Covert communications, or covert channels, are commonly exploited to establish a data exfiltration channel from an insider on a trusted network to a malicious receiver outside the network without using normal communication of the network. It is because the malicious receiver is an unauthorized user of the communication network and so he cannot communicate with any entity in the network. In this study, we construct a new covert wireless unidirectional communication mechanism in an IEEE 802.11 environment. Our covert communication is based on a covert timing channel exploiting the beacon interval of a given commercial-like AP. Because the wireless covert channel we proposed can be implemented only with firmware modification to the WLAN MAC protocol, it is very suitable for application in a real public AP environment. In order to dramatically reduce the chance of covert signals being detected by others, a new and simple covert data encoding scheme, called ping-pong covert timing channel (PPCTC), is proposed, and we show that the covertness of the PPCTC is excellent compared to the previous timing-based covert channels. Although this wireless covert communication is unidirectional communication, since PPCTC has recovery characteristics against consecutive 2-bit errors, stable communication is guaranteed. Furthermore, a covert frame structure is presented for providing the confidentiality and integrity of the information transmitted via our covert channel. To the best of our knowledge, this is the first attempt.

Massive Internet of Things (IoT) networks have a wide range of applications, including but not limited to the rapid delivery of emergency and disaster messages. Although various benchmark algorithms have been developed to date for message delivery in such applications, they pose several practical challenges, such as insufficient network coverage and/or highly redundant transmissions to expand the coverage area, resulting in considerable energy consumption for each IoT device. To overcome this problem, we first characterize a new performance metric, forwarding efficiency, which is defined as the ratio of the coverage probability to the average number of transmissions per device, to evaluate the data dissemination performance more appropriately. Then, we propose a novel and effective forwarding method, fishbone forwarding (FiFo), which aims to improve the forwarding efficiency with acceptable computational complexity. Our FiFo method completes two tasks: 1) it clusters devices based on the unweighed pair group method with the arithmetic average and 2) it creates the main axis and subaxes of each cluster using both the expectation-maximization algorithm for the Gaussian mixture model and principal component analysis. We demonstrate the superiority of FiFo by using a real-world data set. Through intensive and comprehensive simulations, we show that the proposed FiFo method outperforms benchmark algorithms in terms of the forwarding efficiency.

Massive Internet of Things (IoT) networks have a wide range of applications, including but not limited to the rapid delivery of emergency and disaster messages. Although various benchmark algorithms have been developed to date for message delivery in such applications, they pose several practical challenges such as insufficient network coverage and/or highly redundant transmissions to expand the coverage area, resulting in considerable energy consumption for each IoT device. To overcome this problem, we first characterize a new performance metric, forwarding efficiency, which is defined as the ratio of the coverage probability to the average number of transmissions per device, to evaluate the data dissemination performance more appropriately. Then, we propose a novel and effective forwarding method, fishbone forwarding (FiFo), which aims to improve the forwarding efficiency with acceptable computational complexity. Our FiFo method completes two tasks: 1) it clusters devices based on the unweighted pair group method with the arithmetic average; and 2) it creates the main axis and sub axes of each cluster using both the expectation-maximization algorithm for the Gaussian mixture model and principal component analysis. We demonstrate the superiority of FiFo by using a real-world dataset. Through intensive and comprehensive simulations, we show that the proposed FiFo method outperforms benchmark algorithms in terms of the forwarding efficiency.