Real Mobility Traces Research Articles

Delay and cost performance analysis of the diffie-hellman key exchange protocol in opportunistic mobile networks

Diffie-Hellman (DH) provides an efficient key exchange system by reducing the number of cryptographic keys distributed in the network. In this method, a node broadcasts a single public key to all nodes in the network, and in turn each peer uses this key to establish a shared secret key which then can be utilized to encrypt and decrypt traffic between the peer and the given node. In this paper, we evaluate the key transfer delay and cost performance of DH in opportunistic mobile networks, a specific scenario of MANETs where complete end-to-end paths rarely exist between sources and destinations; consequently, the end-to-end delays in these networks are much greater than typical MANETs. Simulation results, driven by a random node movement model and real human mobility traces, showed that DH outperforms a typical key distribution scheme based on the RSA algorithm in terms of key transfer delay, measured by average key convergence time; however, DH performs as well as the benchmark in terms of key transfer cost, evaluated by total key (copies) forwards.

Efficient Similarity Search for Travel Behavior

To provide travel recommendations and planning in the intelligent transportation system (ITS), we must have the ability to find similar travel patterns among users based on their real mobility traces. To measure the similarity of user’s travel behavior, various methods have been proposed, but they usually only rely on a single attributes-related metric. In comparison, studies of the semantic relationships between travel attributes remain scarce, making it difficult to construct a complete mobility pattern that reveals the relevance between users or groups. In this paper, we introduced the heterogeneous information network to build a weighted travel network with spatial–temporal GPS trajectories. The heterogeneous network allows clustering the similar users based on the connections between different attributes instead of attribute values. On this basis, we defined the meta-paths for travel and used each meta-path to formulate a similarity measure over users by improving existing PathSim (Meta-path-based similarity measures) and SimRank. Next, we aggregated different similarities, where each meta-path was automatically weighted by the learning algorithm to make predictions. The experimental results showed that the recall of the similarity measurement algorithm using multiple meta-paths has improved, which yielded better results than the performance of the algorithm using a single meta-path. The performance of the improved PathSim model under different scales of data was 15% higher than the performance of the improved SimRank model in terms of precision and 21% higher in terms of recall. Due to the area under curve values, our experiments also show that a meta-path combination is more effective than the state-of-the-art approaches and can be efficiently computed.

Improving VANET Simulation with Calibrated Vehicular Mobility Traces

Simulation is the most frequently adopted approach for evaluating protocols and algorithms for Vehicular Ad hoc Networks (VANETs) and Delay-Tolerant Networks (DTNs). Usually, simulation tools use mobility traces to build the network topology based on the existing contacts between mobile nodes. However, quality of the traces, in terms of spatial and temporal granularity of each entry in the logfile, is a key factor that impacts the network topology directly. Therefore, the reliability of the results depends strongly on the accurate representation of the real network topology by the vehicular mobility model. We show that five widely adopted existing real vehicular mobility traces present gaps, leading to fallible outcomes. In this work, we propose a solution to fill those gaps, leading to more fine-grained traces, which lead to more trustworthy simulation results. We propose and evaluate a data-based solution using clustering algorithms to fill the gaps of real-world traces. In addition, we also present the evaluation results that compare the communication graph of the original and the calibrated traces using network metrics. The results reveal that the gaps do indeed induce network topologies differing from reality, decreasing the quality of the evaluation results. To contribute to the research community, we have made the calibrated traces publicly available, so that other researchers may adopt them to improve their evaluation results.

Probabilistic and Replication Based Locking Routing Protocol for Delay Tolerant Network

A DTN node under multi copy protocols forwards a message to all connected nodes and improves message delivery at higher consumption of network resources. The probabilistic protocols control resource expenditure by transmitting a message only to nodes having high probability value to encounter its destination. The probability value is increased in terms of number of encounters and reduced by time interval since nodes last saw each other. The probability value computation does not consider impact of speed and number of hops a node has moved away from message destination. Similarly, node with low probability value to deliver a message continue its replication on high probable nodes. This replication produce congestion and messages are dropped before reaching their destinations. In this paper, we have proposed a routing protocol known as Probabilistic and Replication Based Locking Routing protocol for Delay Tolerant Network (RBL). The RBL defines a replication based locking method in which message transmissions and drop have been reduced via novel concept of locking. Moreover, probability value of a node has been computed by dynamic parameters such as the number of hops a node is away from message destination, speed and time elapsed since nodes last saw each other. The RBL has been compared with state-of-art routing protocols under renowned real time mobility traces. The proposed RBL has reduced message transmissions, message drop, hop count, end-to-end delay and increases message delivery.

A theoretical analysis of buffer occupancy for Intermittently-Connected Networks

Network congestion is a well-known problem that may heavily affect the overall network performance. Congestion control approaches in Intermittently-Connected Networks (ICNs) differ from those used in classical networks, since the assumptions of “universal connectivity” of the nodes and “global information” about the network do not hold. In this paper, an analytical framework is proposed to investigate node buffer occupancy in ICNs through bulk-arrivals/bulk-services queuing models. A relation in the z-domain between the discrete probability densities of the buffer state occupancies and of the sizes of the arriving bulks is exploited to analyze two classes of forwarding strategies for ICNs. The infinite- and finite-buffer cases are investigated, simulated, and compared in terms of the concept of stochastic order, which is also used to compare models obtained for different parameter choices. The results can be exploited for buffer dimensioning and for deriving estimates of performance metrics such as average buffer occupancy, average delivery delay, and buffer overflow probability. The theoretical analysis is complemented by numerical outcomes from a network simulator and from real mobility traces.

Energy Efficient Beaconing Control Strategy Based on Time-Continuous Markov Model in DTNs

In delay-tolerant networks (DTNs), unpredictable mobility leads to the uncertainty of network topology, and it is hard to maintain an end-to-end connection from the source to the destination. To realize the forwarding of the messages, beaconing is used to detect the contact probabilities. However, beaconing frequency not only influences the detection probability of the available communications but is concerned with the energy consumption rate as well. The higher the beaconing frequency is, the faster the energy is consumed. The energy consumption leads to the reduction of the network survival time, due to the lack of energy supply. In contrast, the lower the beaconing frequency is, the less frequently available communications are detected, which also leads to a decrease in delivery rate. Therefore, it is meaningful to find out the most suitable beaconing frequency in specific DTNs. In this paper, we propose an energy efficient dynamic beaconing control strategy in energy-constrained DTNs (DBCEC) based on the time-continuous Markov model. Six different dynamic function forms are, respectively, used to control the beaconing frequency. Simulations based on the synthetic mobility pattern and real mobility traces are conducted in the Opportunistic Network Environment (ONE), and the results show that DBCEC-E achieves a better delivery rate without affecting the average delay and the overhead ratio, compared with other beaconing control strategies.

Co-utile P2P ridesharing via decentralization and reputation management

Ridesharing has the potential to bring a wealth of benefits both to the actors directly involved in the shared trip (e.g., shared travel costs or access to high-occupancy vehicle facilities) and also to the society in general (e.g., reduced traffic congestion and CO2 emissions). However, even though ridesharing is based on a win-win collaboration and modern mobile communication technologies have significantly eased discovering and managing ride matches, the adoption of ridesharing has paradoxically decreased during the last years. In this respect, recent studies have highlighted how privacy concerns and the lack of trust among peers are crucial issues that hamper the success of ridesharing. In this paper, we tackle both of these issues by means of (i) a fully decentralized P2P ridesharing management network that avoids centralized ride-matching agencies (and hence private data compilation by such agencies); and (ii) an also decentralized reputation management protocol that brings trust among peers, even when they have not previously interacted. Our proposal rests on the recently proposed notion of co-utility (essentially, self-enforcing and mutually beneficial collaboration), which ensures that rational (even purely selfish) peers will find no incentives to deviate from the prescribed protocols. We have tested our system by using data gathered from real mobility traces of cabs in the San Francisco Bay area, and according to several metrics that quantify the degree of adoption of ridesharing and the ensuing individual and societal benefits.

Friendly-Sharing: Improving the Performance of City Sensoring through Contact-Based Messaging Applications.

Regular citizens equipped with smart devices are being increasingly used as “sensors” by Smart Cities applications. Using contacts among users, data in the form of messages is obtained and shared. Contact-based messaging applications are based on establishing a short-range communication directly between mobile devices, and on storing the messages in these devices for subsequent delivery to cloud-based services. An effective way to increase the number of messages that can be shared is to increase the contact duration. We thus introduce the Friendly-Sharing diffusion approach, where, during a contact, the users are aware of the time needed to interchange the messages stored in their buffers, and they can thus decide to wait more time in order to increase the message sharing probability. The performance of this approach is anyway closely related to the size of the buffer in the device. We therefore compare various policies either for the message selection at forwarding times and for message dropping when the buffer is full. We evaluate our proposal with a modified version of the Opportunistic Networking Environment (ONE) simulator and using real human mobility traces.

SNVC: Social networks for vehicular certification

Vehicular Disruption Tolerant Networks appear due to the search of information by drivers, which are organized into mobile ad hoc networks that may suffer from long interruptions. In such networks, both humans as well as machines contribute with information about the traffic and road conditions. As humans can be biased or may be compelled to disseminate false information for personal gains, the network should incorporate mechanisms to assert the trust and reputation of the information sources. This article proposes an inter-vehicle certification mechanism based on social networks to enable reliable exchange of messages called SNVC - Social Networks for Vehicular Certification. It enables mobile devices to share keys through direct contacts between two acquaintances that warrant their identity, so they mutually sign their certificates, establishing trust in the cyber-physical system. Certificates signed by the friend of a user can be validated if the public key of this friend is validated by a friend in common in the social network. Further, a reputation system rates users or devices based on the reliability of the information that they produce. The evaluation of the mechanism shows the behavior of vehicular networks using real mobility traces. The results show the feasibility of the trust in friends as a way to improve the exchange of reliable messages in a Mobile Opportunistic Network. Also, the attribution of reputation for reliable information promotes the recognition of trustworthy users.

A personalized recommender system for pervasive social networks

The current availability of interconnected portable devices, and the advent of the Web 2.0, raise the problem of supporting anywhere and anytime access to a huge amount of content, generated and shared by mobile users. On the one hand, users tend to be always connected for sharing experiences and conducting their social interactions with friends and acquaintances, through so-called Mobile Social Networks, further improving their social inclusion. On the other hand, the pervasiveness of communication infrastructures spreading data (cellular networks, direct device-to-device contacts, interactions with ambient devices as in the Internet-of-Things) makes compulsory the deployment of solutions able to filter off undesired information and to select what content should be addressed to which users, for both (i) better user experience, and (ii) resource saving of both devices and network.In this work, we propose a novel framework for pervasive social networks, called Pervasive PLIERS (p-PLIERS), able to discover and select, in a highly personalized way, contents of interest for single mobile users. p-PLIERS exploits the recently proposed PLIERS tag-based recommender system (Arnaboldi et al., 2016) as a context reasoning tool able to adapt recommendations to heterogeneous interest profiles of different users. p-PLIERS effectively operates also when limited knowledge about the network is maintained. It is implemented in a completely decentralized environment, in which new contents are continuously generated and diffused through the network, and it relies only on the exchange of single nodes’ knowledge during proximity contacts and through device-to-device communications. We evaluated p-PLIERS by simulating its behavior in three different scenarios: a big event (Expo 2015), a conference venue (ACM KDD’15), and a working day in the city of Helsinki. For each scenario, we used real or synthetic mobility traces and we extracted real datasets from Twitter interactions to characterize the generation and sharing of user contents.

SAVING: socially aware vehicular information-centric networking

Mobile devices today are constantly generating and consuming a tremendous amount of content on the Internet. Caching of such massive data is beyond the capacity of existing cellular networks in terms of both cost and bandwidth due to its connection-centric nature. The increasing demand for content poses fundamental questions like where, what, and how to and retrieve cached content. Leveraging the shift toward a content-centric networking paradigm, we propose to content close to the mobile user to avoid wasting resources and decrease access delays. Therefore, we present SAVING, a socially aware vehicular information-centric networking system for content storage and sharing over vehicles using their computing, caching, and communication (3Cs) capabilities. The encapsulated 3Cs are exploited first to identify the potential candidates, socially important, to in the fleet of vehicles. To achieve this, we propose a novel vehicle ranking system allowing a smart vehicle to autonomously compute its eligibility to address the question, where to cache. The identified vehicles then collaborate to efficiently cache content between them based on the content popularity and availability to decide what and how to cache. Finally, to facilitate efficient content distribution, we present a socially aware content distribution protocol allowing vehicles to communicate to address the question how to retrieve cached content. Implementation results for SAVING on 2986 vehicles with realistic mobility traces suggests it as an efficient and scalable computing, caching, and communication system.

Characterization of a delay and disruption tolerant network in the Amazon basin

Most cities in the Amazon have no data communication infrastructure, and rivers are most of the time the only access mode to connect small cities to urban centers. In this paper, we investigate the deployment of vehicular ad hoc networks (VANETs) formed by boats along the rivers of the Amazon as an alternative to interconnect the small cities to the capital Manaus. Given that boats will opportunistically connect to each other, we have the scenario of a delay and disruption tolerant network (DTN). In a nutshell, the idea is to take advantage of the contacts between the boats that travel daily transporting passengers and products to small cities, communities, villages, as well as to other states. We evaluate the capacity of a DTN formed by boats, using simulations with real boat traffic and mobility traces captured in the Amazon Basin as input. Results show that potential of raw data transfer in this large-scale DTN can achieve 1.38 TB over a week for some 45 hours of contact time between boats.

OPENRP: a reputation middleware for opportunistic crowd computing

The concepts of wisdom of crowd and collective intelligence have been utilized by mobile application developers to achieve large-scale distributed computation, known as crowd computing. The profitability of this method heavily depends on users' social interactions and their willingness to share resources. Thus, different crowd computing applications need to adopt mechanisms that motivate peers to collaborate and defray the costs of participating ones who share their resources. In this article, we propose OPENRP, a novel, lightweight, and scalable system middleware that provides a unified interface to crowd computing and opportunistic networking applications. When an application wants to perform a device-to-device task, it delegates the task to the middleware, which takes care of choosing the best peers with whom to collaborate and sending the task to these peers. OPENRP evaluates and updates the reputation of participating peers based on their mutual opportunistic interactions. To show the benefits of the middleware, we simulated the behavior of two representative crowdsourcing applications: message forwarding and task offloading. Through extensive simulations on real human mobility traces, we show that the traffic generated by the applications is lower compared to two benchmark strategies. As a consequence, we show that when using our middleware, the energy consumed by the nodes is reduced. Finally, we show that when dividing the nodes into selfish and altruistic, the reputation scores of the altruistic peers increase with time, while those of the selfish ones decrease.

The Sleepy Bird Catches More Worms: Revisiting Energy Efficient Neighbor Discovery

Neighbor discovery is a primary enabling ability for many emerging mobile applications. Due to its significant impact on the energy budget of battery equipped devices, energy preserving solutions have been investigated for a long time, often introducing duty cycling. Ultimately, these solutions settle for trade-offs between energy and performance, leaving the final decision on how much energy to allocate to neighbor discovery in the hands of application developers or system engineers. In other words, someone must decide if an improvement in the quality of the discovery (e.g. better discovery latency) is worth an increase in energy consumption. In this paper, we devise a different approach in order to answer the following basic question: how many contacts can a smartphone discover using its battery energy budget? The answer clearly depends on the adopted discovery algorithm, on the mobility conditions, and on the stochastic characteristics of the encounters. However, we demonstrate that there is a natural optimum duty cycle that maximizes the number of discovered contacts in almost every real application scenario. This optimum is natural in the sense that it does not depend on any system level parameter or performance requirements. It depends uniquely on the stochastic characteristics of the meeting process between the nodes. We present an analytic analysis and devise a practical algorithm that dynamically adapts the duty cycle length to the time-varying context, without the need to make assumptions on (or predict) the distribution of the contact duration. The findings presented in the paper are validated against data coming from real human mobility traces and implemented on a mobile application.

Routing Protocols for Delay Tolerant Networks: A Reference Architecture and a Thorough Quantitative Evaluation

In this paper, we propose a reference architecture for Delay-Tolerant Networking (DTN) routing protocols and a thorough quantitative evaluation of many protocols proposed in the literature. We categorize DTN protocols according to their use of the three techniques that are the key elements of our reference architecture: queue management, forwarding and replication. Queue management orders and manages the messages in the node’s buffer; forwarding selects the messages to be delivered when there is a contact; and finally, replication bounds the number of replicas in the network. Contrary to most previous papers, where either only qualitative comparisons have been presented or only a single category of protocols has been analyzed, in our work, we discuss the results of our experimental activity on many of the DTN protocols in the literature. Our results, which have been obtained both using synthetic and real mobility traces, show that an effective combination of the proposed techniques can significantly improve the performance of the protocols in terms of delivery ratio, overhead and delay.

Discovering Mobile Applications in Cellular Device-to-Device Communications: Hash Function and Bloom Filter-Based Approach

We propose a code-based discovery protocol for cellular device-to-device (D2D) communications. To realize proximity-based services such as mobile social networks and mobile marketing using D2D communications, each device should first discover nearby devices, which have mobile applications of interest, by using a discovery protocol. The proposed discovery protocol makes use of a short discovery code that contains compressed information of mobile applications in a device. A discovery code is generated by using either a hash function or a Bloom filter. When a device receives a discovery code broadcast by another device, the device can approximately find out the mobile applications in the other device. The proposed protocol is capable of quickly discovering massive number of devices while consuming a relatively small amount of radio resources. We analyze the performance of the proposed protocol under the random direction mobility model and a real mobility trace. By simulations, we show that the analytical results well match the simulation results and that the proposed protocol greatly outperforms a simple non-filtering protocol.

Providing Privacy for User Location by Collaboration

Location-aware smart phones support various location-based services (LBSs): users query the LBS server and learn on the fly about their surroundings. However, such queries give away private information, enabling the LBS to track users. We address this problem by proposing a user-collaborative privacy-preserving approach for LBSs. Our solution does not require changing the LBS server architecture and does not assume third party servers; yet, it significantly improves users’ location privacy. The gain stems from the collaboration of mobile devices: they keep their context information in a buffer and pass it to others seeking such information. Thus, a user remains hidden from the server, unless all the collaborative peers in the vicinity lack the sought information. We evaluate our scheme against the Bayesian localization attacks that allow for strong adversaries who can incorporate prior knowledge in their attacks. We develop a novel epidemic model to capture the, possibly timedependent, dynamics of information propagation among users. Used in the Bayesian inference framework, this model helps analyze the effects of various parameters, such as users’ querying rates and the lifetime of context information, on users’ location privacy. The results show that our scheme hides a high fraction of locationbased queries, thus significantly enhancing users’ location privacy. Our simulations with real mobility traces corroborate our model-based findings. Finally, our implementation on mobile platforms indicates that it is lightweight and the cost of collaboration is negligible. Key words—Mobile Networks, Location-based Services, Location Privacy, Bayesian Inference Attacks, Epidemic Models

Resource Aware Routing in Heterogeneous Opportunistic Networks

In heterogeneous scenarios, nodes greatly differ with respect to their capabilities and mobility patterns. Moreover, episodic connectivity in opportunistic networks further aggravates the problem of finding a suitable next-hop to obviate unnecessary utilization of network resources. In this paper, we present a Multiattribute Routing Scheme (MARS) based on “Simple Multiattribute Rating Technique” (SMART) that collects samples of vital information about a node's different characteristics. This stochastic picture of a node behavior in multiple dimensions is then effectively employed in calculating its next-hop fitness. We also devise a method based on learning rules of neural networks which dynamically determines relative importance of each dimension to maximize next-hop utility of a node. With simulations, using synthetic and real mobility traces against well-known utility-based schemes, we show that MARS can achieve better delivery ratios despite introducing limited redundancy within the network.

Optimal Base Station Scheduling for Device-to-Device Communication Underlaying Cellular Networks

With the emergence of demands for local area services, device-to-device (D2D) communication is proposed as a vital technology component for the next generation cellular communication to increase spectral efficiency and enhance system capacity. In a D2D underlaying cellular system, through carefully coordinated dynamic scheduling, some base stations can be shut down and the corresponding load can be transferred to the D2D communication, which provides significant energy savings without much impact on the overall system performance. In this paper, we study the base station scheduling in D2D communication given a large scale of users. By formulating this problem into a flow maximization problem that optimizes the data transmission from the base stations to the users, we obtain the optimal base station scheduling solution. Through extensive simulations with real human mobility traces, we demonstrate the effectiveness of our proposed scheme, which significantly enhances the system throughput compared with existing strategies.

Energy-Efficient Wi-Fi Sensing Policy Under Generalized Mobility Patterns With Aging

An essential condition precedent to the success of mobile applications based on Wi-Fi (e.g., iCloud) is an energy-efficient Wi-Fi sensing. Clearly, a good Wi-Fi sensing policy should factor in both inter-access point (AP) arrival time (IAT) and contact duration time (CDT) distributions of each individual. However, prior work focuses on limited cases of those two distributions (e.g., exponential) or proposes heuristic approaches such as Additive Increase (AI). In this paper, we first formulate a generalized functional optimization problem on Wi-Fi sensing under general inter-AP and contact duration distributions and investigate how each individual should sense Wi-Fi APs to strike a good balance between energy efficiency and performance, which is in turn intricately linked with users mobility patterns. We then derive a generic optimal condition that sheds insights into the aging property, underpinning energy-aware Wi-Fi sensing polices. In harnessing our analytical findings and the implications thereof, we develop a new sensing algorithm, called Wi-Fi Sensing with AGing (WiSAG), and demonstrate that WiSAG outperforms the existing sensing algorithms up to 37% through extensive trace-driven simulations for which real mobility traces gathered from hundreds of smartphones is used.