In-body communication is a key enabler for next-generation healthcare applications, allowing seamless networking of implants. Fat tissue, with its lower water content and reduced signal attenuation compared to other body tissues at microwave frequencies, has emerged as a promising medium for radio-based in-body networks. Despite this advantage, signal leakage through the body can compromise privacy, exposing sensitive data and the mere presence of implants to external adversaries. This paper investigates the feasibility of covert communication in fat tissue-based in-body networks by leveraging the previously unexplored signal attenuation properties of human tissue to transmit data undetectable to adversaries, ensuring privacy beyond encryption. We develop a system in which an implanted transmitter communicates discreetly with an implanted receiver, shielded from external passive eavesdroppers. Our theoretical analysis and experimental results demonstrate that the attenuation properties of human tissues enable covert communication at reduced transmit power levels without requiring friendly jamming, unlike over-the-air systems. To further enhance covertness, we explore the use of an external friendly jammer and show its significant benefits. Experimental results show a 500% increase in the maximum channel capacity of covert communication, from 2.86 bps/Hz at -56 dBm transmit power without jamming, to 17 bps/Hz with no bit errors at 0 dBm transmit power with a friendly jammer, using the IEEE 802.15.4 standard for communication in the 2.45 GHz frequency band. These findings highlight that covert communication is achievable in fat tissue-based in-body networks at low data rates without additional infrastructure such as an external jammer. For applications requiring higher data rates, a friendly jammer offers a scalable solution, making this approach practical for a wide range of implant communication scenarios.

The traditional slow-start mechanism in TCP can result in slow ramping-up of the data delivery rate, inefficient bandwidth utilization, and prolonged completion time for small-size flows, especially in networks with a large bandwidth-delay product (BDP). Existing solutions either only work in specific situations, or require network assistance, making them challenging (if even possible) to deploy. This paper presents SUSS (Speeding Up Slow Start): a lightweight, sender-side add-on to the traditional slow-start mechanism, that aims to safely expedite the growth of the congestion window when a flow is significantly below its optimal fair share of the available bandwidth. SUSS achieves this by accelerating the growth in cwnd when exponential growth is predicted to continue in the next round. SUSS employs a novel combination of ACK clocking and packet pacing to effectively mitigate traffic burstiness caused by accelerated increases in cwnd. We have implemented SUSS in the Linux kernel, integrated into the CUBIC congestion control algorithm. Our real-world experiments span many device types and Internet locations, demonstrating that SUSS consistently outperforms traditional slow-start with no measured negative impacts. SUSS achieves over 20% improvement in flow completion time in all experiments with flow sizes less than 5MB and RTT larger than 50 ms.

The goal of covert communication is to make Alice’s transmissions to Bob indistinguishable from background noise and interference, thereby preventing Eve from detecting the presence of the communication. For effective covert communication, Eve’s detection error should be close to random guessing, while Bob’s error in message recovery should be minimal. The performance of covert communication is limited by the Square Root Law (SRL), which constrains Alice’s per-symbol power and transmission capacity. One solution to increase covertness while keeping the communication reliable is to employ a friendly jammer, however, this is not a feasible solution to every scenario. We therefore explore a cooperative jamming strategy instead of relying on a friendly jammer. In our proposed network scenario, two transmitters, Alice-A and Alice-B, alternate between transmitting to Bob and jamming to confuse Eve. We model the interaction between Alice-A and Alice-B using the Alternating Prisoner’s Dilemma game. This model serves as a framework to determine the conditions under which they are incentivized to cooperate. Our findings suggest that rational cooperation can offer a viable alternative to the traditional use of friendly jammers.

Radio links in Wireless Body Area Networks (WBANs) are highly subject to short and long-term attenuation due to the unstable network topology and frequent body blockage. This instability makes it challenging to achieve reliable and energy-efficient communication, but on the other hand, provides a great potential for the sending nodes to dynamically schedule the transmissions at the time with the best-expected channel quality. Motivated by this, we propose IGE (Improved Gilbert-Elliott Markov chain model), a memory-efficient Markov chain model to monitor channel fluctuations and provide a long-term channel prediction. We then design ATPS (Adaptive Transmission Power Selection), a deadline-constrained channel scheduling scheme that enables a sending node to buffer the packets when the channel is bad and schedule them to be transmitted when the channel is expected to be good within a deadline. ATPS can self-learn the pattern of channel changes without imposing a significant computation or memory overhead on the sending node. We evaluate the performance of ATPS through experiments using TelosB motes under different scenarios with different body postures and packet rates. We further compare ATPS with several state-of-the-art schemes including the optimal scheduling policy in which the optimal transmission time for each packet is calculated based on the collected RSSI (Received Signal Strength Indicator) samples in an off-line manner. The experimental results reveal that ATPS performs almost as efficiently as the optimal scheme in high-date-rate scenarios and has a similar trend on power level usage.

Radio links in wireless body area networks (WBANs) suffer from both short-term and long-term variations due to the dynamic network topology and frequent blockage caused by body movements, making it challenging to achieve reliable, energy-efficient and real-time data communication. Through experiments with TelosB motes, we observe a strong positive relationship between the channel quality and the location of the sensor node relative to the gateway. Motivated by this observation, we design Tuatara, a novel power-aware communication protocol that allows each sensor node to dynamically adjust its transmission power based on the channel status inferred from its instant location, aiming to save energy, reduce interference, and improve communication reliability. Combining the orientations measured by motion sensors with the anatomical constraints of body movements, each sensor node can locally estimate its instant location relative to the gateway. Based on a probabilistic model, power level selection is converted to calculate the optimal probability of selecting each power level at a given location, with the objective of minimizing the transmission cost. A learning scheme is designed to adaptively update the power level selection probabilities, making Tuatara self-adaptable to changes in the signal propagation environment. Experimental results demonstrate that Tuatara outperforms the state-of-the-art protocols in various scenarios, with performance close to that of the optimal power selection solution even in scenarios where the packet rate is very low.

In this paper, we address the problem of covert communication under the presence of multiple wardens with a finite blocklength. The system consists of Alice, who aims to covertly transmit to Bob with the help of a jammer. The system also consists of a Fusion Center (FC), which combines all the wardens' information and decides on the presence or absence of Alice. Both Alice and jammer vary their signal power randomly to confuse the FC. In contrast, the FC randomly changes its threshold to confuse Alice. The main focus of the paper is to study the impact of employing multiple wardens on the trade-off between the probability of error at the FC and the outage probability at Bob. Hence, we formulate the probability of error and the outage probability under the assumption that the channels from Alice and jammer to Bob are subject to Rayleigh fading, while we assume that the channels from Alice and jammer to the wardens are not subject to fading. Then, we utilize a two-player zero-sum game approach to model the interaction between joint Alice and jammer as one player and the FC as the second player. We derive the pay-off function that can be efficiently computed using linear programming to find the optimal distributions of transmitting and jamming powers as well as thresholds used by the FC. The benefit of using a cooperative jammer is shown by means of analytical results and numerical simulations to neutralize the advantage of using multiple wardens at the FC.

TCP slow-start grows the congestion window exponentially, aims to quickly probe the throughput of the network path. Stopping this growth at the wrong time can affect the overall network performance. In this paper, we introduce StopEG, an efficient mechanism to accurately and quickly detect when to stop this exponential growth. StopEG reacts to the changes on congestion window size rather than traditional congestion signals such as packet loss. We show that theoretically the number of inflight packets in the forward path is no more than 56.8% of all the inflight packets when the bottleneck link is unsaturated, and use this value as the threshold to stop the exponential growth. StopEG is evaluated through simulations in ns-3 by incorporating it into Google's BBR congestion control algorithm. Simulation results demonstrate its effectiveness in BBR, with a reduction of ≈68% in the length of the bottleneck queue when new connections are initiated.

Radio links in wireless body area networks (WBANs) commonly experience highly time-varying attenuation due to the dynamic network topology and frequent occlusions caused by body movements, making it challenging to design a reliable, energy-efficient, and real-time communication protocol for WBANs. In this article, we present Chimp, a learning-based power-aware communication protocol in which each sending node can self-learn the channel quality and choose the best transmission power level to reduce energy consumption and interference range while still guaranteeing high communication reliability. Chimp is designed based on learning automata that uses only the acknowledgment packets and motion data from a local gyroscope sensor to infer the real-time channel status. We design a new cost function that takes into account the energy consumption, communication reliability and interference and develop a new learning function that can guarantee to select the optimal transmission power level to minimize the cost function for any given channel quality. For highly dynamic postures such as walking and running, we exploit the correlation between channel quality and motion data generated by a gyroscope sensor to fastly estimate channel quality, eliminating the need to use expensive channel sampling procedures. We evaluate the performance of Chimp through experiments using TelosB motes equipped with the MPU-9250 motion sensor chip and compare it with the state-of-the-art protocols in different body postures. Experimental results demonstrate that Chimp outperforms existing schemes and works efficiently in most common body postures. In high-date-rate scenarios, it achieves almost the same performance as the optimal power assignment scheme in which the optimal power level for each transmission is calculated based on the collected channel measurements in an off-line manner.

Since radio links in wireless body area networks (WBANs) commonly experience highly time-varying attenuation due to topology instability, communication protocols with fixed transmission power cannot produce a very good performance in terms of energy consumption, interference range, and communication reliability. We explain that how channel behaviourcan be modelled using Markov Chain. Then, a power-adaptive communication protocol for WBANs is developed in which each sensor node can self-learn its channel and dynamically adjust itstransmission power. We evaluate our scheme through implementing the idea using the TelosB motes. The results demonstrate that our scheme can self-learn the channel behaviours, and reduce energy consumption and interference.