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A Mixed Transmission Strategy to Achieve Energy Balancing in Wireless Sensor Networks

In this paper, we investigate the problem of energy balanced data collection in wireless sensor networks, aiming to balance energy consumption among all sensor nodes during the data propagation process. Energy balanced data collection can potentially save energy consumption and prolong network lifetime, and hence, it has many practical implications for sensor network design and deployment. The traditional hop-by-hop transmission model allows a sensor node to propagate its packets in a hop-by-hop manner toward the sink, resulting in poor energy balancing for the entire network. To address the problem, we apply a slice-based energy model, and divide the problem into inter-slice and intra-slice energy balancing problems. We then propose a probability-based strategy named inter-slice mixed transmission protocol and an intra-slice forwarding technique to address each of the problems. We propose an energy-balanced transmission protocol by combining both techniques to achieve total energy balancing. In addition, we study the condition of switching between inter-slice transmission and intra-slice transmission, and the limitation of hops in an intra-slice transmission. Through our extensive simulation studies, we demonstrate that the proposed protocols achieve energy balancing, prolong network lifespan, and decrease network delay, compared with the hop-by-hop transmission and a cluster-based routing protocol under various parameter settings.

Exploiting Adversarial Jamming Signals for Energy Harvesting in Interference Networks

Anti-jamming interference alignment (IA) is an effective method for battling adversarial jammers for IA networks. Nevertheless, the number of antennas may not be enough to make it feasible in anti-jamming IA. Besides, the abundant power from the jammers and interferences, which used to be deemed as a harmful factor, can be exploited for energy harvesting (EH) by the legitimate users as a power supply. Thus, in this paper, we propose an anti-jamming opportunistic IA (OIA) scheme with wireless EH, which optimizes the transmission rate and EH together. In the proposed scheme, to make the anti-jamming IA network feasible, we select some of the users to transmit information at each time slot, and EH is performed by the other unselected users. Furthermore, to improve the performance of the proposed scheme, EH is also performed by the selected users, and the transmit power and power partition coefficient are jointly optimized to minimize the total transmit power of the OIA network. To reduce the computational complexity of the joint optimization, a suboptimal algorithm is also developed with much lower complexity. Extensive simulation results are presented to show the effectiveness of the proposed anti-jamming OIA scheme with wireless EH.

Improving Safety on Highways by Customizing Vehicular Ad Hoc Networks

This paper studies the need for individualizing vehicular communications in order to improve safety for a highway scenario. Adapting a vehicular ad hoc network to both its individual driver’s characteristic and traffic conditions enables it to transmit in a smart manner to other vehicles. This radical improvement is now possible due to the progress that is being made in vehicular ad hoc networks (VANET). In this paper, we first derive the packet success probability for a chain of vehicles by taking multi-user interference, path loss, and fading into account. Then, by considering the delay constraints and types of potential collisions, we approximate the optimal channel access probabilities. Lastly, we propose an algorithm for customizing channel access probabilities in VANET. Our Monte Carlo simulation results show that this approach achieves more than 25% reduction in traffic collision probability compared with the case with equal channel access probabilities in its optimal range. Therefore, it has a huge advantage over other non-optimal systems.

On Jamming Against Wireless Networks

In this paper, we study jamming attacks against wireless networks. Specifically, we consider a network of base stations (BSs) or access points (APs) and investigate the impact of a fixed number of jammers that are randomly deployed according to a Binomial point process. We investigate the network performance in terms of: 1) the outage probability and 2) the error probability of a victim receiver in the downlink of this wireless network. We derive analytical expressions for both these metrics and discuss in detail how the jammer network must adapt to the various wireless network parameters in order to effectively attack the victim receivers. For instance, we will show that with only 1 jammer per BS/AP: 1) the outage probability of the wireless network can be increased from 1% (as seen in the non-jamming case) to 80% and 2) when retransmissions are used, the jammers cause the effective network activity factor (and hence the interference among the BSs) to be doubled. Furthermore, we show that the behavior of the jammer network as a function of the BS/AP density is not obvious. In particular, a non-trivial behavior is seen, which indicates that the number of jammers required to attack the wireless network must scale with the BS density only until a certain value beyond which it decreases. In the context of error probability of the victim receiver, we study whether or not some recent results related to jamming in the point-to-point link scenario can be extended to the case of jamming against wireless networks. Numerical results are presented to validate all the theoretical inferences presented.

Optimal Relay Selection for Secure Cooperative Communications With an Adaptive Eavesdropper

Optimal relay selection is investigated for secure cooperative communications against an adaptive eavesdropper that can perform eavesdropping if the eavesdropping link has good channel quality or perform jamming otherwise. A number of decode-and-forward relays are available for legitimate communications, among which one relay can be selected to help. For legitimate communications, three cases for availability of the eavesdropping channel information are considered: full channel knowledge, partial channel knowledge, and statistical channel knowledge. An optimal relay selection scheme is proposed for each case. For the first and third cases, exact secrecy outage probability expressions in closed form are derived, and for the second case, an approximate secrecy outage probability is derived, which is tight in the high main-to-eavesdropper ratio regime. Moreover, secrecy diversity order for the proposed relay selection scheme in each case is also derived, which is shown to be a full secrecy diversity. Finally, numerical results are given to verify the theoretical analysis derived in this paper.

Robust Content Delivery and Uncertainty Tracking in Predictive Wireless Networks

Predictive resource allocations (PRAs) have recently gained attention in wireless network literature due to their significant energy-savings and quality of service (QoS) gains. This enhanced performance was primarily demonstrated while assuming the perfect prediction of both mobility traces and anticipated channel rates. While the results are very promising, several technical challenges need to be overcome before PRAs can be practically adopted. Techniques that model the prediction uncertainty and provide probabilistic quality of service (QoS) guarantees are among such challenges. This differs from the traditional robust optimization of wireless resources, as PRAs use a time horizon with predicted demands and anticipated data rates. In this paper, we tackle this problem and present an energy-efficient stochastic PRAs framework that is robust to prediction uncertainty under generic error probability density functions. The framework is applied for video delivery, where the desired video demands are modeled as probabilistic chance constraints over the prediction time horizon, and a deterministic closed form is then derived based on the Bernstein approximation (BA). In addition to handling prediction uncertainty, mechanisms that track the variance of the channel in real-time are practically needed. Towards this end, we demonstrate how a particle filter (PF) can be adopted to effectively achieve this functionality. A low complexity guided heuristic algorithm is also integrated with the BA-based allocations, and particle filter (PF), to provide a real-time solution. Extensive numerical simulations using a standard compliant long term evolution system are then presented to examine the developed solutions under various operating conditions. Results indicate the ability of our framework to significantly reduce base station energy consumption while satisfying users’ QoS under practical prediction uncertainty.