Achieving optimal transmission throughput in data networks in a multi-hop wireless networks is fundamental but hard problem. The situation is aggravated when nodes are mobile. Further, multi-rate system make the analysis of throughput more complicated. In mobile scenario, link may break or be created as nodes are moving within communication range. Route Discovery' which is to find the optimal route and transmission schedule is an important issue. Route discovery entails some cost; so one would not like to initiate discovery too often. On the other hand, not discovering reasonably often entails the risk of being stuck with a suboptimal route and or schedule, which hurts end-to-end throughput. The implementation of the routing decision problem in one dimensional mobile ad hoc network as Markov decision process problem is already is discussed in the paper [1]. A heuristic based on threshold policy is discussed in the same paper without giving a way to find the threshold. In this paper, we suggested a rule for setting the threshold, given the parameters of the system. We also point out that our results remain valid in a slightly different mobility model; this model is a first step towards an open' network in which existing relay nodes can leave and or new relay nodes can join the network.
Many authors have discussed route discovery process @cite_7 , @cite_3 , @cite_12 but neither suggested when to initiate route discovery as their case is related to static case nor they have suggested how frequently to initiate the discovery process, in case of mobile network. Most of them suggested initiation of discovery only when a link, in the existing route, is disrupted i.e., in case of route break. @cite_7 , the authors proposed a modified AODV which uses the concept of reliable distance that change dynamically. Peng @cite_3 suggested distributed route discovery method that uses reinforcement learning. @cite_12 , the authors have used fuzzy controller in every node. In their paper, the destination evaluates performance of all those routes and arranges it in order of preference, when route-request packet reaches its destination.
@cite_3 @cite_12 @cite_7
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Flooding-based approaches are incorporated in reactive routing protocols as the fundamental strategy for route discovery. They overtly affect traffic as the frequency of route discovery increases along with the mobility of users in a mobile ad hoc network (MANET). This paper presents a scheme for reducing overall traffic and end-to-end delay in highly MANET networks. Firstly a new routing algorithm is proposed to reduce the frequency of flood requests by elongating the link duration of the selected paths. In order to increase the path duration, non-disjoint paths are also considered. This concept is a novel approach in route discovery as previous reactive routing protocols seek only disjoint paths. Secondly another novel approach is presented to estimate the link expiration time without the need for global positioning system (GPS) devices. To prevent broadcast storms that may be intrigued during the path discovery operation, another scheme is also introduced. The basic concept behind the proposed scheme is to broadcast only specific and well-defined packets, referred to as "best packets" in the paper. The new protocol is simulated with regard to traffic overhead. Although our main aim in this paper is to reduce the net control traffic in a MANET network, there are other benefits arising from the proposed schemes, namely the increase in link duration, reduction in the end-to-end communication delay, less disruption in data flow, and fewer path setups. Fast and efficient discovery of all neighboring nodes by a node new to a neighborhood is critical to the deployment of wireless ad hoc networks. Different than the conventional ALOHA-type random access discovery schemes, this paper assumes that all nodes in the neighborhood simultaneously send their unique on-off signatures known to the receive node. In particular, a transmitter does not transmit any energy during an ldquooffrdquo mini-slot in its signature sequence. The received signal can be viewed as the outcome of a sequence of tests over the mini-slots, where the outcome of a test is positive if there is energy at the corresponding mini-slot from at least one neighbor, and negative if none of the neighboring nodes transmits energy during the mini-slot. The neighbor discovery problem is thus equivalent to a classical group testing problem. Two practical and scalable detection algorithms are developed from the group testing viewpoint. Unlike some previous neighbor discovery schemes using coherent multiuser detection, which are difficult to implement due to lack of training, the proposed scheme requires only non-coherent energy detection. The proposed algorithms are shown to achieve faster and more reliable discovery than existing random access schemes. Jamming attacks are especially harmful to the reliability of wireless communication, as they can effectively disrupt communication between any node pairs. Existing jamming defenses primarily focus on repairing connectivity between adjacent nodes. In this paper, we address jamming at the network level and focus on restoring the end-to-end data delivery through multipath routing. As long as all paths do not fail concurrently, the end-to-end path availability is maintained. Prior work in multipath selection improves routing availability by choosing node-disjoint paths or link-disjoint paths. However, through our experiments on jamming effects using MicaZ nodes, we show that disjointness is insufficient for selecting fault-independent paths. Thus, we address multipath selection based on the knowledge of a path's availability history. Using Availability History Vectors (AHVs) of paths, we present a centralized AHV-based algorithm to select fault-independent paths, and a distributed AHV-based routing protocol built on top of a classic routing algorithm in ad hoc networks. Our extensive simulation results validate that both AHV-based algorithms are effective in overcoming the jamming impact by maximizing the end-to-end availability of the selected paths. The advances in location-acquisition technologies have led to a myriad of spatial trajectories. These trajectories are usually generated at a low or an irregular frequency due to applications' characteristics or energy saving, leaving the routes between two consecutive points of a single trajectory uncertain (called an uncertain trajectory). In this paper, we present a Route Inference framework based on Collective Knowledge (abbreviated as RICK) to construct the popular routes from uncertain trajectories. Explicitly, given a location sequence and a time span, the RICK is able to construct the top-k routes which sequentially pass through the locations within the specified time span, by aggregating such uncertain trajectories in a mutual reinforcement way (i.e., uncertain + uncertain → certain). Our work can benefit trip planning, traffic management, and animal movement studies. The RICK comprises two components: routable graph construction and route inference. First, we explore the spatial and temporal characteristics of uncertain trajectories and construct a routable graph by collaborative learning among the uncertain trajectories. Second, in light of the routable graph, we propose a routing algorithm to construct the top-k routes according to a user-specified query. We have conducted extensive experiments on two real datasets, consisting of Foursquare check-in datasets and taxi trajectories. The results show that RICK is both effective and efficient. Wireless Sensor Networks have been used in many mobile applications such as wildlife tracking and participatory urban sensing. Because of the combination of high mobility and low-duty-cycle operations, it is a challenging issue to reduce discovery delay among mobile nodes, so that mobile nodes can establish connection quickly once they are within each other's vicinity. Existing discovery designs are essentially pair-wise based, in which discovery is passively achieved when two nodes are pre-scheduled to wake-up at the same time. In contrast, for the first time, this work reduces discovery delay significantly by proactively referring wake-up schedules among a group of nodes. Because proactive references incur additional overhead, we introduce a novel selective reference mechanism based on spatiotemporal properties of neighborhood and the mobility of the nodes. Our quantitative analysis indicates that the discovery delay of our group-based mechanism is significantly smaller than that of the pair-wise one. Our testbed experiments using 40 sensor nodes confirm our theoretical analysis, showing one order of magnitude reduction in discovery delay compared with traditional pair-wise methods with only 0.5 ∼8.8 increase in energy consumption. We consider the problem of neighbor discovery in static wireless ad hoc networks with directional antennas. We propose several probabilistic algorithms in which nodes perform random, independent transmissions to discover their one-hop neighbors. Our neighbor discovery algorithms are classified into two groups, viz. Direct-Discovery Algorithms in which nodes discover their neighbors only upon receiving a transmission from their neighbors and Gossip-based algorithms in which nodes gossip about their neighbors' location information to enable faster discovery. We first consider the operation of these algorithms in a slotted, synchronous system and mathematically derive their optimal parameter settings. We show how to extend these algorithms for an asynchronous system and describe their optimal design. Analysis and simulation of the algorithms show that nodes discover their neighbors much faster using gossip-based algorithms than using direct-discovery algorithms. Furthermore, the performance of gossip-based algorithms is insensitive to an increase in node density. The efficiency of a neighbor discovery algorithm also depends on the choice of antenna beamwidth. We discuss in detail how the choice of beamwidth impacts the performance of the discovery process and provide insights into how nodes can configure their beamwidths. We consider a routing problem in the context of large scale networks with uncontrolled dynamics. A case of uncontrolled dynamics that has been studied extensively is that of mobile nodes, as this is typically the case in cellular and mobile ad-hoc networks. In this paper however we study routing in the presence of a different type of dynamics: nodes do not move, but instead switch between active and inactive states at random times. Our interest in this case is motivated by the behavior of sensor nodes powered by renewable sources, such as solar cells or ambient vibrations. In this paper we formalize the corresponding routing problem as a problem of constructing suitably constrained random walks on random dynamic graphs. We argue that these random walks should be designed so that their resulting invariant distribution achieves a certain load balancing property, and we give simple distributed algorithms to compute the local parameters for the random walks that achieve the sought behavior. A truly novel feature of our formulation is that the algorithms we obtain are able to route messages along all possible routes between a source and a destination node, without performing explicit route discovery repair computations, and without maintaining explicit state information about available routes at the nodes. To the best of our knowledge, these are the first algorithms that achieve true multipath routing (in a statistical sense), at the complexity of simple stateless operations. The rapid deployment of millions of mobile sensors and smartphones has resulted in a demand for opportunistic encounter-based networking to support mobile social networking applications and proximity-based gaming. However, the success of these emerging networks is limited by the lack of effective and energy efficient neighbor discovery protocols. While probabilistic approaches perform well for the average case, they exhibit long tails resulting in high upper bounds on neighbor discovery time. Recent deterministic protocols, which allow nodes to wake up at specific timeslots according to a particular pattern, improve on the worst case bound, but do so by sacrificing average case performance. In response to these limitations, we have designed Searchlight, a highly effective asynchronous discovery protocol that is built on three basic ideas. First, it leverages the constant offset between periodic awake slots to design a simple probing-based approach to ensure discovery. Second, it allows awake slots to cover larger sections of time, which ultimately reduces total awake time drastically. Finally, Searchlight has the option to employ probabilistic techniques with its deterministic approach that can considerably improve its performance in the average case when all nodes have the same duty cycle. We validate Searchlight through analysis and real-world experiments on smartphones that show considerable improvement (up to 50 ) in worst-case discovery latency over existing approaches in almost all cases, irrespective of duty cycle symmetry. Due to slow advance in battery technology, power remains a bottleneck to limit wide applications of mobile ad hoc and wireless sensor networks. Among all extensive studies on minimizing power consumption, neighbor discovery is one of the fundamental components focusing on communication and access. This work surveys research literature on neighbor discovery protocols (NDPs). In general, they can be roughly classified by four underlying principles: randomness, over-half occupation, rotation-resistant intersection, and coprime cycles. The Birthday protocols act as representatives of NDPs using randomness, in which a node decides to listen, transmit, or sleep with probabilities. The original idea of over-half occupation is to be active over at least half of each period, though several refinements have been proposed to decrease its high duty cycle. Methods of rotation-resistant intersection formulate the problem of discovery using combinatorial characteristics of discrete time slots, and guarantee discovery at least once per period. Moreover, neighbor discovery can also be guaranteed within a worst-case bound, as shown by methods adopting coprime cycles. In this paper, we elaborate on these ideas and present several representative protocols, respectively. In particular, we give an integrative analysis of deterministic protocols via a generic framework. A qualitative comparison incorporating multiple criteria and a quantitative evaluation on energy efficiency are also included. Finally, we point out promising research directions towards energy-efficient neighbor discovery. Development in Wireless LAN and Cellular technologies has motivated recent efforts to integrate the two. This creates new application scenarios that were not possible before. Vehicles with Wireless LAN radios can use other vehicles with both Wireless LAN and Cellular radios as mobile gateways and connect to the outside world. We aim to study the feasibility of such global connectivity from the road through simulation of the underlying connectivity characteristics for varying traffic and gateway densities. The connectivity results suggest that each vehicle should be able to connect to at least one gateway for a majority of time. The average path lifetimes are found to be good enough many traditional Internet applications like FTP and HTTP. The effectiveness of the AODV wireless ad-hoc routing protocol over this scenario is evaluated and shown to perform well for the densities considered. However, the routes created by AODV can break very frequently due to the dynamic nature of mobility involved. We introduce a couple of prediction based routing protocols to minimize these route breakages and thus improve performance. These protocols take advantage of some deterministic characteristics of the mobility model to better predict route breakages and take preemptive action. This paper first provides a brief survey on existing traffic offloading techniques in wireless networks. Particularly as a case study, we put forward an online reinforcement learning framework for the problem of traffic offloading in a stochastic heterogeneous cellular network (HCN), where the time-varying traffic in the network can be offloaded to nearby small cells. Our aim is to minimize the total discounted energy consumption of the HCN while maintaining the quality-of-service (QoS) experienced by mobile users. For each cell (i.e., a macro cell or a small cell), the energy consumption is determined by its system load, which is coupled with system loads in other cells due to the sharing over a common frequency band. We model the energy-aware traffic offloading problem in such HCNs as a discrete-time Markov decision process (DTMDP). Based on the traffic observations and the traffic offloading operations, the network controller gradually optimizes the traffic offloading strategy with no prior knowledge of the DTMDP statistics. Such a model-free learning framework is important, particularly when the state space is huge. In order to solve the curse of dimensionality, we design a centralized @math -learning with compact state representation algorithm, which is named @math -learning. Moreover, a decentralized version of the @math -learning is developed based on the fact the macro base stations (BSs) can independently manage the operations of local small-cell BSs through making use of the global network state information obtained from the network controller. Simulations are conducted to show the effectiveness of the derived centralized and decentralized @math -learning algorithms in balancing the tradeoff between energy saving and QoS satisfaction. We present a route planning technique solely based on the concept of node contraction. The nodes are first ordered by 'importance'. A hierarchy is then generated by iteratively contracting the least important node. Contracting a node υ means replacing shortest paths going through v by shortcuts. We obtain a hierarchical query algorithm using bidirectional shortest-path search. The forward search uses only edges leading to more important nodes and the backward search uses only edges coming from more important nodes. For fastest routes in road networks, the graph remains very sparse throughout the contraction process using rather simple heuristics for ordering the nodes. We have five times lower query times than the best previous hierarchical Dijkstra-based speedup techniques and a negative space overhead, i.e., the data structure for distance computation needs less space than the input graph. CHs can be combined with many other route planning techniques, leading to improved performance for many-to-many routing, transit-node routing, goal-directed routing or mobile and dynamic scenarios. We consider the task of topology discovery of sparse random graphs using end-to-end random measurements (e.g., delay) between a subset of nodes, referred to as the participants. The rest of the nodes are hidden, and do not provide any information for topology discovery. We consider topology discovery under two routing models: (a) the participants exchange messages along the shortest paths and obtain end-to-end measurements, and (b) additionally, the participants exchange messages along the second shortest path. For scenario (a), our proposed algorithm results in a sub-linear edit-distance guarantee using a sub-linear number of uniformly selected participants. For scenario (b), we obtain a much stronger result, and show that we can achieve consistent reconstruction when a sub-linear number of uniformly selected nodes participate. This implies that accurate discovery of sparse random graphs is tractable using an extremely small number of participants. We finally obtain a lower bound on the number of participants required by any algorithm to reconstruct the original random graph up to a given edit distance. We also demonstrate that while consistent discovery is tractable for sparse random graphs using a small number of participants, in general, there are graphs which cannot be discovered by any algorithm even with a significant number of participants, and with the availability of end-to-end information along all the paths between the participants. © 2012 Wiley Periodicals, Inc. Random Struct. Alg., 2013 In mobile networks, distance variations caused by node mobility generate fluctuations in the channel gains. Such fluctuations can be treated as another type of fading besides multipath effects. In this paper, the interference statistics in mobile random networks are characterized by incorporating the distance variations of mobile nodes to the channel gain fluctuations. The mean interference is calculated at the origin and at the border of a finite mobile network. The network performance is evaluated in terms of the outage probability. Compared to a static network, the interference in a single snapshot does not change under uniform mobility models. However, random waypoint mobility increases (decreases) the interference at the origin (at the border). Furthermore, due to the correlation of the node locations, the interference and outage are temporally and spatially correlated. We quantify the temporal correlation of the interference and outage in mobile Poisson networks in terms of the correlation coefficient and conditional outage probability, respectively. The results show that it is essential that routing, MAC, and retransmission schemes need to be smart (i.e., correlation-aware) to avoid bursts of transmission failures. In this paper we propose a novel relay selection method for cooperation communication networks using fuzzy logic. Many efforts have been made in the literature to select the superior relay based on relay's SNR SER and or relay's reputation (in cooperation stimulation methods). We jointly consider four criteria for the process of relay selection, relay's SNR SER, relay's reputation, relaying strategy and relay location. We consider the condition in which network users employ different relaying strategies, i.e., some relay nodes employ decode-and-forward strategy, some nodes employ amplify-and-forward strategy, and the other ones employ compress-and-forward strategy. Also, we categorize the network users into LM clusters and take into account the relative distance of potential relay nodes from the source and destination nodes in the process of relay selection. This relative distance has significant impact on the average achievable rate in destination node. Finally, by using a fuzzy logic decision making method, we select the “best relay” based on these four criteria. This paper characterizes the performance in terms of queueing stability of a network composed of multiple MIMO transmitter-receiver pairs taking into account the dynamic traffic pattern and the probing feedback cost. We adopt a centralized scheduling scheme that selects a number of active pairs in each time-slot. We consider that the transmitters apply interference alignment (IA) technique if two or more pairs are active, whereas in the special case where one pair is active point-to-point MIMO singular value decomposition (SVD) is used. We consider a time-division duplex (TDD) system where transmitters acquire their channel state information (CSI) by decoding the pilot sequences sent by the receivers. Since global CSI knowledge is required for IA, the transmitters have also to exchange their estimated CSIs over a backhaul of limited capacity (i.e. imperfect case). Under this setting, we characterize in this paper the stability region of the system under both the imperfect and perfect (i.e. unlimited backhaul) cases, then we examine the gap between these two resulting regions. Further, under each case we provide a centralized probing policy that achieves the max stability region. These stability regions and scheduling policies are given for the symmetric system, where all the path loss coefficients are equal to each other, as well as for the general system. For the symmetric system, we provide the conditions under which IA yields a queueing stability gain compared to SVD. Under the general system, the adopted scheduling policy is of a high computational complexity for moderate numbers of pairs, consequently we propose an approximate policy that has a reduced complexity but that achieves only a fraction of the system stability region. A characterization of this fraction is provided. Location is a key context ingredient and many existing pervasive applications rely on the current locations of their users. However, with the ability to predict the future location and movement behavior of a user, the usability of these applications can be greatly improved. In this paper, we propose an approach to predict both the intended destination and the future route of a person. Rather than predicting the destination and future route separately, we have focused on making prediction in an integrated way by exploiting personal movement data (i.e. trajectories) collected by GPS. Since trajectories contain daily whereabouts information of a person, the proposed approach first detects the significant places where the person may depart from or go to using a clustering-based algorithm called FBM (Forward-Backward Matching), then abstracts the trajectories based on a space partitioning method, and finally extracts movement patterns from the abstracted trajectories using an extended CRPM (Continuous Route Pattern Mining) algorithm. Extracted movement patterns are organized in terms of origin-destination couples. The prediction is made based on a pattern tree built from these movement patterns. With the real personal movement data of 14 participants, we conducted a number of experiments to evaluate the performance of our system. The results show that our approach can achieve approximately 80 and 60 accuracy in destination prediction and 1-step prediction, respectively, and result in an average deviation of approximately 60 m in continuous future route prediction. Finally, based on the proposed approach, we implemented a prototype running on mobile phones, which can extract patterns from a user's historical movement data and predict the destination and future route. In a wireless network, the set of transmitting nodes changes frequently because of the MAC scheduler and the traffic load. Analyzing the connectivity of such a network using static graphs would lead to pessimistic performance results. In this paper, we consider an ad hoc network with half-duplex radios that uses multihop routing and slotted ALOHA for the network MAC contention and introduce a random dynamic multi-digraph to model its connectivity. We first provide analytical results about the degree distribution of the graph. Next, defining the path formation time as the minimum time required for a causal path to form between the source and destination on the dynamic graph, we derive the distributional properties of the connection delay using techniques from first-passage percolation and epidemic processes.We show that the delay scales linearly with the distance and provide asymptotic results (with respect to time) for the positions of the nodes which are able to receive information from a transmitter located at the origin. We also provide simulation results to support the theoretical results. A routing problem in static wireless ad hoc networks is considered as it arises in a rapidly deployed, sensor based, monitoring system known as the wireless sensor network. Information obtained by the monitoring nodes needs to be routed to a set of designated gateway nodes. In these networks, every node is capable of sensing, data processing, and communication, and operates on its limited amount of battery energy consumed mostly in transmission and reception at its radio transceiver. If we assume that the transmitter power level can be adjusted to use the minimum energy required to reach the intended next hop receiver then the energy consumption rate per unit information transmission depends on the choice of the next hop node, i.e., the routing decision. We formulate the routing problem as a linear programming problem, where the objective is to maximize the network lifetime, which is equivalent to the time until the network partition due to battery outage. Two different models are considered for the information-generation processes. One assumes constant rates and the other assumes an arbitrary process. A shortest cost path routing algorithm is proposed which uses link costs that reflect both the communication energy consumption rates and the residual energy levels at the two end nodes. The algorithm is amenable to distributed implementation. Simulation results with both information-generation process models show that the proposed algorithm can achieve network lifetime that is very close to the optimal network lifetime obtained by solving the linear programming problem. Neighbor discovery plays a crucial role in the formation of wireless sensor networks and mobile networks where the power of sensors (or mobile devices) is constrained. Due to the difficulty of clock synchronization, many asynchronous protocols based on wake-up scheduling have been developed over the years in order to enable timely neighbor discovery between neighboring sensors while saving energy. However, existing protocols are not fine-grained enough to support all heterogeneous battery duty cycles, which can lead to a more rapid deterioration of long-term battery health for those without support. Existing research can be broadly divided into two categories according to their neighbor-discovery techniques — the quorum based protocols and the co-primality based protocols. In this paper, we propose two neighbor discovery protocols, called Hedis and Todis, that optimize the duty cycle granularity of quorum and co-primality based protocols respectively, by enabling the finest-grained control of heterogeneous duty cycles. We compare the two optimal protocols via analytical and simulation results, which show that although the optimal co-primality based protocol (Todis) is simpler in its design, the optimal quorum based protocol (Hedis) has a better performance since it has a lower relative error rate and smaller discovery delay, while still allowing the sensor nodes to wake up at a more infrequent rate.