Zizhan Zheng

Steady and fair rate allocation for rechargeable sensors in perpetual sensor networks

Renewable energy enables sensor networks with the capability to recharge and provide perpetual data services. Due to low recharging rates and the dynamics of renewable energy such as solar and wind power, providing services without interruptions caused by battery runouts is non-trivial. Most environment monitoring applications require data collection from all nodes at a steady rate. The objective of this paper is to design a solution for fair and high throughput data extraction from all nodes in presence of renewable energy sources. Specifically, we seek to compute the lexicographically maximum data collection rate for each node, such that no node will ever run out of energy. We propose a centralized algorithm and an asynchronous distributed algorithm that can compute the optimal lexicographic rate assignment for all nodes. The centralized algorithm jointly computes the optimal data collection rate for all nodes along with the flows on each link, while the distributed algorithm computes the optimal rate when the routes are pre-determined. We prove the optimality for both the centralized and the distributed algorithms, and use a testbed with 155 sensor nodes to validate the distributed algorithm.

Perpetual and fair data collection for environmental energy harvesting sensor networks

Renewable energy enables sensor networks with the capability to recharge and provide perpetual data services. Due to low recharging rates and the dynamics of renewable energy such as solar and wind power, providing services without interruptions caused by battery runouts is nontrivial. Most environment monitoring applications require data collection from all nodes at a steady rate. The objective of this paper is to design a solution for fair and high throughput data extraction from all nodes in the presence of renewable energy sources. Specifically, we seek to compute the lexicographically maximum data collection rate and routing paths for each node such that no node will ever run out of energy. We propose a centralized algorithm and two distributed algorithms. The centralized algorithm jointly computes the optimal data collection rate for all nodes along with the flows on each link, the first distributed algorithm computes the optimal rate when the routing structure is a given tree, and the second distributed algorithm, although heuristic, jointly computes a routing structure and a high lexicographic rate assignment that is nearly optimum. We prove the optimality for the centralized and the first distributed algorithm, and use real test-bed experiments and extensive simulations to evaluate both of the distributed algorithms.

Trap Coverage: Allowing Coverage Holes of Bounded Diameter in Wireless Sensor Networks

Tracking of movements such as that of people, animals, vehicles, or of phenomena such as fire, can be achieved by deploying a wireless sensor network. So far only prototype systems have been deployed and hence the issue of scale has not become critical. Real-life deployments, however, will be at large scale and achieving this scale will become prohibitively expensive if we require every point in the region to be covered (i.e., full coverage), as has been the case in prototype deployments. In this paper we therefore propose a new model of coverage, called trap coverage, that scales well with large deployment regions. A sensor network providing trap coverage guarantees that any moving object or phenomena can move at most a (known) displacement before it is guaranteed to be detected by the network, for any trajectory and speed. Applications aside, trap coverage generalizes the de-facto model of full coverage by allowing holes of a given maximum diameter. From a probabilistic analysis perspective, the trap coverage model explains the continuum between percolation (when coverage holes become finite) and full coverage (when coverage holes cease to exist). We take first steps toward establishing a strong foundation for this new model of coverage. We derive reliable, explicit estimates for the density needed to achieve trap coverage with a given diameter when sensors are deployed randomly. Our density estimates are more accurate than those obtained using asymptotic critical conditions. We show by simulation that our analytical predictions of density are quite accurate even for small networks. We then propose polynomial-time algorithms to determine the level of trap coverage achieved once sensors are deployed on the ground. Finally, we point out several new research problems that arise by the introduction of the trap coverage model.

Maximizing the Contact Opportunity for Vehicular Internet Access

With increasing popularity of media enabled hand-helds, the need for high data-rate services for mobile users is evident. Large-scale Wireless LANs (WLANs) can provide such a service, but they are expensive to deploy and maintain. Open WLAN access-points (APs), on the other hand, need no new deployments, but can offer only opportunistic services with no guarantees on short term throughput. In contrast, a carefully planned sparse deployment of roadside WiFi provides an economically scalable infrastructure with quality of service assurance to mobile users. In this paper, we propose to study the deployment techniques with respect to roadside WiFi. In particular, we present a new metric, called Contact Opportunity, as a characterization of a roadside WiFi network. Informally, the contact opportunity for a given deployment measures the fraction of distance or time that a mobile user is in contact with some AP when moving through a certain path. Such a metric is closely related to the quality of data service that a mobile user might experience while driving through the system. We then present an efficient deployment method that maximizes the worst case contact opportunity under a budget constraint. We further show how to extend this concept and the deployment techniques to a more intuitive metric -- the average throughput -- by taking various dynamic elements into account. Simulations over a real road network and experimental results show that our approach achieves more than 200% higher minimum contact opportunity, 30%-100% higher average contact opportunity and a significantly improved distribution of average throughput compared with two commonly used baseline algorithms.

Alpha Coverage: Bounding the Interconnection Gap for Vehicular Internet Access

Vehicular Internet access via open WLAN access points (APs) has been demonstrated to be a feasible solution to provide opportunistic data service to moving vehicles. Using an in situ deployment, however, such a solution does not provide worst-case performance guarantees due to unpredictable intermittent connectivity. On the other hand, a solution that tries to cover every point in an entire road network with APs (full coverage) is not very practical due to the prohibitive deployment and operational cost. In this paper, we introduce a new notion of intermittent coverage for mobile users, called a-coverage, which provides worst-case guarantees on the interconnection gap while using significantly fewer APs than needed for full coverage. We propose efficient algorithms to verify whether a given deployment provides alpha-coverage and approximation algorithms for determining a deployment of APs that will provide alpha-coverage. We compare alpha-coverage with opportunistic access of open WLAN APs (modeled as a random deployment) via simulations over a real-world road network and show that using the same number of APs as random deployment, alpha-coverage bounds the interconnection gap to a much smaller distance than that in a random deployment.

Towards autonomic computing middleware via reflection

Autonomic computing middleware is a promising way to enable middleware based systems to cope with the rapid and continuous changes in the era of Internet. Technically, there have been three fundamental and challenging capabilities to an autonomic computing middleware, including how to monitor, reason and control middleware platform and applications. This position paper presents a reflection-based approach to autonomic computing middleware, which shows the philosophy that autonomic computing should focus on how to reason while reflective computing supports how to monitor and control. In this approach, the states and behaviors of middleware-based systems can be observed and changed through reflective mechanisms embedded in middleware platform at runtime. On the basis of reflection, some autonomic computing facilities could be constructed to reason and decide when and what to change. The approach is demonstrated on a reflective J2EE application server, which can automatically optimize itself in the standard J2EE benchmark testing

Maximizing system throughput by cooperative sensing in cognitive radio networks

Cognitive radio networks (CRNs) allow unlicensed users to opportunistically access the licensed spectrum without causing disruptive interference to the primary users (PUs). One of the main challenges in CRNs is the ability to detect PU transmissions. Recent works have suggested the use of secondary user (SU) cooperation over individual sensing to improve sensing accuracy. In this paper, we consider a CRN consisting of multiple PUs and SUs to study the problem of maximizing the total expected system throughput. First, we study the sensing decision problem for maximizing the system throughput subject to a constraint on the PU throughput, and we design a Bayesian decision rule-based algorithm. The problem is shown to be strongly NP-hard and solved via a greedy algorithm with time complexity O([(N5)/(log2[1/(1-ε)])]), where N is the total number of SUs. The algorithm achieves a throughput strictly greater than 1/2(1-ε) of the optimal solution and results in a small constraint violation that goes to zero with ε. We then investigate the more general problem with constraints on both PU throughput and the sensing time overhead, which limits the number of SUs that can participate in cooperative sensing. We illustrate the efficacy of the performance of our algorithms and provide sensitivity analysis via a numerical investigation.

Sparse WiFi deployment for vehicular internet access with bounded interconnection gap

Vehicular Internet access via open WiFi access points (APs) has been demonstrated to be a feasible solution to provide opportunistic data service to moving vehicles. Using an in situ deployment, however, such a solution does not provide performance guarantees due to unpredictable intermittent connectivity. On the other hand, a solution that tries to cover every point in an entire road network with APs (a full coverage) is not very practical due to prohibitive deployment and operational costs. In this paper, we introduce a new notion of intermittent coverage for mobile users, called Alpha Coverage, which provides worst-case guarantees on the interconnection gap, i.e., the distance or expected delay between two consecutive mobile-AP contacts for a vehicle, while using significantly fewer APs than needed for full coverage. We propose efficient algorithms to verify whether a given deployment provides Alpha Coverage. The problem of finding an economic deployment that provides α -coverage turns out to be NP-hard. We hence provide both approximation algorithms that have provable guarantees on the performance as well as efficient heuristics that perform well in practice. The efficiency of our algorithms is demonstrated via simulations using data from real-world road networks.

A Game Theoretic Model for Defending Against Stealthy Attacks with Limited Resources

Stealthy attacks are a major threat to cyber security. In practice, both attackers and defenders have resource constraints that could limit their capabilities. Hence, to develop robust defense strategies, a promising approach is to utilize game theory to understand the fundamental trade-offs involved. Previous works in this direction, however, mainly focus on the single-node case without considering strict resource constraints. In this paper, a game-theoretic model for protecting a system of multiple nodes against stealthy attacks is proposed. We consider the practical setting where the frequencies of both attack and defense are constrained by limited resources, and an asymmetric feedback structure where the attacker can fully observe the states of nodes while largely hiding its actions from the defender. We characterize the best response strategies for both attacker and defender, and study the Nash Equilibria of the game. We further study a sequential game where the defender first announces its strategy and the attacker then responds accordingly, and design an algorithm that finds a nearly optimal strategy for the defender to commit to.

Stealthy attacks meets insider threats: A three-player game model

Advanced persistent threat (APT) is becoming a major threat to cyber security. As APT attacks are often launched by well funded entities that are persistent and stealthy in achieving their goals, they are highly challenging to combat in a cost-effective way. The situation becomes even worse when a sophisticated attacker is further assisted by an insider with privileged access to the inside information. Although stealthy attacks and insider threats have been considered separately in previous works, the coupling of the two is not well understood. As both types of threats are incentive driven, game theory provides a proper tool to understand the fundamental tradeoffs involved. In this paper, we propose the first three-player attacker-defender-insider game to model the strategic interactions among the three parties. Our game extends the two-player FlipIt game model for stealthy takeover by introducing an insider that can trade information to the attacker for a profit. We characterize the subgame perfect equilibria of the game with the defender as the leader and the attacker and the insider as the followers, under two different information trading processes. We make various observations and discuss approaches for achieving more efficient defense in the face of both APT and insider threats.