IEEE 2017-2018 Mobile Computing Projects in DotNet

Abstract:

Access to sensitive information is traditionally achieved through an authentication and authorization process via a username/password combination to validate a user's identity that is stored within the system being accessed. This method creates delays before sensitive information can be obtained in the circumstance that the user's identity is previously unknown, due to necessary human intervention during the pre-registration process. To expedite the retrieval of sensitive information in time-critical situations, we propose a new model of trust negotiation that defines a new trust profile that contains a collection of credentials describing the user's access history. The new model of trust negotiation utilizes role-based and attribute-based access control as part of the new trust profile to model the sensitivity of information that is being requested, where access is governed by role and credentials captured in attributes. As a result of our work, an authorization system based on trust negotiation can examine the user's history in detail, decide whether to authorize the user, and add its own record of user access to the user's trust profile that can be utilized in future attempts at access at other locations.

Abstract:

Wireless Sensor Networks (WSNs) are often deployed in hostile environments where an adversary can physically capture some of the nodes, first can reprogram, and then, can replicate them in a large number of clones, easily taking control over the network. A few distributed solutions to address this fundamental problem have been recently proposed. However, these solutions are not satisfactory. First, they are energy and memory demanding: A serious drawback for any protocol to be used in the WSN-resource-constrained environment. Further, they are vulnerable to the specific adversary models introduced in this paper. The contributions of this work are threefold. First, we analyze the desirable properties of a distributed mechanism for the detection of node replication attacks. Second, we show that the known solutions for this problem do not completely meet our requirements. Third, we propose a new self-healing, Randomized, Efficient, and Distributed (RED) protocol for the detection of node replication attacks, and we show that it satisfies the introduced requirements. Finally, extensive simulations show that our protocol is highly efficient in communication, memory, and computation; is much more effective than competing solutions in the literature; and is resistant to the new kind of attacks introduced in this paper, while other solutions are not.

Abstract:

With the exponential increase of the mobile devices and the fast development of cloud computing, a new computing paradigm called mobile cloud computing (MCC) is put forward to solve the limitation of the mobile device's storage, communication, and computation. Through mobile devices, users can enjoy various cloud computing services during their mobility. However, it is difficult to ensure security and protect privacy due to the openness of wireless communication in the new computing paradigm. Recently, Tsai and Lo proposed a privacy-aware authentication (PAA) scheme to solve the identification problem in MCC services and proved that their scheme was able to resist many kinds of existing attacks. Unfortunately, we found that Tsai and Lo's scheme cannot resist the service provider impersonation attack, i.e., an adversary can impersonate the service provider to the user. Also, the adversary can extract the user's real identity during executing the service provider impersonation attack. To address the above problems, in this paper, we construct a new PAA scheme for MCC services by using an identity-based signature scheme. Security analysis shows that the proposed PAA scheme is able to address the serious security problems existing in Tsai and Lo's scheme and can meet security requirements for MCC services. The performance evaluation shows that the proposed PAA scheme has less computation and communication costs compared with Tsai and Lo's PAA scheme.

Abstract:

In recent years, the Smart City concept has become popular for its promise to improve the quality of life of urban citizens. The concept involves multiple disciplines, such as Smart health care, Smart transportation, and Smart community. Most services in Smart Cities, especially in the Smart healthcare domain, require the real-time sharing, processing, and analyzing of Big Healthcare Data for intelligent decision making. Therefore, a strong wireless and mobile communication infrastructure is necessary to connect and access Smart healthcare services, people, and sensors seamlessly, anywhere at any time. In this scenario, mobile cloud computing (MCC) can play a vital role by offloading Big Healthcare Data related tasks, such as sharing, processing, and analysis, from mobile applications to cloud resources, ensuring quality of service demands of end users. Such resource migration, which is also termed virtual machine (VM) migration, is effective in the Smart healthcare scenario in Smart Cities. In this paper, we propose an ant colony optimization-based joint VM migration model for a heterogeneous, MCC-based Smart Healthcare system in Smart City environment. In this model, the user's mobility and provisioned VM resources in the cloud address the VM migration problem. We also present a thorough performance evaluation to investigate the effectiveness of our proposed model compared with the state-of-the-art approaches.

Abstract:

Auction based participant selection has been widely used for mobile crowd sensing (MCS) to achieve user incentive and assignment optimization. However, mobile crowd sensing problems solved with auction-based approaches usually involve participants' privacy concerns because a participant's bids may contain her private information (such as location visiting patterns), and disclosure participants' bids may disclose their private information as well. In this paper, we study how to protect such bid privacy in a temporally and spatially dynamic MCS system. We assume that both sensing tasks and mobile participants have dynamic characteristics over spatial and temporal domains. Following the classical VCG auction, we carefully design a scalable grouping based privacy-preserving participant selection scheme, which leverages Lagrange polynomial interpolation to perturb participants' bids within groups. The proposed solution does not affect the operation of current MCS platform. Both theoretical analysis and real-life tracing data simulations verify the efficiency and security of the proposed solution.

Abstract:

Location Based Services (LBS) have become extremely popular over the past decade, being used on a daily basis by millions of users. Instances of real-world LBS range from mapping services (e.g., Google Maps) to lifestyle recommendations (e.g., Yelp) to real-estate search (e.g., Redfin). In general, an LBS provides a public (often web-based) search interface over its backend database (of tuples with 2D geolocations), taking as input a 2D query point and returning k tuples in the database that are closest to the query point, where k is usually a small constant such as 20 or 50. Such a public interface is often called a k-Nearest-Neighbor, i.e., kNN, interface. In this paper, we consider a novel problem of enabling density based clustering over the backend database of an LBS using nothing but limited access to the kNN interface provided by the LBS. Specifically, a key limit enforced by most real-world LBS is a maximum number of kNN queries allowed from a user over a given time period. Since such a limit is often orders of magnitude smaller than the number of tuples in the LBS database, our goal here is to mine from the LBS a cluster assignment function f(·), such that for any tuple t in the database (which may or may not have been accessed), f(·) can produce the cluster assignment of t with high accuracy. We conduct a comprehensive set of experiments over benchmark datasets and popular real-world LBS such as Yahoo! Flickr, Zillow, Redfin and Google Maps and demonstrate the effectiveness of our proposed techniques.

Abstract:

Most of existing indoor navigation systems work in a client/server manner, which needs to deploy comprehensive localization services together with precise indoor maps a prior. In this paper, we design and realize a Peer-to-Peer navigation system, named ppNav, on smartphones, which enables the fast-to-deploy navigation services, avoiding the requirements of pre-deployed location services and detailed floorplans. ppNav navigates a user to the destination by tracking user mobility, promoting timely walking tips, and alerting potential deviations, according to a previous traveller’s trace experience. Specifically, we utilize the ubiquitous WiFi fingerprints in a novel diagrammed form and extract both radio and visual features of the diagram to track relative locations and exploit fingerprint similarity trend for deviation detection. We further devise techniques to lock on a user to the nearest reference path in case he/she arrives at an uncharted place. Consolidating these techniques, we implement ppNav on commercial mobile devices and validate its performance in real environments. Our results show that ppNav achieves delightful performance, with an average relative error of 0.9m in trace tracking and a maximum delay of 9 samples (about 4.5s) in deviation detection.

Abstract:

Mobile-edge computation offloading (MECO) off-loads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we study resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First, for the TDMA MECO system with infinite or finite cloud computation capacity, the optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under the constraint on computation latency. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Moreover, for the cloud with finite capacity, a sub-optimal resource-allocation algorithm is proposed to reduce the computation complexity for computing the threshold. Next, we consider the OFDMA MECO system, for which the optimal resource allocation is formulated as a mixed-integer problem. To solve this challenging problem and characterize its policy structure, a low-complexity sub-optimal algorithm is proposed by transforming the OFDMA problem to its TDMA counterpart. The corresponding resource allocation is derived by defining an average offloading priority function and shown to have close-to-optimal performance in simulation.

Abstract:

With the popularity of wearable devices, along with the development of clouds and cloudlet technology, there has been increasing need to provide better medical care. The processing chain of medical data mainly includes data collection, data storage and data sharing, etc. Traditional healthcare system often requires the delivery of medical data to the cloud, which involves users’ sensitive information and causes communication energy consumption. Practically, medical data sharing is a critical and challenging issue. Thus in this paper, we build up a novel healthcare system by utilizing the flexibility of cloudlet. The functions of cloudlet include privacy protection, data sharing and intrusion detection. In the stage of data collection, we first utilize Number Theory Research Unit (NTRU) method to encrypt user’s body data collected by wearable devices. Those data will be transmitted to nearby cloudlet in an energy efficient fashion. Secondly, we present a new trust model to help users to select trustable partners who want to share stored data in the cloudlet. The trust model also helps similar patients to communicate with each other about their diseases. Thirdly, we divide users’ medical data stored in remote cloud of hospital into three parts, and give them proper protection. Finally, in order to protect the healthcare system from malicious attacks, we develop a novel collaborative intrusion detection system (IDS) method based on cloudlet mesh, which can effectively prevent the remote healthcare big data cloud from attacks. Our experiments demonstrate the effectiveness of the proposed scheme.

Abstract:

The increase in data storage and power consumption at data-centers has made it imperative to design energy efficient distributed storage systems (DSS). The energy efficiency of DSS is strongly influenced not only by the volume of data, frequency of data access and redundancy in data storage, but also by the heterogeneity exhibited by the DSS in these dimensions. To this end, we propose and analyze the energy efficiency of a heterogeneous distributed storage system in which n storage servers (disks) store the data of R distinct classes. Data of class i is encoded using a (n,ki) erasure code and the (random) data retrieval requests can also vary across classes. We show that the energy efficiency of such systems is closely related to the average latency and hence motivates us to study the energy efficiency via the lens of average latency. Through this connection, we show that erasure coding serves the dual purpose of reducing latency and increasing energy efficiency. We present a queuing theoretic analysis of the proposed model and establish upper and lower bounds on the average latency for each data class under various scheduling policies. Through extensive simulations, we present qualitative insights which reveal the impact of coding rate, number of servers, service distribution and number of redundant requests on the average latency and energy efficiency of the DSS.