Hosam Rowaihy

Limiting Sybil Attacks in Structured P2P Networks

One practical limitation of structured peer-to-peer (P2P) networks is that they are frequently subject to Sybil attacks: malicious parties can compromise the network by generating and controlling large numbers of shadow identities. In this paper, we propose an admission control system that mitigates Sybil attacks by adaptively constructing a hierarchy of cooperative peers. The admission control system vets joining nodes via client puzzles. A node wishing to join the network is serially challenged by the nodes from a leaf to the root of the hierarchy. Nodes completing the puzzles of all nodes in the chain are provided a cryptographic proof of the vetted identity. We evaluate our solution and show that an adversary must perform days or weeks of effort to obtain even a small percentage of nodes in small P2P networks, and that this effort increases linearly with the size of the network. We further show that we can place a ceiling on the number of IDs any adversary may obtain by requiring periodic reassertion of the IDs continued validity.

Mitigating Performance Degradation in Congested Sensor Networks

Data generated in wireless sensor networks may not all be alike: some data may be more important than others and hence may have different delivery requirements. In this paper, we address differentiated data delivery in the presence of congestion in wireless sensor networks. We propose a class of algorithms that enforce differentiated routing based on the congested areas of a network and data priority. The basic protocol, called congestion-aware routing (CAR), discovers the congested zone of the network that exists between high-priority data sources and the data sink and, using simple forwarding rules, dedicates this portion of the network to forwarding primarily high-priority traffic. Since CAR requires some overhead for establishing the high-priority routing zone, it is unsuitable for highly mobile data sources. To accommodate these, we define MAC-enhanced CAR (MCAR), which includes MAC-layer enhancements and a protocol for forming high-priority paths on the fly for each burst of data. MCAR effectively handles the mobility of high-priority data sources, at the expense of degrading the performance of low-priority traffic. We present extensive simulation results for CAR and MCAR, and an implementation of MCAR on a 48-node testbed.

Frugal Sensor Assignment

When a sensor network is deployed in the field it is typically required to support multiple simultaneous missions, which may start and finish at different times. Schemes that match sensor resources to mission demands thus become necessary. In this paper, we consider new sensor-assignment problems motivated by frugality, i.e., the conservation of resources, for both static and dynamic settings. In general, the problems we study are NP-hard even to approximate, and so we focus on heuristic algorithms that perform well in practice. In the static setting, we propose a greedy centralized solution and a more sophisticated solution that uses the Generalized Assignment Problem model and can be implemented in a distributed fashion. In the dynamic setting, we give heuristic algorithms in which available sensors propose to nearby missions as they arrive. We find that the overall performance can be significantly improved if available sensors sometimes refuse to offer utility to missions they could help based on the value of the mission, the sensors remaining energy, and (if known) the remaining target lifetime of the network. Finally, we evaluate our solutions through simulations.

Assigning sensors to missions with demands

We introduce Semi-Matching with Demands (SMD), which models a certain problem in sensor networks of assigning individual sensors to sensing tasks. If there are multiple sensing tasks or missions to be accomplished simultaneously, and if sensor assignment must be exclusive, then this is a bipartite semi-matching problem. Each mission is associated with a demand value and a profit value; each sensormission pair is associated with a utility offer (possibly 0). The goal is a sensor assignment that maximizes the profits of the satisfied missions (with no credit for partially satisfied missions). SMD is NP-hard and as hard to approximate as MAXIMUM INDEPENDENT SET. Therefore we investigate less difficult constrained versions of the problem. We give a simple greedy Δ-approximation algorithm for a degree-constrained version (Δ-SMD), in which each mission receives positive utility offers from at most Δ sensors. For small Δ, we show that Δ-SMD is equivalent to k-SET PACKING (with k = Δ), which yields a polynomial-time (Δ+1)/2- approximation. For Δ = 2, we solve the problem optimally by reduction to maximum matching. Finally, we introduce a geometric version which remains strongly NP-hard but has a PTAS.

Reasoning and resource allocation for sensor-mission assignment in a coalition context

We consider the problem of sensor-mission assignment as that of allocating a collection of intelligence, surveillance and reconnaissance (ISR) assets (including sensors and sensor platforms) to a set of mission tasks in an attempt to satisfy the ISR requirements of those tasks. This problem is exacerbated in a coalition context because the full range of possible ISR solutions is not easy to obtain at-a-glance. Moreover, the operational environment is highly dynamic, with frequent changes in ISR requirements and availability of assets. In this paper we describe a solution for the sensor-mission assignment problem that aims to maximize agility in sensor-mission assignment, while preserving robustness. The search space of potential solutions is reduced by employing a semantic reasoner to work out the types of sensor and platform bundles suitable for a given set of ISR tasks. Then, an efficient resource allocation algorithm is used to assign bundles of sensor/platform instances to satisfy each task, within the search space determined by the reasoner. The availability of instances takes into account access rights on those instances across the coalitionpsilas inventory. We describe a proof-of-concept implementation of this approach, in the form of a decision support tool for ISR planning.We illustrate the approach in the context of a coalition peace support operation scenario.

Simulation of dynamic traffic control system based on wireless sensor network

The use of wireless sensor network in the smart traffic control systems is very beneficial and starting to be very promising in the design and implementation for such systems. It will help in saving people time and adapt the intersections traffic lights to the traffic loads from each direction. In this paper we present an intelligent traffic signals control system based on a wireless sensor network (WSN). It uses the vehicle queue length during red cycle to perform better control in the next green cycle. The main objective is to minimize the average waiting time that will reduce the queues length and do better traffic management based on the arrivals in each direction. The system also includes an approach to alert the people about the red light crossing to minimize the possibility of accidents due to red light crossing violations. The system was simulated and results are shown in the end of this paper.

Sensor-mission assignment in wireless sensor networks

When a sensor network is deployed, it is typically required to support multiple simultaneous missions. Schemes that assign sensing resources to missions thus become necessary. In this article, we formally define the sensor-mission assignment problem and discuss some of its variants. In its most general form, this problem is NP-hard. We propose algorithms for the different variants, some of which include approximation guarantees. We also propose distributed algorithms to assign sensors to missions which we adapt to include energy-awareness to extend network lifetime. Finally, we show comprehensive simulation results comparing these solutions to an upper bound on the optimal solution.

Sensor-Mission Assignment in Constrained Environments

When a sensor network is deployed in the field it is typically required to support multiple simultaneous missions, which may start and finish at different times. Schemes that match sensor resources to mission demands thus become necessary. In this paper, we consider new sensor-assignment problems motivated by frugality, i.e., the conservation of resources, for both static and dynamic settings. In the most general setting, the problems we study are NP-hard even to approximate, and so we focus on heuristic algorithms that perform well in practice. In the static setting, we propose a greedy centralized solution and a more sophisticated solution that uses the Generalized Assignment Problem model and can be implemented in a distributed fashion. In what we call the dynamic setting, missions arrive over time and have different durations. For this setting, we give heuristic algorithms in which available sensors propose to nearby missions as they arrive. We find that the overall performance can be significantly improved if available sensors sometimes refuse to offer utility to missions they could help, making this decision based on the value of the mission, the sensors remaining energy, and (if known) the remaining target lifetime of the network. Finally, we evaluate our solutions through simulations.

Assigning Sensors to Competing Missions

When a sensor network is deployed in the field, it is typically required to support multiple simultaneous missions, which may start and finish at different times. Schemes that match sensor resources to mission demands thus become necessary. In this paper, we propose centralized and distributed schemes to assign sensors to missions. We also adapt our distributed scheme to make it energy-aware to extend network lifetime. Finally, we show simulation results comparing these solutions. We find that our greedy algorithm frequently performs near-optimally and that the distributed schemes usually perform nearly as well.

Detection and Localization Sensor Assignment with Exact and Fuzzy Locations

Sensor networks introduce new resource allocation problems in which sensors need to be assigned to the tasks they best help. Such problems have been previously studied in simplified models in which utility from multiple sensors is assumed to combine additively. In this paper we study more complex utility models, focusing on two particular applications: event detection and target localization. We develop distributed algorithms to assign directional sensors of different types to multiple simultaneous tasks using exact location information. We extend our algorithms by introducing the concept of fuzzy location which may be desirable to reduce computational overhead and/or to preserve location privacy. We show that our schemes perform well using both exact or fuzzy location information.