Integrating haptic feedback with audio and video not only expands the perceptual dimensions of multimedia applications but also enhances user engagement and experience. However, higher signal sampling rates and multi-degree-freedom in haptic interaction increase data significantly. For low-latency and reliable transmission of haptic signal (i.e. tactile and kinesthetic signals), efficient haptic coding is crucial. Existing algorithms overlook haptic signal characteristics, leaving room for improvement. We analyze the statistical characteristics of kinesthetic signals in-depth. Based on the local linear characteristics of position and velocity signals, and the sparse distribution of force signal, we propose an improved kinesthetic coding algorithm by combining dead-zone coding with segmented linear prediction. Extensive experiments on the standard datasets of the IEEE P1918.1.1 Haptic Codecs Task Group demonstrate the superior performance compared to state-of-the-art methods, achieving a more than halved reduction in data transmission rates with high signal-to-noise ratios and structural similarity.

Abstract Analyzing Android malware sample is a time‐consuming and error‐prone work. Automatic techniques to perform suspicious apps screening and threat level evaluation is highly desired by the antivirus industry. In this paper, we proposed a novel automated framework called DroidScreening to speed up the Android malware analysis process and to assist antivirus analyst in generating the heuristics pattern for scanner. DroidScreening employs lazy associative classification (LAC) algorithms to produce classification model by learning from malicious evidence features extracted by using static analysis on Android application package files. Moreover, we proposed a novel high‐interaction execution environment that can interact with malicious Android code, so that the analysis samples are induced to start executing their true malicious behavior. Experimentation on malware datasets and using LAC with traditional learning approaches show that the LAC algorithms outperformed other classification algorithms. Finally, we analyzed the performance of DroidScreening and compare it with other similar research work. Copyright © 2016 John Wiley & Sons, Ltd.

Wireless sensor networks (WSNs) deployed for mission-critical applications face the fundamental challenge of meeting stringent spatiotemporal performance requirements using nodes with limited sensing capacity. Although advance network planning and dense node deployment may initially achieve the required performance, they often fail to adapt to the unpredictability and variability of physical reality. This paper explores efficient use of mobile sensors to address limitations of static WSNs for target detection. We propose a data-fusion-based detection model that enables static and mobile sensors to effectively collaborate in target detection. An optimal sensor movement scheduling algorithm is developed to minimize the total moving distance of sensors while achieving a set of spatiotemporal performance requirements including high detection probability, low system false alarm rate, and bounded detection delay. The effectiveness of our approach is validated by extensive simulations based on real data traces collected by 23 sensor nodes.

We investigated whether flapping flight has an inherent stability by analyzing the inertial and aerodynamic effects of flapping wings on body dynamics. Based on wing and body kinematics of free flying fruit flies during rapid maneuvers, we found a passive counter torque due to body rotation. It is identified both in simulation through quasi-steady state aerodynamic model and through experiments on a dynamically scaled robotic wing. An analytical form is derived correspondingly. In the turning yaw axis, the estimated damping coefficient of flapping wings is significantly higher than body frictional damping; this indicates a passive deceleration during turning. By simulating insect to rotate about each principal axis of inertial and body frames, we calculated the corresponding damping coefficients, and further analyzed the attitude stability. The result reveals that, passive damping of flapping flight, while does not necessarily lead to a stable full body dynamics, provides a considerable passive restoring torque that could be critical for flight stabilization and control in the design of micro aerial vehicles. Preliminary analysis on the scaling parameters of passive damping was also performed.

Wings of insects are flexible structures. Although there has been much recent progress in the area of insect flight aerodynamics, very little is known about how wing flexibility influences aerodynamic forces during flapping flight. We investigated this question using a dynamically scaled mechanical model of insect wings. Using a suite of wings with varying flexural stiffness (EI) values, we generated aerodynamic polar plots to characterize the force coefficients of flexible wings. These polar plots showed that the aerodynamic performance of the wings varied with wing flexibility. In general, aerodynamic force production decreased with increasing flexibility. Both lift and drag coefficients of wings are greater when wings are more rigid. However, at very high angles of attack, flexible wings generated greater lift than a rigid wing. In addition, the ratio of lift-to-drag also decreased with increasing flexibility. These data show that flexible wings offer no aerodynamic advantage over a rigid wing under steady state circumstances. Because wing material in insects is usually flexible but reinforced by wing veins, we tested the hypothesis that wing veins enhance the aerodynamic performance of wings by increasing their effective stiffness. Our data suggests that even a very basic framework of appropriately placed wing veins can substantially increase the functional rigidity of the wings thereby enhancing its aerodynamic performance.

Wireless sensor networks (WSNs) deployed for mission-critical applications face the fundamental challenge of meeting stringent spatiotemporal performance requirements using nodes with limited sensing capacity. Although advance network planning and dense node deployment may initially achieve the required performance, they often fail to adapt to the unpredictability of physical reality. This paper explores efficient use of mobile sensors to address the limitations of static WSNs in target detection. We propose a data fusion model that enables static and mobile sensors to effectively collaborate in target detection. An optimal sensor movement scheduling algorithm is developed to minimize the total moving distance of sensors while achieving a set of spatiotemporal performance requirements including high detection probability, low system false alarm rate and bounded detection delay. The effectiveness of our approach is validated by extensive simulations based on real data traces collected by 23 sensor nodes.

Context-aware computing is characterized by the software's ability to continuously adapt its behavior to an environment over which it has little control. This style of interaction is imperative in ad hoc mobile networks that consist of numerous mobile hosts coordinating opportunistically via transient wireless connections. In this paper, we provide a formal abstract characterization of an application's context that extends to encompass a neighborhood within the ad hoc network. We provide a context specification mechanism that allows individual applications to tailor their operating contexts to their personalized needs. We describe a context maintenance protocol that provides this context abstraction in ad hoc networks through continuous evaluation of the context. This relieves the application developer of the obligation of explicitly managing mobility and its implications on behavior. We also characterize the performance of this protocol in ad hoc networks through simulation experiments. Finally, we examine real-world application examples demonstrating its use.

Abstract — In this paper, we propose and study a novel dis-tributed vehicle traffic information system using a wireless sensor network. Besides the system model, the contributions of the paper include a road network aware information publication protocol, a distributed query processing protocol with transient memory, and the adaptive interaction model between the two, all in the context of a vehicle traffic information system. Both theoretical and simulation results are presented. I.

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.

In this paper, we investigate efficient strategies for supporting on-demand information dissemination and gathering in large-scale wireless sensor networks. In particular, we propose a "comb-needle" discovery support model resembling an ancient method: use a comb to help find a needle in sand or a haystack. The model combines push and pull for information dissemination and gathering. The push component features data duplication in a linear neighborhood of each node. The pull component features a dynamic formation of an on-demand routing structure resembling a comb. The comb-needle model enables us to investigate the cost of a spectrum of push and pull combinations for supporting query and discovery in large-scale sensor networks. Our result shows that the optimal routing structure depends on the frequency of query occurrence and the spatial-temporal frequency of related events in the network. The benefit of balancing push and pull for information discovery is demonstrated

This article proposes the minimum power configuration (MPC) approach to power management in wireless sensor networks. In contrast to earlier research that treats different radio states (i.e., transmission/reception/idle) in isolation, MPC integrates them in a joint optimization problem that depends on both the set of active nodes and the transmission power. We propose four approximation algorithms with provable performance bounds and two practical routing protocols. Simulations based on realistic radio models show that the MPC approach can conserve more energy than existing minimum power routing and topology control protocols. Furthermore, it can flexibly adapt to network workload and radio platforms.

There has been a lot of work on localization for sensor networks. Most localization schemes assume an already deployed network. Very little research has been done for active sensor deployment with mobile robots. In this paper, we present a distributed sequential localization process which is tightly integrated with robotic sensor deployment. Being location-aware, sensors can be placed in advantageous locations to avoid big localization errors. In turn, the accurate localization result helps further sensor deployment. We compare our distributed sequential localization scheme with five existing localization algorithms, both through simulations and through repeated runs on a mote testbed. Results show that the distributed sequential localization algorithm produces comparable results to the state-of-the-art centralized localization algorithms. Experiments also show that pre-post processing plays important roles at getting good localization results for all localization algorithms

One of the most common communication patterns in sensor networks is routing data to a base station, while the base station can be either static or mobile. Even in static cases, a static spanning tree may not survive for a long time due to failures of sensor nodes. In this paper, we present an adaptive spanning tree routing mechanism, using real-time reinforcement learning strategies. We demonstrate via simulation that without additional control packets for tree maintenance, adaptive spanning trees can maintain the “best” connectivity to the base station, in spite of node failures or mobility of the base station. 1.

We consider applications of sensor networks wherein data packets generated by every node have to reach the base station. This results in a many-to-one communication paradigm referred to as convergecast. We are interested in determining a TDMA schedule that minimizes the total time required to complete the convergecast. We consider a simple version of the problem wherein every node generates exactly one packet. We propose a distributed convergecast scheduling algorithm that requires at most 3N timeslots, where N represents the number of nodes in the network. Through extensive simulations, we demonstrate that actual number of timeslots needed is around 1.5N. In addition to time efficiency, we prove that our convergecast scheduling algorithm requires the nodes to buffer no more than two packets at any instance. We propose a sleep schedule that conserves more than 50% of the energy. We present simulation results for a real application scenario to show that our convergecast scheduling algorithm performs significantly better than existing convergecast algorithms.

Greedy geographic routing is an attractive localized routing scheme for wireless sensor networks due to its efficiency and scalability. However, greedy geographic routing may fail due to routing voids on random network topologies. We study greedy geographic routing in an important class of wireless sensor networks (e.g., surveillance or object tracking systems) that provide sensing coverage over a geographic area. Our analysis and simulation results demonstrate that an existing geographic routing algorithm, greedy forwarding (GF), can successfully find short routing paths based on local states in sensing-covered networks. In particular, we derive theoretical upper bounds on the network dilation of sensing-covered networks under GF. We also propose a new greedy geographic routing algorithm called Bounded Voronoi Greedy Forwarding (BVGF) that achieves path dilation lower than 4.62 in sensing-covered networks as long as the communication range is at least twice the sensing range. Furthermore, we extend GF and BVGF to achieve provable performance bounds in terms of total number of transmissions and reliability in lossy networks.

This paper proposes the minimum power configuration (MPC) approach to energy conservation in wireless sensor networks. In sharp contrast to earlier research that treats topology control, power-aware routing, and sleep management in isolation, MPC integrates them as a joint optimization problem in which the power configuration of a network consists of a set of active nodes and the transmission powers of the nodes. We show through analysis that the minimum power configuration of a network is inherently dependent on the data rates of sources. We propose several approximation algorithms with provable performance bounds compared to the optimal solution, and a practical Minimum Power Configuration Protocol (MPCP) that can dynamically (re)configure a network to minimize the energy consumption based on current data rates. Simulations based on realistic radio models of the Mica2 motes show that MPCP can conserve significantly more energy than existing minimum power routing and topology control protocols.

We consider the problem of information dissemination in wireless ad-hoc networks. Capacity constraints and the varying needs of applications lead to the need for delivering data at varying resolutions as a function of distance. We introduce a primitive, which we call visibility, to quantify the variable-resolution requirement. We design and analyze new variable resolution multicast algorithms based on simple probabilistic schemes to achieve a required visibility. We also examine how multi-path effects influence visibility and propagation algorithms. Finally, we consider the problem of designing the visibility function to maximize an overall utility function (specified by applications) over the network. We derive a condition relating optimal utility and visibility, which can be used to derive the optimal visibility for a class of utility functions. 1

This article presents FAR, a Face-Aware Routing protocol for mobicast---a spatiotemporal variant of multicast tailored for sensor networks with environmental mobility. FAR features face-routing and timed-forwarding for delivering a message to a mobile delivery zone. Both analytical and statistical results show that FAR achieves reliable spatial and just-in-time message delivery with only moderate communication and memory overhead. This article also presents a novel distributed algorithm for spatial neighborhood discovery for FAR bootstrapping. The spatiotemporal performance and reliability of FAR are demonstrated via network simulations.

This work presents a novel protocol for a spatiotemporal variant of multicast called mobicast, designed to support message delivery in ad hoc sensor networks. The spatiotemporal character of mobicast relates to the obligation to deliver a message to all the nodes that will he present at time t in some geographic zone Z, where both the location and shape of the delivery zone are a function of time over some interval (t/sub start/, t/sub end/). The protocol, called face-aware routing (FAR), exploits ideas adapted from existing applications of face routing to achieve reliable mobicast delivery. The key features of the protocol are a routing strategy, which uses information confined solely to a node's immediate spatial neighborhood, and a forwarding schedule, which employs only local topological information. Statistical results shows that, in uniformly distributed random disk graphs, the spatial neighborhood size is usually less than 20. This suggests that FAR is likely to exhibit a low average memory cost. An estimation formula for the average size of the spatial neighborhood in a random network is another analytical result reported in this paper. This paper also presents a novel and low cost distributed algorithm for spatial neighborhood discovery.

Greedy geographic routing is attractive in wireless sensor networks due to its efficiency and scalability. However, greedy geographic routing may incur long routing paths or even fail due to routing voids on random network topologies. We study greedy geographic routing in an important class of wireless sensor networks that provide sensing coverage over a geographic area (e.g., surveillance or object tracking systems). Our geometric analysis and simulation results demonstrate that existing greedy geographic routing algorithms can successfully find short routing paths based on local states in sensing-covered networks. In particular, we derive theoretical upper bounds on the network dilation of sensing-covered networks under greedy geographic routing algorithms. Furthermore, we propose a new greedy geographic routing algorithm called Bounded Voronoi Greedy Forwarding (BVGF) that allows sensing-covered networks to achieve an asymptotic network dilation lower than 4:62 as long as the communication range is at least twice the sensing range. Our results show that simple greedy geographic routing is an effective routing scheme in many sensing-covered networks.

In this paper we investigate efficient strategies for supporting on-demand information dissemination and gathering in large-scale vwireless sensor networks. In particular, we propose a "comb-needle" discovery support model resembling an ancient method: use a comb to help find a needle in sands or a haystack. The model combines push and pull for information dissemination and gathering. The push component features data duplication in a linear neighborhood of each node. The pull component features a dynamic formation of an on-demand routing structure resembling a comb. The comb-needle model enables us to investigate the cost of a spectrum of push and pull combinations for supporting discovery and query in large scale sensor networks. Our result shows that the optimal routing structure depends on the frequency of query occurrence and the spatial-temporal frequency of related events in the network. The benefit of balancing push and pull for discovery in large scale geometric networks are demonstrated. We also raise the issue of query coverage in unreliable networks and investigate how redundancy can improve the coverage via both theoretical analysis and simulation. Last, we study adaptive strategies for the case where the frequencies of query and events are unknown a priori and time-varying.

The design of ad hoc mobile applications often requires the availability of a consistent view of the application state among the participating hosts. Such views are important because they simplify both the programming and verification tasks. We argue that preventing the occurrence of unannounced disconnection is essential to constructing and maintaining a consistent view in the ad hoc mobile environment. In this light, we provide the specification for a partitionable group membership service supporting ad hoc mobile applications and propose a protocol for implementing the service. A unique property of this partitionable group membership is that messages sent between group members are guaranteed to be delivered successfully, given appropriate system assumptions. This property is preserved over time despite movement and frequent disconnections. The protocol splits and merges groups and maintains a logical connectivity graph based on a notion of safe distance. An implementation of the protocol in Java is available for testing. This work is used in an implementation of LIME, a middleware for mobility that supports transparent sharing of data in both wired and ad hoc wireless environments.

Sensor networks often involve the monitoring of mobile phenomena. We believe this task can be facilitated by a spatiotemporal multicast protocol which we call "mobicast". Mobicast is a novel spatiotemporal multicast protocol that distributes a message to nodes in a delivery zone that evolves over time in some predictable manner. A key advantage of mobicast lies in its ability to provide reliable and just-in-time message delivery to mobile delivery zones on top of a random network topology. Mobicast can in theory achieve good spatiotemporal delivery guarantees by limiting communication to a mobile forwarding zone whose size is determined by the global worst-case value associated with a compactness metric defined over the geometry of the network (under a reasonable set of assumptions). In this work, we first studied the compactness properties of sensor networks with uniform distribution. The results of this study motivate three approaches for improving the efficiency of spatiotemporal multicast in such networks. First, spatiotemporal multicast protocols can exploit the fundamental tradeoff between delivery guarantees and communication overhead in spatiotemporal multicast. Our results suggest that in such networks, a mobicast protocol can achieve relatively high savings in message forwarding overhead by slightly relaxing the delivery guarantee, e.g., by optimistically choosing a forwarding zone that is smaller than the one needed for a 100% delivery guarantee. Second, spatiotemporal multicast may exploit local compactness values for higher efficiency for networks with non uniform spatial distribution of compactness. Third, for random uniformly distributed sensor network deployment, one may choose a deployment density to best support spatiotemporal communication. We also explored all these directions via simulation and results are presented in this paper.

Sensor networks not only have the potential to change the way we use, interact with, and view computers, but also the way we use, interact with, and view the world around us. In order to maximize the effectiveness of sensor networks, one has to identify, examine, understand, and provide solutions for the fundamental problems related to wireless embedded sensor networks. We believe that one of such problems is to determine how well the sensor network monitors the instrumented area. These problems are usually classified as coverage problems. There already exist several methods that have been proposed to evaluate a sensor network's coverage.We start from one of such method and provide a new approach to complement it. The method of using the minimal exposure path to quantify coverage has been optimally solved using a numerical approximation approach. The minimal exposure path can be thought of as the worst-case coverage of a sensor network. Our first goal is to develop an efficient localized algorithm that enables a sensor network to determine its minimal exposure path. The theoretical highlight of this paper is the closed-form solution for minimal exposure in the presence of a single sensor. This solution is the basis for the new and significantly faster localized approximation algorithm that reduces the theoretical complexity of the previous algorithm. On the other hand, we introduce a new coverage problem - the maximal exposure path - which is in a sense the best-case coverage path for a sensor network. We prove that the maximal exposure path problem is NP-hard, and thus, we provide heuristics to generate approximate solutions.In addition, we demonstrate the effectiveness of our algorithms through several simulations. In the case of the minimal single-source minimal exposure path, we use variational calculus to determine exact solutions. For the case of maximal exposure, we use networks with varying numbers of sensors and exposure models.

Context-aware computing is characterized by the ability of a software system to continuously adapt its behavior to a changing environment over which it has little or no control. Previous work along these lines presumed a rather narrow definition of context, one that was centered on resources immediately available to the component in question, e.g., communication bandwidth, physical location, etc. This paper explores context-aware computing in the setting of ad hoc networks consisting of numerous mobile hosts that interact with each other opportunistically via transient wireless interconnections. We extend the context to encompass awareness of an entire neighborhood within the ad hoc network. A formal abstract characterization of this new perspective is proposed. The result is a specification method and associated context maintenance protocol. The former enables an application to define an individualized context, one that extends across multiple mobile hosts in the ad hoc network. The latter makes it possible to delegate the continuous reevaluation of the context and the performance of operations on it to some middleware operating below the application level. This relieves application development of the obligation of explicitly managing mobility and its implications on the component's behavior.

A cooperative vehicle collision warning system is made possible by the progress in advanced positioning systems and wireless communication technology. We report our work in developing a cooperative intersection collision warning system that is not limited by the requirement of line-of-sight. The novel elements of the system include the use of a dynamic ad hoc wireless network for peer-to-peer data sharing, a new intersection collision warning algorithm, and a flexible and extensible software architecture and system design. The very same system is also capable of collision warning for other scenarios such as frontal and rear-end. Further, the tunable parameters in the algorithm make it possible for the system to adapt to the preference and capability of each individual driver.

The design of ad hoc mobile applications often requires the availability of a consistent view of the application state among the participating hosts. Such views are important because they simplify both the programming and verification tasks. Essential to constructing a consistent view is the ability to know what hosts are within proximity of each other, i.e., form a group in support of the particular application. We propose an algorithm that allows hosts within communication range to maintain a consistent view of the group membership despite movement and frequent disconnections. The novel features of this algorithm are its reliance on location information and a conservative notion of logical connectivity that creates the illusion of announced disconnection. Movement patterns and delays are factored in the policy that determines which physical connections are susceptible to disconnection.