Established approaches to data aggregation in wireless sensor networks (WSNs) do not cover the variety of new use cases developing with the advent of the Internet of Things. In particular, the current push towards fog computing, in which control, computation, and storage are moved to nodes close to the network edge, induces a need to collect data at multiple sinks, rather than the single sink typically considered in WSN aggregation algorithms. Moreover, for machine-to-machine communication scenarios, actuators subscribing to sensor measurements may also be present, in which case data should be not only aggregated and processed in-network, but also disseminated to actuator nodes. In this paper, we present mixed-integer programming formulations and algorithms for the problem of energy-optimal routing and multiple-sink aggregation, as well as joint aggregation and dissemination, of sensor measurement data in IoT edge networks. We consider optimisation of the network for both minimal total energy usage, and min-max per-node energy usage. We also provide a formulation and algorithm for throughput-optimal scheduling of transmissions under the physical interference model in the pure aggregation case. We have conducted a numerical study to compare the energy required for the two use cases, as well as the time to solve them, in generated network scenarios with varying topologies and between 10 and 40 nodes. Although aggregation only accounts for less than 15% of total energy usage in all cases tested, it provides substantial energy savings. Our results show more than 13 times greater energy usage for 40-node networks using direct, shortest-path flows from sensors to actuators, compared with our aggregation and dissemination solutions.

Optical wireless networks realized by means of gigabit optical wireless communication (OWC) systems are becoming, in a variety of applications, an important alternative, or a complementary solution, to their fiber-based counterparts. However, performance of the OWC systems can be considerably degraded in periods of unfavorable weather conditions, such as heavy fog, which temporarily reduce the effective capacity of the network. In this paper, we focus on optical wireless mesh networks that use terrestrial links (called FSO - free-space optical - links) composed of several parallel full-duplex FSO systems, and present two complementary solutions that together provide a means to maximize network traffic performance in various weather conditions encountered during network operation. The first solution is a method for estimating the degradation of the effective FSO link capacity in adverse weather conditions such as fog, rain and snow (called the weather states in this paper). The second solution is an optimization model aiming at maximizing the network traffic throughput for a given list of weather states, derived from the conducted measurements. The model assumes the so-called affine flow thinning (AFT) traffic routing and protection mechanism capable of controlling the end-to-end traffic flows in response to fluctuations of capacity available on FSO links caused by changing weather conditions. The proposed link capacity modeling approach and the elaborated optimization model are verified through an exhaustive numerical study, illustrating the trade-off between the increase of traffic performance of the FSO networks and the corresponding cost of additional OWC systems.

In recent years, since the cloud can provide tremendous advantages regarding storage and computing resources, the industry has been motivated to move industrial control systems to the cloud. However, the cloud also introduces significant security challenges since moving control systems to the cloud can enable attackers to infiltrate the system and establish an attack that can lead to damages and disruptions with potentially catastrophic consequences. Therefore, some security measures are necessary to detect these attacks in a timely manner and mitigate their impact. In this paper, we propose a security framework for cloud control systems that makes them resilient against attacks. This framework includes three steps: attack detection, attack isolation, and attack mitigation. We validate our proposed framework on a real testbed and evaluate its capability by subjecting it to a set of attacks. We show that our proposed solution can detect an attack in a timely manner and keep the plant stable, with high performance during the attack.

Discontinuous Reception (DRX) has been included in 4G-LTE as the main power saving mechanism for User Equipment (UE). However, the existing 3-state DRX model is not sufficient for new use cases introduced by 4G and 5G. For example, the device discovery process in Device to Device (D2D) communication has a significant impact on delay and power consumption, but the existing conventional 3-state DRX model is unable to capture this impact. In this paper, we propose a novel 5-state DRX model for D2D communications using a semi-Markov process, with separate arrival processes for data and discovery messages. The proposed model includes separate states for the discovery process, and is general and easy to extend for possible future changes. Our model allows us to investigate the behavior of the system under various DRX and discovery parameters. Numerical results show significant influence of the discovery phase on the performance of the DRX mechanism and provide new insights into how it affects power saving and delay.

Massive multiple-input multiple-output (MIMO) is expected to be a vital component in future 5G systems. As such, there is a need for new modeling in order to investigate the performance of massive MIMO not only at the physical layer but also higher up the networking stack. In this paper, we present general optimization models for massive MIMO, based on mixed-integer programming and compatible sets, with both maximum ratio combining and zero-forcing precoding schemes. We then apply our models to the case of joint device scheduling and power control for heterogeneous devices and traffic demands, in contrast to the existing power control schemes that consider only homogeneous users and saturated scenarios. Our results show that substantial benefits, in terms of energy usage, can be achieved without sacrificing throughput and that both the signaling overhead and the complexity of end devices can be reduced by abrogating the need for uplink power control through efficient scheduling.