Link error and malicious packet dropping are two sources for packet losses in multi-hop wireless ad hoc network. In this paper, while observing a sequence of packet losses in the network, we are interested in determining whether the losses are caused by link errors only, or by the combined effect of link errors and malicious drop. We are especially interested in the insider-attack case, whereby malicious nodes that are part of the route exploit their knowledge of the communication context to selectively drop a small amount of packets critical to the network performance. Because the packet dropping rate in this case is comparable to the channel error rate, conventional algorithms that are based on detecting the packet loss rate cannot achieve satisfactory detection accuracy. To improve the detection accuracy, we propose to exploit the correlations between lost packets. Furthermore, to ensure truthful calculation of these correlations, we develop a homomorphic linear authenticator (HLA) based public auditing architecture that allows the detector to verify the truthfulness of the packet loss information reported by nodes. This construction is privacy preserving, collusion proof, and incurs low communication and storage overheads. To reduce the computation overhead of the baseline scheme, a packet-block-based mechanism is also proposed, which allows one to trade detection accuracy for lower computation complexity. best ieee project center chennai best ieee project center chennai
We consider a delay tolerant network under two message forwarding schemes-a non-replicative direct delivery scheme and a replicative epidemic routing scheme. Our objective is to track the degree of spread of a message in the network. Such estimation can be used for on-line control of message dissemination. With a homogeneous mobility model with pairwise i.i.d. exponential inter-meeting times, we rigorously derive the system dynamic and measurement equations for optimal tracking by a Kalman filter. Moreover, we provide a framework for tracking a large class of processes that can be modeled as density-dependent Markov chains. We also apply the same filter with a heterogeneous mobility, where the aggregate inter-meeting times exhibit a power law with exponential tail as in real-world mobility traces, and show that the performance of the filter is comparable to that with homogeneous mobility.
Organizations are starting to realize the significant value of advertising on mobile devices, and a number of systems have been developed to exploit this opportunity. From a privacy perspective, practically all systems developed so far are based either on a trusted third-party model or on a generalized architecture. We propose a system for delivering context, location, time, and preference-aware advertisements to mobiles with a novel architecture to preserve privacy. The main adversary in our model is the server distributing the ads, which is trying to identify users and track them, and to a lesser extent, other peers in the wireless network. When a node is interested in an ad, it forms a group of nearby nodes seeking ads and willing to cooperate to achieve privacy. Peers combine their interests using a shuffling mechanism in an ad-hoc network and send them through a primary peer to the ad-server. In this way, preferences are masqueraded to request custom ads, which are then distributed by the primary peer.
Applications running on the same Wireless Sensor Network (WSN) platform usually have different Quality of Service (QoS) requirements. Two basic requirements are low delay and high data integrity. However, in most situations, these two requirements cannot be satisfied simultaneously. In this paper, based on the concept of potential in physics, we propose IDDR, a multi-path dynamic routing algorithm, to resolve this conflict. By constructing a virtual hybrid potential field, IDDR separates packets of applications with different QoS requirements according to the weight assigned to each packet, and routes them towards the sink through different paths to improve the data fidelity for integrity-sensitive applications as well as reduce the end-to-end delay for delay-sensitive ones. Using the Lyapunov drift technique, we prove that IDDR is stable. Simulation results demonstrate that IDDR provides data integrity and delay differentiated services.
A location-aware news feed (LANF) system generates news feeds for a mobile user based on her spatial preference (i.e., her current location and future locations) and non-spatial preference (i.e., her interest). Existing LANF systems simply send the most relevant geo-tagged messages to their users. Unfortunately, the major limitation of such an existing approach is that, a news feed may contain messages related to the same location (i.e., point-of-interest) or the same category of locations (e.g., food, entertainment or sport). We argue that diversity is a very important feature for location-aware news feeds because it helps users discover new places and activities. In this paper, we propose D-MobiFeed; a new LANF system enables a user to specify the minimum number of message categories (h) for the messages in a news feed. In D-MobiFeed, our objective is to efficiently schedule news feeds for a mobile user at her current and predicted locations, such that (i) each news feed contains messages belonging to at least h different categories, and (ii) their total relevance to the user is maximized. To achieve this objective, we formulate the problem into two parts, namely, a decision problem and an optimization problem. For the decision problem, we provide an exact solution by modeling it as a maximum flow problem and proving its correctness. The optimization problem is solved by our proposed three-stage heuristic algorithm.
Mobile sensing relies on data contributed by users through their mobile device (e.g., smart phone) to obtain useful information about people and their surroundings. However, users may not want to contribute due to lack of incentives and concerns on possible privacy leakage. To effectively promote user participation, both incentive and privacy issues should be addressed. Although incentive and privacy have been addressed separately in mobile sensing, it is still an open problem to address them simultaneously. In this paper, we propose two credit-based privacy-aware incentive schemes for mobile sensing systems, where the focus is on privacy protection instead of on the design of incentive mechanisms. Our schemes enable mobile users to earn credits by contributing data without leaking which data they have contributed, and ensure that malicious users cannot abuse the system to earn unlimited credits. Specifically, the first scheme considers scenarios where an online trusted third party (TTP) is available, and relies on the TTP to protect user privacy and prevent abuse attacks. The second scheme considers scenarios where no online TTP is available. It applies blind signature, partially blind signature, and a novel extended Merkle tree technique to protect user privacy and prevent abuse attacks. Security analysis and cost evaluations show that our schemes are secure and efficient.
In some specific applicable scenarios, nodes are placed in some geographical areas and limited to move in their own community. We investigate the tradeoff between the delivery delay and the number of transmissions in the above multi–community delay tolerant networks by propagating a data packet in a carefully chosen segment of community. First, three restricted epidemic routings are proposed: the shortest community-hop path scheme, the rectangle scheme, and the parallelogram scheme. Second, the ratios of the average number of communities that can propagate the data packet in the proposed schemes to those that can propagate in the epidemic routing are analyzed. The ratios are found to be small and to decrease with the increase in the number of communities. The tail distribution of the inter-meeting time of any two nodes in the neighboring communities is then demonstrated to be exponential. Third, the delivery delay of the proposed schemes is analyzed by Markovian chain tool. The experiments show that the theoretical model proposed here is reliable, and that the proposed schemes can significantly decrease the number of transmissions, even if these schemes increase the deliverydelay to some extent.
In wireless networks, getting the global knowledge of channel state information (CSI, e.g., channel gain or link loss probability) is always beneficial for the nodes to optimize the network design. However, the node usually only has the local CSI between itself and other nodes, and lacks the CSI between any pair of other nodes. To enable all the nodes to get the global CSI, in this paper, we propose a network–codedthird–party information exchange scheme, with an emphasis on minimizing the total transmission costfor ( ) exchanging the CSI among the nodes. We show that for a network of N nodes, if and only if any k nodes (1 ≤ k <; N) send at least (2 : k) packets, a feasible solution exists for third–party informationexchange. Formulating the problem of feasible and optimal solutions as an integer linear programming (ILP) problem, we compute the optimal number of packets that must be transmitted by every node. Guided by the necessary and sufficient condition, we construct two practical transmission schemes: fair load (FL) scheme and proportional load (PL) scheme. A deterministic encoding strategy based on XORs coding over GF(2) is further designed to guarantee that with FL or PL scheme, each node finally can decode the complete packets. It is shown that in two specific networks, these two schemes are optimal, achieving the minimum transmission cost. In more general networks, simulation results show that PL is still close to optimal with a high probability. Finally, a distributed transmission protocol is developed, which allows FL and PL schemes to be operated in a distributed and hence scalable manner.
With the increase in flexibility and capabilities of wireless networks, the use of distributed computing over wireless network environments is being researched in order to maximize network sustainability and interoperability among distributed nodes. To this end, a new paradigm is required for optimization of a more generalized environment. This environment would include various nodes of different processing and communication abilities constrained by circuit powers and residual energy over individual dynamic wireless channels. In this paper, we present a novel strategy named link capacity–energy awarewireless distributed computing (LEA-WDC) for maximizing the lifetime of a wireless network. The major advantage of LEA-WDC is its achievement of lifetime maximization by systematically reconciling highly coupled system parameters (tasks, processing power, communication power, and residual energy) in terms of the role of nodes and the layer of each node. To attain an optimal solution, we perform unique interworking optimization via decomposition in accordance with the roles of header and slave nodes. The evaluation results of our simulation verify that the lifetime is further maximized by finding the optimal transmission power of each node according to the Shannon capacity.
Cellular networks are faced with serious congestions nowadays due to the recent booming growth and popularity of wireless devices and applications. Opportunistically accessing the unused licensed spectrum, cognitive radio can potentially harvest more spectrum resources and enhance the capacity of cellular networks. In this paper, we propose a new multihop cognitive cellular network (MC2N) architecture to facilitate the ever exploding data transmissions in cellular networks. Under the proposed architecture, we then investigate the minimum energy consumption problem by exploring joint frequency allocation, link scheduling, routing, and transmission power control. Specifically, we first formulate a maximum independent set (MIS) based energy consumption optimization problem, which is a non-linear programming problem. Different from most previous work assuming all the MISs are known, finding which is in fact NP-complete, we employ a column generation based approach to circumvent this problem. We develop an ϵ-bounded algorithm, which can obtain a feasible solution that are less than (1 + ϵ) and larger than (1 – ϵ) of the optimal result of MP, and analyzed its computational complexity. We also revisit the minimum energy consumption problem by taking uncertain channel bandwidth into consideration. Simulation results show that we can efficiently find ϵ-bounded approximate results and the optimal result as well.
It is understood from past decade of research that a wireless multi-hop network can achieve maximumnetwork throughput only when its nodes operate at a minimum common transmission power level that ensures network connectivity (availability). This point of optimality where maximum availability and throughput is guaranteed in an interference-optimal network has been the basis of numerous design problems in wireless networks. In this paper, we claim that when performability (availability weighted performance) is considered as opposed to average case throughput performance, there does not exist a transmission power (or node density) that can maximize both availability and performability. Since the current mesh networks are expected to deliver carrier-grade services to its users, the availability–performability tradeoff presented in this paper holds a special importance. While availability metric is a necessary one for any networking system intended to provide continuous service, past research has shown a strong correlation between performability and quality of user experience in case of wirelessnetworks. The contributions of the paper are as follows: (1) We first define availability and performabilityin the context of wireless mesh networks, and then develop efficient algorithms on the basis of intelligent state sampling that can calculate both the quantities with reasonable accuracy. (2) We apply the evaluation methods to two existing mesh networks (GoogleWiFi and PoncaCityMesh) to demonstrate that their current design can not guarantee a reasonable level of availability orperformability. (3) Using hundreds of hours of simulations, we analyze the impact of two basic deployment factors (node density and transmission power) on availability and performability. We outline numerous novel results that emerge due to joint availability–performability analysis including the observation about availability–performability tradeoff.
Wearable sensory devices are becoming the enabling technology for many applications in healthcare and well-being, where computational elements are tightly coupled with the human body to monitor specific events about their subjects. Classification algorithms are the most commonly used machine learning modules that detect events of interest in these systems. The use of accurate and resource-efficient classification algorithms is of key importance because wearable nodes operate on limited resources on one hand and intend to recognize critical events (e.g., falls) on the other hand. These algorithms are used to map statistical features extracted from physiological signals onto different states such as health status of a patient or type of activity performed by a subject. Conventionally selectedfeatures may lead to rapid battery depletion, mainly due to the absence of computing complexity criterion while selecting prominent features. In this paper, we introduce the notion of power–awarefeature selection, which aims at minimizing energy consumption of the data processing for classification applications such as action recognition. Our approach takes into consideration the energy cost of individual features that are calculated in real-time. A graph model is introduced to represent correlation and computing complexity of the features. The problem is formulated using integer programming and a greedy approximation is presented to select the features in a power-efficient manner. Experimental results on thirty channels of activity data collected from real subjects demonstrate that our approach can significantly reduce energy consumption of the computing module, resulting in more than 30 percent energy savings while achieving 96.7 percent classification accuracy.
Network coding has been shown to be an effective approach to improve the wireless systemperformance. However, many security issues impede its wide deployment in practice. Besides the well-studied pollution attacks, there is another severe threat, that of wormhole attacks, which undermines the performance gain of network coding. Since the underlying characteristics of network codingsystems are distinctly different from traditional wireless networks, the impact of wormhole attacks and countermeasures are generally unknown. In this paper, we quantify wormholes‘ devastating harmful impact on network coding system performance through experiments. We first propose a centralizedalgorithm to detect wormholes and show its correctness rigorously. For the distributed wirelessnetwork, we propose DAWN, a Distributed detection Algorithm against Wormhole in wireless Networkcoding systems, by exploring the change of the flow directions of the innovative packets caused bywormholes. We rigorously prove that DAWN guarantees a good lower bound of successful detectionrate. We perform analysis on the resistance of DAWN against collusion attacks. We find that the robustness depends on the node density in the network, and prove a necessary condition to achieve collusion-resistance. DAWN does not rely on any location information, global synchronization assumptions or special hardware/middleware. It is only based on the local information that can be obtained from regular network coding protocols, and thus the overhead of our algorithms is tolerable. Extensive experimental results have verified the effectiveness and the efficiency of DAWN.
Existing social networking services recommend friends to users based on their social graphs, which may not be the most appropriate to reflect a user’s preferences on friend selection in real life. In this paper, we present Friendbook, a novel semantic–based friend recommendation system for socialnetworks, which recommends friends to users based on their life styles instead of social graphs. By taking advantage of sensor-rich smartphones, Friendbook discovers life styles of users from user-centric sensor data, measures the similarity of life styles between users, and recommends friends to users if their life styles have high similarity. Inspired by text mining, we model a user’s daily life as life documents, from which his/her life styles are extracted by using the Latent Dirichlet Allocation algorithm. We further propose a similarity metric to measure the similarity of life styles between users, and calculate users’ impact in terms of life styles with a friend-matching graph. Upon receiving a request, Friendbook returns a list of people with highest recommendation scores to the query user. Finally, Friendbook integrates a feedback mechanism to further improve the recommendation accuracy. We have implemented Friendbook on the Android-based smartphones, and evaluated its performance on both small-scale experiments and large-scale simulations. The results show that therecommendations accurately reflect the preferences of users in choosing friends.
Location–based services (LBS) require users to continuously report their location to a potentially untrusted server to obtain services based on their location, which can expose them to privacy risks. Unfortunately, existing privacy-preserving techniques for LBS have several limitations, such as requiring a fully-trusted third party, offering limited privacy guarantees and incurring high communication overhead. In this paper, we propose a user–defined privacy grid system called dynamic grid system(DGS); the first holistic system that fulfills four essential requirements for privacy-preserving snapshot and continuous LBS. (1) The system only requires a semi-trusted third party, responsible for carrying out simple matching operations correctly. This semi-trusted third party does not have any information about a user‘s location. (2) Secure snapshot and continuous location privacy is guaranteed under ourdefined adversary models. (3) The communication cost for the user does not depend on the user‘s desired privacy level, it only depends on the number of relevant points of interest in the vicinity of theuser. (4) Although we only focus on range and k-nearest-neighbor queries in this work, our system can be easily extended to support other spatial queries without changing the algorithms run by the semi-trusted third party and the database server, provided the required search area of a spatial query can be abstracted into spatial regions. Experimental results show that our DGS is more efficient than the state-of-the-art privacy-preserving technique for continuous LBS.