Location-Based Lattice Mobility Model for Wireless Sensor Networks

Mobility models are used to evaluate the network protocols of the ad hoc network using the simulation. The random waypoint model is a model for mobility that is usually used for the performance evaluation of ad-hoc mobile networks. Mobile nodes have dynamic mobility in the ad hoc network, so the mobility model plays an important role to evaluate the protocol performance. In this article, we developed modify random waypoint mobility (MRWM) model based on a random waypoint for the mobile ad hoc network. In this article, the comparative analysis of modifying random waypoint mobility and random waypoint model on the ad hoc On-Demand Distance Vector (AODV) routing protocol has been done for a large wireless ad hoc network (100 nodes) with the random mobile environment for the 1800s simulation time. To enhance the confidence in the protocol widespread simulations were accomplished under heavy traffic (i.e. 80 nodes) conditions. The proposed model protocol has been investigated with the performance metrics: throughput; packet delivery ratio; packet dropping ratio; the end-to-end delay and normalized routing overhead. The obtained results revealed that the proposed modified random waypoint mobility model reduces the mobility as compared to the random waypoint mobility model and it is trace is more realistic.

In present study, researcher has investigated the impact of node mobility by evaluating the performances of different routing protocols using mobility models. Detailed simulations were carried out, using QualNet simulator with varying node density. In the simulation study the Random Waypoint, Group and File mobility models were considered. Simulation results illustrated that, the performance of routing protocols significantly affect with the mobility models. Overall, the performance of various routing protocols is highly affected under the Random Waypoint mobility model than that of File and Group mobility models. It has happened because of frequent node movement under the Random Waypoint mobility model than that of other mobility models.

NS-3 has been one of the popular network simulator software for many years especially in research related to Mobile Adhoc Networks (MANETs). In NS-3, there is provision of several mobility models including Random Waypoint (RWP) mobility model and Steady State Random Waypoint (SSRWP) mobility model. RWP mobility model suffers from the transition phase related imperfection. SSRWP mobility model overcomes this limitation of RWP mobility by allowing the steady state initialization states of nodes in terms of position, speed and pause time of mobile nodes right from the beginning of the simulation. As SSRWP mobility model avoids any requirement of warm-up (cut-off) phase of RWP mobility model, it saves a significant amount of time of warm-up (cut-off) phase as well as establishes a high level of confidence in results obtained due to absence of any subjective guess. In the present work, RWP and SSRWP mobility models have been investigated using AODV routing protocol and it has been found that a way to mitigate the misleading effect of the transition phase of RWP mobility model is to have a sufficiently large simulation time which results, to a good extent, in convergence of performance of RWP mobility model toward that of SSRWP mobility model.

Wireless networking enabled vehicles can form vehicular ad hoc mesh networks (VMeshs). Using cooperative communication among VMeshs, a local transient information could be "retained" within a given geographic region for a certain period of time, without any infrastructure help. In this paper, we study this "storage capability" of VMeshs. We analyze the scenarios of highway traffic (both one-way and two-way highway free flow traffic) and vehicular traffic in a city environment. For highway traffic, we study different properties of the "VMesh storage", using a simulation tool that accurately models the freeway vehicular mobility. For city traffic, we first perform simulations based on real traffic trace of San Francisco Yellow Cabs. Then we compare the results with the scenario where a general Random Way Point (RWP) mobility model is used. Our results show that transmission range has high impact on the storage lifetime for one-way highway traffic, and the size of the region in which we want the information stored has high impact for two-way highway traffic. For city-wide traffic, the storage's lifetime generated using San Francisco Yellow Cab trace is shorter than that obtained using the RWP mobility model. This is due to the regular movement of the cabs as compared to the random vehicle movement in the RWP mobility model.

The most widespread notion of mobility model is the representation of mobile node’s movement pattern in the wireless ad hoc networks which has a significant impact on the performance of the network protocols. In this paper, we have proposed an Animal Migration Inspired Group Mobility (AMIGM) model for mobile ad hoc networks based on the migration behavior of animals like, insects, flock of birds, schools of fishes, reptiles, amphibians, etc. The propound model tries to overcome the limitations of the existing mobility models, such as temporal dependencies, spatial dependencies, geographical restrictions and migration of nodes between the group of nodes so that it can realistically model the real world application scenarios. The proposed AMIGM model is based on Animal Migration Optimization (AMO) algorithm, in which each group of nodes has two phases namely, Migration phase and Population updating phase. In the first phase, the model simulates the movement of nodes in the group from one position to another by obeying the swarming laws. In the second phase, the model simulates joining and leaving of the nodes in the group during migration. The protocol dependent and independent performance metrics of the proposed model are compared with Random Waypoint Mobility model (RWP) and Reference Point Group Mobility model (RPGM) through ns-2 simulator.

Mobility pattern plays an important role in designing the wireless body sensor networks (WBSN) and also would affect the performance of WBSN in health care application. This paper is the first attempt to investigate the mobility model in WBSN based on the existing mobility models in wireless data networks and ad hoc networks. We first briefly review the existing mobility models in related research areas such as wireless ad hoc network and cellular networks. Further, we propose a dedicated mobility model for wireless body sensor networks by concentrating on the unique characteristics of WBSN and finally study the effects of mobility on the performance of WBSN by simulation experiments

Wireless network of wearable biomedical sensors by human body shows great potential to enhance the biometrics performance significantly. Meanwhile, it poses prominent characteristics and challenges to physicians and engineers for its particular medical application as compared to other application of wireless sensor networks (WSN). Mobility pattern plays an important role in designing the wireless body sensor networks (WBSN) and will also affect the accuracy of modeling WBSN in health care application. Much of the mobility scenarios generated in current work of wireless body sensor networks has used fairly simple models to generate the mobile topological graph, which bear little resemblance to the actual mobility patterns. This paper is the first attempt to investigate the mobility model in WBSN based on the existing mobility models in wireless data networks and ad hoc networks. We first briefly review the existing mobility models in related research areas such as wireless ad hoc network and cellular networks. Further on, we propose a dedicated and more realistic mobility model named BAMM (Body Area Mobility Model) for wireless body sensor networks by concentrating on the unique characteristics of WBSN and finally study the effects of mobility on the performance of WBSN by simulation experiments.

Many wireless sensor network applications must resolve the inherent conflict between energy efficient and nodes of coverage in wireless sensor networks to achieve desired quality of service (QoS). The wireless sensor networks (WSNs) have been the main focus in the past few years, and it is becoming a special issue. We study the problem of controlling the node locations and energy within ad-hoc sensor networks. Network survivability is a very important measure for deciding the efficiency of network protocols. It includes a measure of the network lifetime as well as the quality of service. In fact, these dynamics are very important in evaluating networks. The wireless network applications can be found in battlefield communication, homeland security, pollution sensing and traffic monitoring. As such, there has been an increasing need for defining and developing simulation frameworks for carrying out high-fidelity WSN simulation. In this cause, we present a simulation by including factors such as energy-consumption, Bandwidth, nodes, and coverage and provides an object-oriented definition of target, sensor and sink nodes, sensor and wireless communication nodes, and physical radio such as relating of nodes, mobility model and power model both energy-producing and energy-consuming components, the simulation was developed using C#.

In this paper we study the so-called random waypoint (RWP) mobility model in the context of cellular networks. In the RWP model the nodes, i.e., mobile users, move along a zigzag path consisting of straight legs from one waypoint to the next. Each waypoint is assumed to be drawn from the uniform distribution over the given convex domain. In this paper we characterise the key performance measures, mean handover rate and mean sojourn time from the point of view of an arbitrary cell, as well as the mean handover rate in the network. To this end, we present an exact analytical formula for the mean arrival rate across an arbitrary curve. This result together with the pdf of the node location, allows us to compute all other interesting measures. The results are illustrated by several numerical examples. For instance, as a straightforward application of these results one can easily adjust the model parameters in a simulation so that the scenario matches well with, e.g., the measured sojourn times in a cell.

Mobility is the most issues for the majority ofprotocols including the RPL (IPv6 Routing Protocol for LowPower and Lossy Networks). RPL a routing protocolstandardized by IETF is usually used in Internet of ThingsTechnology. It is proposed to support communications in Lowpower and Lossy Networks (LLNs). However, mobility limitsthe use of RPL protocol in realistic study. In this paper wehave classify the mobility models in two entities in order toevaluate the performances of RPL in each entity separately. Sowe have defines two different scenarios. We first, evaluatecharacteristics of RPL with a group mobility models whichcontain Reference Point Mobility Model (RPGM) and Nomadic Mobility Model (Nomadic) Mobility Models. Then wegive another evaluation of features of RPL with the Entitymobility models which contain Random Walk Mobility Model(RWK), Random Waypoint Mobility Models (RWP) and selfsimilarleast action walk (SLAW) Mobility models. The resultsshow that the type of mobility models has a direct influence onthe protocol performances. In addition, increasing of numberof nodes causes an increasing of all parameters, especially indelivered and received data. Furthermore, the group mobilitymodels give better metrics than entity mobility models in termsof lost packets, Packet Delivery Ratio (PDR) and Throughput. Also, in each type of mobility models each model providesbetter metrics than others. RPG offers best number of lostpackets and PDR than Nomadic model and lowest in terms ofThroughput while SLAW models gives the best value in allmetrics than RWK and RWP. Our simulation shows clearlythat lost packets, PDR and Throughput are directly related tothe type of mobility models.

Statistical Equivalent Models, or SEMs, have recently been proposed as a general approach to study computer simulators. By fitting a statistical model to the simulator's output, SEMs provide an efficient way to quickly explore the simulator's result. In this paper, we develop a SEM for random waypoint mobility, one of the most widely used mobility models employed by network simulators in the evaluation of communication protocols for wireless multi-hop ad hoc networks (MANETs). We chose the random waypoint mobility model as a case study of SEMs due to recent results pointing out some serious drawbacks of the model (e.g., [1]). In particular, these studies show that, under the random waypoint mobility regime, average node speed tends to zero in steady state. They also show that average node speed varies considerably from the expected average value for the time scales under consideration in most simulation analysis. In order to investigate further the behavior of the random waypoint model, we developed a SEM that captured speed decay over time under random waypoint mobility using maximum speed and terrain size as input parameters. A Bayesian approach to model fitting was employed to capture the uncertainty due to unknown parameters of the statistical model. The SEM is given by the posterior predictive distributions of the average node speed as a function of time. A direct result from our model is that, by characterizing average node speed as a function of time, our approach provides an accurate estimate of the “warm-up” period required by simulations using the random waypoint mobility model. Simulation data from the “warmup” period can then be discarded to obtain accurate protocol performance results. Given that random waypoint mobility is still, by far, the most widely used mobility model in the evaluation of MANETs, the contribution of this work is potentially significant as it allows network protocol designers to continue to use the original random waypoint mobility model and yet obtain accurate results characterizing MANET protocol performance.

The Multi-Wireless-Agent-Communication (MWAC) model uses a multiagent self-organization process through to manage communications in wireless sensor networks. This paper presents a first improvement of the MWAC model using an ant colony optimization. We introduce the use of ant colony optimization in the context of multiagent systems and wireless sensor networks. Ants are not simply deployed on the multiagent society. Each ant interacts with agents to obtain the best compromise on the routes choice to enable a reliable communication.

Although most of the studies on coverage and connectivity in wireless sensor networks (WSNs) considered two-dimensional (2D) settings, such networks can in reality be accurately modeled in a three-dimensional (3D) space. The concepts of continuum percolation theory best fit the problem of connectivity in WSNs to find out whether the network provides long-distance multihop communication. In this paper, we focus on percolation in coverage and connectivity in 3D WSNs. We say that the network exhibits a coverage percolation (respectively, connectivity percolation) when a giant covered region (respectively, giant connected component) almost surely spans the entire network for the first time. Because of the dependency between coverage and connectivity, the problem is not only a continuum percolation problem but also an integrated continuum percolation problem. Thus, we propose an integrated-concentric-sphere model to address coverage and connectivity in 3D WSNs in an integrated way. First, we compute the critical density lambdaC con above which coverage percolation in 3D WSNs will almost surely occur. Second, we compute the critical density lambdac con above which connectivity percolation in 3D WSNs will almost surely occur. Third, we compute the critical density lambdac cov-con above which both coverage and connectivity percolation in 3D WSNs will almost surely occur. For each of these three problems, we also compute their corresponding critical network degree. Our results can be helpful in the design of energy-efficient topology control protocols for 3D WSNs in terms of coverage and connectivity.

The performance evaluation of ad hoc network components through simulation allows for isolation of parameters, easy access to global quantities and a statistically significant number of repeatable trials. In designing network protocols, it is important to investigate the relationship between global network performance and the underlying locally observed network characteristics. Mobility models are used to define the movement of nodes in the simulation space. A wide variety of mobility models exist, and the choice of mobility model has significant influence over locally observed metrics. Unfortunately, many mobility models exhibit spatial and temporal non-stationarity of important metrics, such that global averages of certain metrics are not representative of local observations. This finding impacts on the development of adaptive ad hoc architectures. This paper explores the spatial stationarity of frequently used mobility models. It focuses on the importance of stationarity in relation to the evaluation of performance studies. The spatial non-stationarity of link statistics, and the impact of this artefact on network performance evaluation is examined for the Toroidal Random Waypoint and Random Direction mobility models. We show that the Toroidal Random Waypoint model exhibits spatial stationarity of link statistics. Furthermore, we demonstrate that network performance studies in spatially stationary environments produce dissimilar results to those produced by non-stationary environments. This shows the importance of spatial stationarity when investigating global network performance and locally observed network characteristics.

In this paper, we examine some of the challenges concerning error correction and multi-hop communication in wireless sensor networks (WSNs). We consider Bose, Chaudhuri and Hoquenghem (BCH) codes with different codeword lengths and error correction capabilities. In contrast with previous works, that implement coding algorithm in hardware and consequently used hardware energy profiling, in this work we consider a more realistic assumption that the node's /spl mu/-processor performs coding and energy consumption of decoding is measured using JouleTrack. Energy efficiency is used as the performance metric for communication and the interaction of multi-hop communication and coding is investigated. Simulation results show that communicating long packets is more energy efficient. If communication is multi-hop with sufficient number of hops, then, the uncoded system is beneficial. However, coding is inevitable in the case where intermediate nodes, when needed, are not available.