A stochastic model is introduced that accurately models the message delay in mobile ad hoc networks where nodes relay messages and the networks are sparsely populated. The model has only two input parameters: the number of nodes and the parameter of an exponential distribution which describes the time until two random mobiles come within communication range of one another. Closed-form expressions are obtained for the Laplace-Stieltjes transform of the message delay, defined as the time needed to transfer a message between a source and a destination. From this we derive both a closed-form expression and an asymptotic approximation (as a function of the number of nodes) of the expected message delay. As an additional result, the probability distribution function is obtained for the number of copies of the message at the time the message is delivered. These calculations are carried out for two protocols: the two-hop multicopy and the unrestricted multicopy protocols. It is shown that despite its simplicity, the model accurately predicts the message delay for both relay strategies for a number of mobility models (the random waypoint, random direction and the random walker mobility models).

Previous work on the coverage of mobile sensor networks focuses on algorithms to reposition sensors in order to achieve a static configuration with an enlarged covered area. In this paper, we study the dynamic aspects of the coverage of a mobile sensor network that depend on the process of sensor movement. As time goes by, a position is more likely to be covered; targets that might never be detected in a stationary sensor network can now be detected by moving sensors. We characterize the area coverage at specific time instants and during time intervals, as well as the time it takes to detect a randomly located stationary target. Our results show that sensor mobility can be exploited to compensate for the lack of sensors and improve network coverage. For mobile targets, we take a game theoretic approach and derive optimal mobility strategies for sensors and targets from their own perspectives.

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In this paper we study the dynamic aspects of the coverage of a mobile sensor network resulting from continuous movement of sensors. As sensors move around, initially uncovered locations are likely to be covered at a later time. A larger area is covered as time continues, and intruders that might never be detected in a stationary sensor network can now be detected by moving sensors. However, this improvement in coverage is achieved at the cost that a location is covered only part of the time, alternating between covered and not covered. We characterize area coverage at specific time instants and during time intervals, as well as the time durations that a location is covered and uncovered. We further consider the time it takes to detect a randomly located intruder and prove that the detection time is exponentially distributed with parameter 2λrv s where λ represents the sensor density, r represents the sensor's sensing range, andv s denotes the average sensor speed. Our results show that sensor mobility brings about unique dynamic coverage properties not present in a stationary sensor network, and that mobility can be exploited to compensate for the lack of sensors to improve coverage. For mobile intruders, we take a game theoretic approach and derive optimal mobility strategies for both sensors and intruders. We prove that the optimal sensor strategy is to choose their directions uniformly at random between [0, 2π). The optimal intruder strategy is to remain stationary to maximize its detection time.This solution represents a mixed strategy which is a Nash equilibrium of the zero-sum game between mobile sensors and intruders.

IEEE Communications Surveys & Tutorials • Third Quarter 2005 22d hoc networks are complex distributed systems that consist of wireless mobile or static nodes that can freely and dynamically self-organize. In this way they form arbitrary, and temporary, "ad hoc" network topologies, allowing devices to seamlessly interconnect in areas with no pre-existing infrastructure. Recently, the introduction of new protocols such as Bluetooth [1], IEEE 802.11 [2], and Hyperlan [3] are making possible the deployment of ad hoc networks for commercial purposes. As a result, considerable research efforts have been made in this new challenging wireless environment. For simplicity, in this article we will use the term MANETs instead of mobile ad hoc networks, and SANETs instead of static ad hoc networks. Also we note that the term ad hoc networks will represent both mobile ad hoc networks (MANETs) and static ad hoc networks (SANETs).TCP (Transmission Control Protocol) [4] was designed to provide reliable end-to-end delivery of data over unreliable networks. In theory, TCP should be independent of the technology of the underlying infrastructure. In particular, TCP should not care whether the Internet Protocol (IP) is running over wired or wireless connections. In practice, it does matter because most TCP deployments have been carefully designed based on assumptions that are specific to wired networks. Ignoring the properties of wireless transmission can lead to TCP implementations with poor performance.In ad hoc networks, the principal problem of TCP lies in performing congestion control in case of losses that are not induced by network congestion. Since bit error rates are very low in wired networks, nearly all TCP versions nowadays assume that packet losses are due to congestion. Consequently, when a packet is detected to be lost, either by timeout or by multiple duplicated ACKs, TCP slows down the sending rate by adjusting its congestion window. Unfortunately, wireless networks suffer from several types of losses that are not related to congestion, making TCP not adapted to this environment. Numerous enhancements and optimizations have been proposed over the last few years to improve TCP performance over one-hop wireless (not necessarily ad hoc) networks. These improvements include infrastructure-based WLANs [5][6][7][8], mobile cellular networking environments [9,10], and satellite networks [11,12]. Ad hoc networks inherit several features of these networks, in particular high bit error rates and path asymmetry, and add new problems caused by mobility and multi-hop communications, such as network partitions, route failures, and hidden (or exposed) terminals. We note that the following TCP versions -Tahoe, Reno, Newreno, and Vegas -perform differently in ad hoc networks [13]. However, all these versions suffer from the same problem: the inability to distinguish between packet losses due to congestion and losses due to the specific features of ad hoc networks. For more details about TCP versions see the Appendix; for a survey of...