Adversarial Multiple Access Channel with Individual Injection Rates

Anantharamu, Lakshmi; Chlebus, Bogdan S.; Rokicki, Mariusz A.

doi:10.1007/978-3-642-10877-8_15

Cited by 23 publications

(35 citation statements)

References 22 publications

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“…What remains to show is the upper bound of (6). Suppose, to arrive at a contradiction, that the number of time steps j in [t 1 , t 2 ], that satisfies the rightmost inequality of condition (6), is less than ϕ(ω − 2).…”

Section: Lemma 14mentioning

confidence: 99%

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Scalable wake-up of multi-channel single-hop radio networks

Chlebus

Marco

Kowalski

2016

Theoretical Computer Science

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We consider single-hop radio networks with multiple channels as a model of wireless networks. There are n stations connected to b radio channels that do not provide collision detection. A station uses all the channels concurrently and independently. Some k stations may become active spontaneously at arbitrary times. The goal is to wake up the network, which occurs when all the stations hear a successful transmission on some channel. Duration of a waking-up execution is measured starting from the first spontaneous activation. We present a deterministic algorithm for the general problem that wakes up the network in O(k log 1/b k log n) time, where k is unknown. We give a deterministic scalable algorithm for the special case when b > d log log n, for some constant d > 1, which wakes up the network in O( k b log n log(b log n)) time, with k unknown. This algorithm misses time optimality by at most a factor of O(log n(log b+log log n)), because any deterministic algorithm requires Ω( k b log n k ) time. We give a randomized algorithm that wakes up the network within O(k 1/b ln 1 ǫ ) rounds with a probability that is at least 1 − ǫ, for any 0 < ǫ < 1, where k is known. We also consider a model of jamming, in which each channel in any round may be jammed to prevent a successful transmission, which happens with some known parameter probability p, independently across all channels and rounds. For this model, we give two deterministic algorithms for unknown k: one wakes up the network in time O(log −1 ( 1 p ) k log n log 1/b k), and the other in time O(log −1 ( 1 p ) k b log n log(b log n)) but assuming the inequality b > log(128b log n), both with a probability that is at least 1 − 1/poly(n).

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Section: Lemma 14mentioning

confidence: 99%

“…Suppose, to arrive at a contradiction, that the number of time steps j in [t 1 , t 2 ], that satisfies the rightmost inequality of condition (6), is less than ϕ(ω − 2). Let B ⊆ [t 1 , t 2 ] be the set of balanced time steps j ∈ [t 1 , t 2 ] such that condition (6) is not satisfied.…”

Section: Lemma 14mentioning

confidence: 99%

Scalable wake-up of multi-channel single-hop radio networks

Chlebus

Marco

Kowalski

2016

Theoretical Computer Science

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“…In particular, given any C and any T , the total bandwidth requested by the clients in C (specifically, c∈C b c |τ c ∩ T |/w) has to be no larger that a fraction ρ of the bandwidth that can be provided by the stations that can serve the clients in C (S(C )) plus a constant term β (that allows for some burstiness). The second condition (2) imposes that the requested bandwidth b c of each client must be no larger that the bandwidth B of each station. Naturally, if some client had a request of bandwidth larger than B it would be impossible to satisfy it.…”

Section: Adversarial Model and Problem Definitionmentioning

confidence: 99%

“…Later, there were approaches to apply it in the context of wireless networks: modelled as time-varying channels [4], radio channels with collisions [12,2], or SINR networks [15]. In a single-hop radio channels with collisions, more detail competitive analysis of dynamic and stochastic traffic was performed [9].…”

Section: Related Workmentioning

confidence: 99%

“…If B max ≤ B, we are done. Otherwise, given that B max > B, station s has more than one client assigned in slot i, since otherwise the client arrival schedule would violate Equation (2). If the sum of the bandwidth used on some pairs (s , j) and (s , k) is at most B, consider another assignment A where the clients assigned to (s , j) and (s , k) in A are now all assigned to (s , j), and the clients assigned to (s, i) in A are now split between (s, i) and (s , k).…”

Section: Unique Stations Group and Distinct Client Bandwidthmentioning

confidence: 99%

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Station Assignment with Applications to Sensing

Anta

Kowalski

Mosteiro

et al. 2013

Lecture Notes in Computer Science

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Abstract. We study an allocation problem that arises in various scenarios. For instance, a health monitoring system where ambulatory patients carry sensors that must periodically upload physiological data. Another example is participatory sensing, where communities of mobile device users upload periodically information about their environment. We assume that devices or sensors (generically called clients) join and leave the system continuously, and they must upload/download data to static devices (or base stations), via radio transmissions. The mobility of clients, the limited range of transmission, and the possibly ephemeral nature of the clients are modeled by characterizing each client with a life interval and a stations group, so that different clients may or may not coincide in time and/or stations to connect. The intrinsically shared nature of the access to base stations is modeled by introducing a maximum station bandwidth that is shared among its connected clients, a client laxity, which bounds the maximum time that an active client is not transmitting to some base station, and a client bandwidth, which bounds the minimum bandwidth that a client requires in each transmission. Under the model described, we study the problem of assigning clients to base stations so that every client transmits to some station in its group, limited by laxities and bandwidths. We call this problem the Station Assignment problem. We study the impact of the rate and burstiness of the arrival of clients on the solvability of Station Assignment. To carry out a worst-case analysis we use a typical adversarial methodology: we assume the presence of an adversary that controls the arrival and departure of clients. The adversary is limited by two parameters that model the rate and the burstiness of the stations load (hence, limitting the rate and burstiness of the client arrivals). Specifically, we show upper and lower bounds on the rate and burstiness of the arrival for various client arrival schedules and protocol classes. The problem has connections with Load Balancing and Scheduling, usually studied using competitive analysis. To the best of our knowledge, this is the first time that the Station Assignment problem is studied under adversarial arrivals.

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Broadcasting in Ad Hoc Multiple Access Channels

Anantharamu

Chlebus

2013

Structural Information and Communication Complexity

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We study broadcast in multiple access channels in dynamic adversarial settings. There is an unbounded supply of anonymous stations attached to a synchronous channel. There is an adversary who injects packets into stations to be broadcast on the channel. The adversary is restricted by injection rate, burstiness, and by how many passive stations can be simultaneously activated by providing them with packets. We consider deterministic distributed broadcast algorithms, which are further categorized by their properties. We investigate for which injection rates can algorithms attain bounded packet latency, when adversaries are restricted to be able to activate at most one station per round. The rates of algorithms we present make the increasing sequence consisting of

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Adversarial Multiple Access Channel with Individual Injection Rates

Cited by 23 publications

References 22 publications

Scalable wake-up of multi-channel single-hop radio networks

Scalable wake-up of multi-channel single-hop radio networks

Station Assignment with Applications to Sensing

Broadcasting in Ad Hoc Multiple Access Channels

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