Wireless reconfigurable networks are prone to several security attacks such as selective forwarding, black holes, and wormholes. In this paper, we introduce a network coding method that detects security attacks related to routing. The proposed method works on a distributed fashion performing random network coding over nodes composing a given route, not only to distribute content, but also to provide data confidentiality by cooperation as a mechanism of detection. The method presents a robust, accurate and fast response under security attacks for varying network conditions, such as density and interference, and it also increases successfully received packets without a significant sacrifice of the bandwidth usage.

Reconfigurable wireless networks, such as ad hoc or wireless sensor networks, do not rely on fixed infrastructure. Nodes must cooperate in the multi-hop routing process. This dynamic and open nature make reconfigurable networks vulnerable to routing attacks that could degrade significantly network performance. Intrusion detection systems consist of a set of techniques designed to identify hostile behavior. In this paper, there are several approaches for intrusion detection in reconfigurable network routing such as collaborative, statistical, or machine learning-based techniques. In this paper, we introduce a new approach to intrusion detection for reconfigurable network routing based on linear systems theory. Using this approach, we can discriminate routing attacks by considering the system's z-plane poles. The z-plane can be thought of as a two dimensional feature space that arises naturally. It is independent of the number of network attack detection metrics and does not require extra dimensionality reduction. Two different host-based intrusion detection techniques, inspired by this new linear systems perspective, are presented and analyzed through a case study. The case study considers the effects of attack severity and node mobility to the attack detection performance. High attack detection accuracy was obtained without increasing packet overhead for both techniques by analyzing locally available information.

The design of routing protocols for wireless sensor networks (WSNs) has been traditionally tackled by assuming battery-powered sensors, in which minimizing the power consumption was the main objective. Advances in technology and the ability to harvest energy from the environment has enabled self-sustaining systems and thus diminish the significance of network lifetime considerations in the design of WSNs. Although WSNs operated by energy-harvesting sensors are not limited by network lifetime, they still pose new design challenges due to the unstable and uncertain amount of energy that can be harvested from the environment. In this paper, we propose a new protocol for energy-harvesting sensor networks that uses adaptive transmission power to maintain the network connectivity, and distributes the traffic load on the network. Based on local information, each node dynamically adjusts its transmission power in order to maximize the network's end-to-end performance. The simulation results indicate that the proposed protocol keeps the network connected at most of the times by using an efficient power management, outperforming greedy forwarding and dynamic duty cycle protocols in terms of packet delivery ratio, delay, and power management.

Fast development in hardware miniaturization and massive production of sensors make them cost efficient and vastly available to be used in various applications in our daily life more specially in environment monitoring applications. However, energy consumption is still one of the barriers slowing down the development of several applications. Slow development in battery technology, makes energy harvesting (EH) as a prime candidate to eliminate the sensor’s energy barrier. EH sensors can be the solution to enabling future applications that would be extremely costly using conventional battery-powered sensors. In this paper, we analyze the performance improvement and evaluation of EH sensors in various situations. A network model is developed to allow us to examine different scenarios. We borrow a clustering concept, as a proven method to improve energy efficiency in conventional sensor network and brought it to EH sensor networks to study its effect on the performance of the network in different scenarios. Moreover, a dynamic and distributed transmission power management for sensors is proposed and evaluated in both networks, with and without clustering, to study the effect of power balancing on the network end-to-end performance. The simulation results indicate that, by using clustering and transmission power adjustment, the power consumption can be distributed in the network more efficiently, which result in improving the network performance in terms of a packet delivery ratio by 20%, 10% higher network lifetime by having more alive nodes and also achieving lower delay by reducing the hop-count.

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