You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
This paper presents a simple local medium access control protocol, called , for multi-hop wireless networks with a single channel that is provably robust against adaptive adversarial jamming. The wireless network is modeled as a unit disk graph on a set of nodes distributed arbitrarily in the plane. In addition to these nodes, there are adversarial jammers that know the protocol and its entire history and that are allowed to jam the wireless channel at any node for an arbitrary @math -fraction of the time steps, where @math is an arbitrary constant. We assume that the nodes cannot distinguish between jammed transmissions and collisions of regular messages. Nevertheless, we show that achieves an asymptotically optimal throughput if there is a sufficiently dense distribution of nodes.

Cited Abstract(s):
[1]: 

[2]: We consider a scenario where a sophisticated jammer jams an area in a single-channel wireless sensor network. The jammer controls the probability of jamming and transmission range to cause maximal damage to the network in terms of corrupted communication links. The jammer action ceases when it is detected by a monitoring node in the network, and a notification message is transferred out of the jamming region. The jammer is detected at a monitor node by employing an optimal detection test based on the percentage of incurred collisions. On the other hand, the network computes channel access probability in an effort to minimize the jamming detection plus notification time. In order for the jammer to optimize its benefit, it needs to know the network channel access probability and number of neighbors of the monitor node. Accordingly, the network needs to know the jamming probability of the jammer. We study the idealized case of perfect knowledge by both the jammer and the network about the strategy of one another, and the case where the jammer or the network lack this knowledge. The latter is captured by formulating and solving optimization problems, the solutions of which constitute best responses of the attacker or the network to the worst-case strategy of each other. We also take into account potential energy constraints of the jammer and the network. We extend the problem to the case of multiple observers and adaptable jamming transmission range and propose a intuitive heuristic jamming strategy for that case.

[3]: 802.11a, b, and g standards were designed for deployment in cooperative environments, and hence do not include mechanisms to protect from jamming attacks. In this paper, we explore how to protect 802.11 networks from jamming attacks by having the legitimate transmission hop among channels to hide the transmission from the jammer. Using a combination of mathematical analysis and prototype experimentation in an 802.11a environment, we explore how much throughput can be maintained in comparison to the maintainable throughput in a cooperative, jam-free environment. Our experimental and analytical results show that in today's conventional 802.11a networks, we can achieve up to 60 of the original throughput. Our mathematical analysis allows us to extrapolate the throughput that can be maintained when the constraint on the number of orthogonal channels used for both legitimate communication and for jamming is relaxed.

[4]: 

[5]: This paper considers the problem of an attacker disrupting an encrypted victim wireless ad hoc network through jamming. Jamming is broken down into layers and this paper focuses on jamming at the Transport Network layer. Jamming at this layer exploits AODV and TCP protocols and is shown to be very effective in simulated and real networks when it can sense victim packet types, but the encryption is assumed to mask the entire header and contents of the packet so that only packet size, timing, and sequence is available to the attacker for sensing. A sensor is developed and tested on live data. The classification is found to be highly reliable for many packet types. The relative roles of size, timing, and sequence are discussed along with the implications for making networks more secure.

[6]: In this paper, we address the problem of cross-layer denial of service attack in wireless data networks. We introduce SPREAD -a novel adaptive diversification approach to provide resiliency against such attacks. SPREAD relies on a mechanism-hopping technique, which can be seen as a multi-layer extension of the frequency-hopping technique. We apply a game-theoretic framework for modeling the interaction of the communicating nodes and the adversaries and analyze the proposed approach. We reason about the advantages of SPREAD against various types of jammers and demonstrate the effectiveness of our approach in the case of IEEE 802.11 protocol stack by studying the EIFS attack, periodical jamming and a Packet-Size Game. As an example, we show that mechanism-hopping over two instances of IEEE 802.11 can achieve several orders of magnitude gain in throughput over a single-instance network under the EIFS attack.
Traditionally, jamming defense mechanisms operate on the physical layer [6] [3] [4] . Mechanisms have been designed to avoid jamming as well as detect jamming. Spread spectrum technology has been shown to be very effective to avoid jamming as with widely spread signals, it becomes harder to detect the start of a packet quickly enough in order to jam it. Unfortunately, protocols such as IEEE 802.11b use relatively narrow spreading [1] , and some other IEEE 802.11 variants spread signals by even smaller factors [5] . Therefore, a jammer that simultaneously blocks a small number of frequencies renders spread spectrum techniques useless in this case. As jamming strategies can come in many different flavors, detecting jamming activities by simple methods based on signal strength, carrier sensing, or packet delivery ratios has turned out to be quite difficult [2] .