Multiphase deployment models for fast self healing in wireless sensor networks

The majority of studies on security in resource limited wireless sensor networks (WSN) focus on finding an efficient balance among energy consumption, computational speed and memory usage. Besides these resources, time is a relatively immature aspect that can be considered in system design and performance evaluations. In a recent study(Castelluccia and Spognardi, 2007), the time dimension is used to lower the ratio of compromised links, thus, improving resiliency in key distribution in WSNs. This is achieved by making the old and possibly compromised keys useful only for a limited amount of time. In this way, the effect of compromised keys diminish in time, so the WSN selfheals. In this study we further manipulate the time dimension and propose a deployment model that speeds up the resilience improvement process with a tradeoff between connectivity and resiliency. In our method, self healing speeds up by introducing nodes that belong to future generations in the time scale. In this way, the duration that the adversary can make use of compromised keys become smaller

A resilient key predistribution scheme for multiphase wireless sensor networks

In wireless sensor networks, sensor nodes eventually die due to battery depletion. Wireless sensor networks (WSNs) in which new nodes are periodically redeployed with certain intervals, called generations, to replace the dead nodes are called multi-phase wireless sensor networks. In the literature, there are several key predistribution schemes proposed for secure operation of WSNs. However, these schemes are designed for single phase networks which are not resilient against continuous node capture attacks; even under temporary attacks on the network, the harm caused by the attacker does not heal in time. However, the periodic deployments in multi-phase sensor networks could be utilized to improve the resiliency of the WSNs by deploying nodes with fresh keys. In the literature, there is limited work done in this area. In this paper, we propose a key predistribution scheme for multi-phase wireless sensor networks which is highly resilient under node capture attacks. In our scheme, called RGM (random generation material) key predistribution scheme, each generation of deployment has its own random keying material and pairwise keys are established between node pairs of particular generations. These keys are specific to these generations. Therefore, a captured node cannot be abused to obtain keys of other generations. We compare the performance of our RGM scheme with a well-known multi-phase key predistribution scheme and showed that RGM achieves up to three-fold more resiliency. Even under heavy attacks, our scheme's resiliency performance is 50% better in steady state

On Energy Efficient Hierarchical Cross-Layer Design: Joint Power Control and Routing for Ad Hoc Networks

In this paper, a hierarchical cross-layer design approach is proposed to increase energy efficiency in ad hoc networks through joint adaptation of nodes' transmitting powers and route selection. The design maintains the advantages of the classic OSI model, while accounting for the cross-coupling between layers, through information sharing. The proposed joint power control and routing algorithm is shown to increase significantly the overall energy efficiency of the network, at the expense of a moderate increase in complexity. Performance enhancement of the joint design using multiuser detection is also investigated, and it is shown that the use of multiuser detection can increase the capacity of the ad hoc network significantly for a given level of energy consumption.Comment: To appear in the EURASIP Journal on Wireless Communications and Networking, Special Issue on Wireless Mobile Ad Hoc Network

Optimal Resource Allocation in Random Networks with Transportation Bandwidths

We apply statistical physics to study the task of resource allocation in random sparse networks with limited bandwidths for the transportation of resources along the links. Useful algorithms are obtained from recursive relations. Bottlenecks emerge when the bandwidths are small, causing an increase in the fraction of idle links. For a given total bandwidth per node, the efficiency of allocation increases with the network connectivity. In the high connectivity limit, we find a phase transition at a critical bandwidth, above which clusters of balanced nodes appear, characterised by a profile of homogenized resource allocation similar to the Maxwell's construction.Comment: 28 pages, 11 figure

Local heuristic for the refinement of multi-path routing in wireless mesh networks

We consider wireless mesh networks and the problem of routing end-to-end traffic over multiple paths for the same origin-destination pair with minimal interference. We introduce a heuristic for path determination with two distinguishing characteristics. First, it works by refining an extant set of paths, determined previously by a single- or multi-path routing algorithm. Second, it is totally local, in the sense that it can be run by each of the origins on information that is available no farther than the node's immediate neighborhood. We have conducted extensive computational experiments with the new heuristic, using AODV and OLSR, as well as their multi-path variants, as underlying routing methods. For two different CSMA settings (as implemented by 802.11) and one TDMA setting running a path-oriented link scheduling algorithm, we have demonstrated that the new heuristic is capable of improving the average throughput network-wide. When working from the paths generated by the multi-path routing algorithms, the heuristic is also capable to provide a more evenly distributed traffic pattern

Non-Orthogonal Multiple Access for mmWave Drones with Multi-Antenna Transmission

Unmanned aerial vehicles (UAVs) can be deployed as aerial base stations (BSs) for rapid establishment of communication networks during temporary events and after disasters. Since UAV-BSs are low power nodes, achieving high spectral and energy efficiency are of paramount importance. In this paper, we introduce non-orthogonal multiple access (NOMA) transmission for millimeter-wave (mmWave) drones serving as flying BSs at a large stadium potentially with several hundreds or thousands of mobile users. In particular, we make use of multi-antenna techniques specifically taking into consideration the physical constraints of the antenna array, to generate directional beams. Multiple users are then served within the same beam employing NOMA transmission. If the UAV beam can not cover entire region where users are distributed, we introduce beam scanning to maximize outage sum rates. The simulation results reveal that, with NOMA transmission the spectral efficiency of the UAV based communication can be greatly enhanced compared to orthogonal multiple access (OMA) transmission. Further, the analysis shows that there is an optimum transmit power value for NOMA beyond which outage sum rates do not improve further

Learning in stochastic neural networks for constraint satisfaction problems

Researchers describe a newly-developed artificial neural network algorithm for solving constraint satisfaction problems (CSPs) which includes a learning component that can significantly improve the performance of the network from run to run. The network, referred to as the Guarded Discrete Stochastic (GDS) network, is based on the discrete Hopfield network but differs from it primarily in that auxiliary networks (guards) are asymmetrically coupled to the main network to enforce certain types of constraints. Although the presence of asymmetric connections implies that the network may not converge, it was found that, for certain classes of problems, the network often quickly converges to find satisfactory solutions when they exist. The network can run efficiently on serial machines and can find solutions to very large problems (e.g., N-queens for N as large as 1024). One advantage of the network architecture is that network connection strengths need not be instantiated when the network is established: they are needed only when a participating neural element transitions from off to on. They have exploited this feature to devise a learning algorithm, based on consistency techniques for discrete CSPs, that updates the network biases and connection strengths and thus improves the network performance

Not All Wireless Sensor Networks Are Created Equal: A Comparative Study On Tunnels

Wireless sensor networks (WSNs) are envisioned for a number of application scenarios. Nevertheless, the few in-the-field experiences typically focus on the features of a specific system, and rarely report about the characteristics of the target environment, especially w.r.t. the behavior and performance of low-power wireless communication. The TRITon project, funded by our local administration, aims to improve safety and reduce maintenance costs of road tunnels, using a WSN-based control infrastructure. The access to real tunnels within TRITon gives us the opportunity to experimentally assess the peculiarities of this environment, hitherto not investigated in the WSN field. We report about three deployments: i) an operational road tunnel, enabling us to assess the impact of vehicular traffic; ii) a non-operational tunnel, providing insights into analogous scenarios (e.g., underground mines) without vehicles; iii) a vineyard, serving as a baseline representative of the existing literature. Our setup, replicated in each deployment, uses mainstream WSN hardware, and popular MAC and routing protocols. We analyze and compare the deployments w.r.t. reliability, stability, and asymmetry of links, the accuracy of link quality estimators, and the impact of these aspects on MAC and routing layers. Our analysis shows that a number of criteria commonly used in the design of WSN protocols do not hold in tunnels. Therefore, our results are useful for designing networking solutions operating efficiently in similar environments

Distributed drone base station positioning for emergency cellular networks using reinforcement learning

Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network

Fast network configuration in Software Defined Networking

Software Defined Networking (SDN) provides a framework to dynamically adjust and re-program the data plane with the use of flow rules. The realization of highly adaptive SDNs with the ability to respond to changing demands or recover after a network failure in a short period of time, hinges on efficient updates of flow rules. We model the time to deploy a set of flow rules by the update time at the bottleneck switch, and formulate the problem of selecting paths to minimize the deployment time under feasibility constraints as a mixed integer linear program (MILP). To reduce the computation time of determining flow rules, we propose efficient heuristics designed to approximate the minimum-deployment-time solution by relaxing the MILP or selecting the paths sequentially. Through extensive simulations we show that our algorithms outperform current, shortest path based solutions by reducing the total network configuration time up to 55% while having similar packet loss, in the considered scenarios. We also demonstrate that in a networked environment with a certain fraction of failed links, our algorithms are able to reduce the average time to reestablish disrupted flows by 40%