Mayank Raj

Using data mules to preserve source location privacy in Wireless Sensor Networks

Abstract Wireless Sensor Networks (WSNs) have many promising applications for monitoring critical regions, like military surveillance and wildlife monitoring. In such applications, it is critical to protect the location of the source sensor that generates the data, as exposure of this information usually reveals the location of the object being monitored. Traditional security mechanisms, like encryption, have been proven to be ineffective as the location of the source can also be revealed by analyzing the traffic flow in the network. In this paper, we investigate the source-location privacy issue. We first propose a realistic semi-global eavesdropping attack model and show its effectiveness in compromising an existing source-location preserving technique. Furthermore, to measure source location privacy against the semi-global eavesdropper, we define a model for α -angle anonymity. Additionally, we design a new protocol called Mule-Saving-Source (MSS) that preserves α -angle anonymity by adapting the conventional function of data mules. We theoretically analyze the delay incurred by using data mules in MSS, and we examine via extensive simulations the trade-off between the delay and privacy preservation under different data mule mobility patterns. We categorize the delay in MSS as being caused primarily due to the buffering time at the source sensor and the data mules. Motivated by this observation, we propose two modifications to MSS, Mule-Saving-Source-Shortest Path (MSS-SP) and Mule-Saving-Source-Two Level (MSS-TL), both aimed at reducing the total delay by reducing the buffering time at the data mule and source respectively. Through theoretical analysis, we examine the delay in the proposed modifications and evaluate their performance with the MSS protocol using a comprehensive set of simulations. Furthermore, to study the impact of the mobility model of the data mules on the MSS protocol, we compare the performance of the MSS protocol by changing the mobility model of data mules to a Random Waypoint based model.

An integrated cloud-based framework for mobile phone sensing

Nowadays mobile phones are not only communication devices, but also a source of rich sensory data that can be collected and exploited by distributed people-centric sensing applications. Among them, environmental monitoring and emergency response systems can particularly benefit from people-based sensing. Due to the limited resources of mobile devices, sensed data are usually offloaded to the cloud. However, state-of-the art solutions lack a unified approach suitable to support diverse applications, while reducing the energy consumption of the mobile device. In this paper, we specifically address mobile devices as rich sources of multi-modal data collected by users. In this context, we propose an integrated framework for storing, processing and delivering sensed data to people-centric applications deployed in the cloud. Our integrated platform is the foundation of a new delivery model, namely, Mobile Application as a Service (MAaaS), which allows the creation of people-centric applications across different domains, including participatory sensing and mobile social networks. We specifically address a case study represented by an emergency response system for fire detection and alerting. Through a prototype testbed implementation, we show that the proposed framework can reduce the energy consumption of mobile devices, while satisfying the application requirements.

A Storage Infrastructure for Heterogeneous and Multimedia Data in the Internet of Things

The Internet of Things (IoT) consists of networked objects deployed worldwide and connected over the Internet. As a consequence, the major aspects of the IoT are represented by the heterogeneity and the huge number of the participating devices. These aspects also constitute the major challenges in the definition of a storage infrastructure suitable for IoT applications. In this paper, we introduce a novel data model and storage infrastructure for the IoT to address these challenges. Different from other works in the literature, we exploit a document-oriented approach and show how it is suitable to support both heterogeneous and multimedia data. Our solution is built on top of the CouchDB database server, offers a Restful API, and provides a rich set of features targeted to IoT applications. Moreover, we devise optimized schemes for uploading documents which are specifically tailored to resource-constrained IoT devices. We evaluate our proposed schemes both analytically and with experiments in a real system.

Fixed mobile convergence: challenges and solutions

Fixed mobile convergence is an emerging technology, which aims at integration and creation of a unified communication infrastructure from fixed and wireless mobile networks. In this converged communication infrastructure, users move across networks and access services seamlessly using different devices. Voice and video over IP is one of the emerging technologies in the realization of FMC. In this article we first discuss the features and technological requirements of FMC networks. We then present an architecture for implementing FMC and describe how to realize the voice call continuity of VoIP sessions using the current IP infrastructure. We further identify the requirements for VVoIP in ensuring video call continuity as well as the challenges associated with the migration from voice to multimedia services.

Fast track article: Deployment of robust wireless sensor networks using gene regulatory networks: An isomorphism-based approach

The communication between nodes in a Wireless Sensor Network (WSN) may fail due to different factors, such as hardware malfunctions, energy depletion, temporal variations of the wireless channel and interference. To maximize efficiency, the sensor network deployment must be robust and resilient to such failures. One effective solution to this problem is to exploit a bio-inspired approach based on Gene Regulatory Networks (GRNs). Owing to million years of evolution, GRNs display intrinsic properties of adaptation and robustness, thus making them suitable for dynamic network environments. In this article, we exploit the genetic structure of real organisms to deploy bio-inspired WSNs that are isomorphic to certain GRN sub-networks. Exhaustive structural analysis, simulations and experimental results on a WSN testbed demonstrate that bio-inspired WSNs are resilient to node and link failures and offer better performance than existing solutions for robust WSNs.

E-DARWIN: Energy Aware Disaster Recovery Network using WiFi Tethering

In this paper, we propose a novel architecture called Energy Aware Disaster Recovery Network using WiFi Tethering (E-DARWIN). The underlying idea is to make use of WiFi Tethering technology ubiquitously available on wireless devices, like smartphones and tablets, to set up an ad hoc network for data collection in disaster scenarios. To this end, we design novel mechanisms, which aid in autonomous creation of the ad hoc network, distribution of data capturing task among the devices, and collection of data with minimum delay. Specifically, we design and implement a distributed coalition formation game for distributing the data capturing task among wireless devices based on their capabilities, available energy, and network participation for higher network lifetime. Finally, we evaluate the performance of the proposed architecture using a prototype application implemented on Android platform and large-scale simulations.

Robust Deployment of Wireless Sensor Networks Using Gene Regulatory Networks

Sensor nodes in a Wireless Sensor Network (WSN) are responsible for sensing the environment and propagating the collected data in the network. The communication between sensor nodes may fail due to different factors, such as hardware failures, energy depletion, temporal variations of the wireless channel and interference. To maximize efficiency, the sensor network deployment must be robust and resilient to such failures. One effective solution to this problem has been inspired by Gene Regulatory Networks (GRNs). Owing to millions of years of evolution, GRNs display intrinsic properties of adaptation and robustness, thus making them suitable for dynamic network environments. In this paper, we exploit real biological gene structures to deploy wireless sensor networks, called bio-inspired WSNs. Exhaustive structural analysis of the network and experimental results demonstrate that the topology of bio-inspired WSNs is robust, energy-efficient, and resilient to node and link failures.

Using data mules to preserve source location privacy in wireless sensor networks

Wireless sensor networks (WSNs) have many promising applications for monitoring critical regions, such as in military surveillance and target tracking. In such applications, privacy of the location of the source sensor is of utmost importance as its compromise may reveal the location of the object being monitored. Traditional security mechanisms, like encryption, have proven to be ineffective as location of the source can also be revealed by analysis of the direction of traffic flow in the network. In this paper, we investigate the source-location privacy issue. We first propose a semi-global eavesdropping attack model which we show as being more realistic than the local or global eavesdropping attack model discussed in literature. In this model, we use a linear-regression based traffic analysis technique and show that it is effective in inferring the location of the data source under an existing source-location preserving technique. To measure source location privacy against this semi-global eavesdropping, we define an α-angle anonymity model. Additionally, we adapt the conventional function of data mules to design a new protocol for securing source location privacy, called the Mules-Saving-Source (MSS) protocol, which provides α-angle anonymity. We analyze the delay incurred by using data mules in our protocol and examine the association between privacy preservation and data delay in our protocol through simulation.

Energy adaptive mechanism for p2p file sharing protocols

Peer to peer (P2P) file sharing applications have gained considerable popularity and are quite bandwidth and energy intensive. With the increased usage of P2P applications on mobile devices, its battery life has become of significant concern. In this paper, we propose a novel mechanism for energy adaptation in P2P file sharing protocols to significantly enhance the possibility of a client completing the file download before exhausting its battery. The underlying idea is to group mobile clients based on their energy budget and impose restrictions on bandwidth usage and hence on energy consumption. This allows us to provide favoured treatment to low energy devices, while still ensuring long-term fairness through a credit based mechanism and preventing free riding. Furthermore, we show how the proposed mechanism can be implemented in a popular P2P file sharing application, the BitTorrent protocol and analyze it through a comprehensive set of simulations.

Efficient Communications in Wireless Sensor Networks Based on Biological Robustness

Robustness in wireless sensor networks (WSNs) is a critical factor that largely depends on their network topology and on how devices can react to disruptions, including node and link failures. This article presents a novel solution to obtain robust WSNs by exploiting principles of biological robustness at nanoscale. Specifically, we consider Gene Regulatory Networks (GRNs) as a model for the interaction between genes in living organisms. GRNs have evolved over millions of years to provide robustness against adverse factors in cells and their environment. Based on this observation, we apply a method to build robust WSNs, called bio-inspired WSNs, by establishing a correspondence between the topology of GRNs and that of already-deployed WSNs. Through simulation in realistic conditions, we demonstrate that bio-inspired WSNs are more reliable than existing solutions for the design of robust WSNs. We also show that communications in bio-inspired WSNs have lower latency as well as lower energy consumption than the state of the art.