Recent Internet of Things (IoT) security incidents indicate that current IoT defense methods are insufficient to defend against DDoS attacks due to a lack of timely and plausible mitigation mechanisms. These methods may make wrong decisions on traffic throttling when network traffic patterns dynamically change. To swiftly and properly defend against DDoS attacks in IoT, we propose a novel deep reinforcement learning (DRL) based DDoS defense approach. Our goals are to both safeguard normal traffic and discard malicious traffic. To achieve these goals, we establish a software defined networking (SDN) based IoT network by replacing traditional IoT gateways at certain locations with SDN switches. The proposed method consists of network monitoring, reward evaluation, and defense policy execution. The DRL agent is able to monitor the whole network by utilizing various traffic features from the SDN-enabled IoT network framework. We propose an adaptive punishment based reward function to accelerate the learning procedure. We also propose an available bandwidth allocation algorithm to refine defense policy so as to protect more normal traffic. The experimental results demonstrate that the proposed method could determine optimal defense policy faster by around 200 episodes and forward more normal traffic by around 22%.

We consider the behavior of mobile users that, upon depletion of their device's energy due to communication, can detour from their regular path, in order to reach a location where the device can be recharged. We develop abstractions of this recharging behavior and analytically derive a charging-aware mobility model. The device is viewed as a mobile node. Based on the Palm inversion formula, we derive the (integral-form) stationary probability density function of the node's location, subject to attraction exerted by one charger in a bounded convex area. The analytical and numerical results demonstrate the distortion effect the charger has on the spatial node distribution. We observe, and explain, a counter-intuitive effect whereby the node density exhibits peaks that are asymmetric, coupled with a relative decrease of the density around the charger area. In addition, we provide a method to approximate the probability density function when multiple chargers are deployed. We examine the accuracy of this approximation and comment on its limitations.

Today's mobile battery-powered communication devices require that users access chargers via wired and, recently, wireless recharging facilities. For a device departing from a location with a given energy “budget”, a plausible strategy is to seek a charger location once the energy is exhausted. We present a model of mobile nodes that captures the paths followed by the nodes with depleted energy seeking, possibly via a detour, to reach a charger. The derived location-dependent mobile node density distribution is used to express the location-dependent congestion of a wireless network whose capacity is used by the mobile nodes. The boundaries, and the relative placement of the charger, create intriguing discontinuities in the probability density function of the nodes across space. We find that chargers are not always “hotspots” in terms of node density, and that the energy budget of the nodes at the beginning of their trip impacts that density and determines the hotspot. Moreover, upon energy depletion, to detour from one's path or not, results in a distinctly different relative impact of the charger placement on the ability of nodes to sustain communication, depending, again on the energy budget the nodes have upon departure from a waypoint.

Recently remarkable progress has been made on the development of wireless charging technologies. Their augmentation to Intermittently Connected Mobile ad hoc Networks (ICMNs) can boost the network performance with the benefit of elongated nodal lifespans. This benefit, however, is also accompanied by new potential challenges for protocol designers, as the mobile nodes can assume unconventionally dynamic, heterogeneous, and interdependent movement patterns due to dis/charging processes. The interplay between mobility, energy, and communication can skew the efficacy of existing routing protocols. In this paper, we devise a series of grid-based mobility models to mimic the possible charging-aware moving behaviors of wirelessly chargeable ICMNs that are assisted by renewable charging resources. Properties of the mobility models are investigated through simulation with classic routing protocols for ICMNs (i.e. epidemic routing and randomized flooding). Results show that the charging-aware mobility can enable the nodes to maintain high energy and good packet delivery ratios, depending on the aggressiveness of transmission and distribution of chargers.

Received Signal Strength Indication (RSSI) measurements are known to be noisy and to exhibit high degree of variability, yet massive sets of RSSI data are streamed and collected with the purpose of using them for localization. In this paper we determine that the full resolution of RSSI measurements is unnecessary and propose, based on an evaluation of indoor localization experiments, a technique of modest computational effort, using Genetic Algorithms, to determine how to best quantize RSSI measurements. Our approach treats the localization algorithm as a “black box” and, hence, its applicability is broad. However, for the purpose of exposition and to generate absolute performance metrics, a particular profiling-based localization algorithm is used. Results from experiments involving real collected RSSI values indicate that it is possible to reduce the RSSI data volume by approximately 72% with no noticeable reduction in localization accuracy.

Many-to-one transport results in congestion in wireless sensor networks (WSNs). This is due to the sudden impulse of information flowing to a single destination by single branch. More attention has been paid to congestion control in WSN, but most research works in the past were apt to handle it by controlling output sending rate, and seldom sought solutions on utilizing the idle resources of non-congestion branch. In this paper, an Active Congestion Help (ACOH) mechanism is designed to find and build an auxiliary routing when congestion. For fairness and flow balancing, Adaptive Flow Allocation algorithm (AF) is designed to distribute flow as adaptive proportion between the main routing and the auxiliary. AF is considering the transmitting-receiving rate of congestive and auxiliary node, and quickly converges to a fair and efficient proportion. In ACOH, a simple backpressure is used to handle the data flow which exceeds the maximum throughput. Simulation results validate the effectiveness of our ACOH.

In this paper, we propose a novel multi-tier clustering strategy using cultural-based multiobjective particle swarm optimization (Cultural-MOPSO) for maximizing the longevity of wireless sensor networks. We take into account the distribution of surviving nodes and define three objective functions based on multi-tier cluster hierarchy to fully evaluate every clusterhead election. Simulation results demonstrate that our proposed strategy can achieve energy-efficient multi-tier cluster hierarchy and outperform two other PSO-based clustering schemes in terms of a new metric of network longevity.