes in a mobile ad hoc network can arrange themselves along a predetermined curve in the plane, and can maintain themselves in such a configuration in the presence of changes in the underlying mobile ad hoc network, specifically, when nodes may join or leave the system or may fail. Our strategy is to allow the mobile nodes to implement a virtual layer consisting of mobile client nodes, stationary Virtual Nodes (VNs) at predetermined locations in the plane, and local broadcast communication. The VNs coordinate among themselves to distribute the client nodes relatively evenly among the VNs’ regions, and each VN directs its local client nodes to form themselves into the local portion of the target curve.

An envisioned usage of sensor networks is in surveillance systems for detecting a target or monitoring a physical phenomenon in a region. Traditionally, stationary sensor networks are deployed to carry out the sensing operations. In many applications, if the monitored region is relatively large compared to the sensing range of a node, a large number of nodes are required in the region to achieve high coverage. Using mobile nodes in such situations can be an attractive alternative. Mobility of sensor nodes has been studied in sensor networks for many purposes such as power saving, data collection, and packet delivery. However, nearly all research literature for the target detection problem has focused on stationary sensor networks. This paper investigates the problem of detecting the presence/absence of a target using mobile sensor networks. It presents an analytic method to evaluate the detection latency based on a collaborative sensing approach using nodes with uncoordinated mobility. We verify the analytic model through simulations. The analytic method provides a simple way of analyzing the tradeoff between number of nodes and detection latency in a mobile sensor network. The analysis is also used to compare the performance of mobile and stationary sensor networks with respect to these measures. Results show that if the target is present at the worst possible location in a given deployment, then detection latency of mobile sensor networks is considerably less as compared to that of stationary networks with the same number of nodes.

Abstract—Two schools of thought exist in terms of handling mobility in mobile ad hoc networks (MANETs). One is the traditional connection-based model, which views node mobility as undesirable and tries to either remove (through recovery schemes) or mask (through tolerant schemes) the effect of mobility. The other is the mobility-assisted model, which considers mobility as a desirable feature, where routing is based on the store-carry-forward paradigm with random or controlled movement of mobile nodes (called ferries). It is well known that mobility increases the capacity of MANETs by reducing the number of relays in routing. Surprisingly, only two models, diameter hop count in the connection-based model and constant hop count in the mobility-assisted model, which correspond to two extremes of the spectrum, have been systematically studied. In this paper, we propose a new routing model that deals with message routing, as well as trajectory planning, of the ferries that carry the message. A logarithmic number of relays are enforced to achieve a good balance among several contradictory goals, including increasing network capacity, increasing ferry sharing, and reducing moving distance. The model considers the dynamic control of ferries in terms of the number of ferries, trajectory planning of ferries, and node communication and synchronization. The effectiveness of the proposed model is evaluated analytically, as well as through simulation. Index Terms—MANETs, mobile nodes, network capacity, store-carry-forward, trajectory planning. 1