Analysis of Slotted ALOHA with an Age Threshold

We present a comprehensive steady-state analysis of threshold-ALOHA, a distributed age-aware modification of slotted ALOHA proposed in recent literature. In threshold-ALOHA, each terminal suspends its transmissions until the Age of Information (AoI) of the status update flow it is sending reaches a certain threshold $\Gamma$. Once the age exceeds $\Gamma$, the terminal attempts transmission with constant probability $\tau$ in each slot, as in standard slotted ALOHA. We analyze the time-average expected AoI attained by this policy, and explore its scaling with network size, $n$. We derive the probability distribution of the number of active users at steady state, and show that as network size increases the policy converges to one that runs slotted ALOHA with fewer sources: on average about one fifth of the users is active at any time. We obtain an expression for steady-state expected AoI and use this to optimize the parameters $\Gamma$ and $\tau$, resolving the conjectures in \cite{doga} by confirming that the optimal age threshold and transmission probability are $2.2n$ and $4.69/n$, respectively. We find that the optimal AoI scales with the network size as $1.4169n$, which is almost half the minimum AoI achievable with slotted ALOHA, while the loss from the maximum throughput of $e^{-1}$ remains below $1\%$. We compare the performance of this rudimentary algorithm to that of the SAT policy that dynamically adapts its transmission probabilities

Underwater Data Collection Using Robotic Sensor Networks

We examine the problem of utilizing an autonomous underwater vehicle (AUV) to collect data from an underwater sensor network. The sensors in the network are equipped with acoustic modems that provide noisy, range-limited communication. The AUV must plan a path that maximizes the information collected while minimizing travel time or fuel expenditure. We propose AUV path planning methods that extend algorithms for variants of the Traveling Salesperson Problem (TSP). While executing a path, the AUV can improve performance by communicating with multiple nodes in the network at once. Such multi-node communication requires a scheduling protocol that is robust to channel variations and interference. To this end, we examine two multiple access protocols for the underwater data collection scenario, one based on deterministic access and another based on random access. We compare the proposed algorithms to baseline strategies through simulated experiments that utilize models derived from experimental test data. Our results demonstrate that properly designed communication models and scheduling protocols are essential for choosing the appropriate path planning algorithms for data collection.United States. Office of Naval Research (ONR N00014-09-1-0700)United States. Office of Naval Research (ONR N00014-07-1-00738)National Science Foundation (U.S.) (NSF 0831728)National Science Foundation (U.S.) (NSF CCR-0120778)National Science Foundation (U.S.) (NSF CNS-1035866

Counter-intuitive throughput behaviors in networks under end-to-end control

It has been shown that as long as traffic sources adapt their rates to aggregate congestion measure in their paths, they implicitly maximize certain utility. In this paper we study some counter-intuitive throughput behaviors in such networks, pertaining to whether a fair allocation is always inefficient and whether increasing capacity always raises aggregate throughput. A bandwidth allocation policy can be defined in terms of a class of utility functions parameterized by a scalar a that can be interpreted as a quantitative measure of fairness. An allocation is fair if alpha is large and efficient if aggregate throughput is large. All examples in the literature suggest that a fair allocation is necessarily inefficient. We characterize exactly the tradeoff between fairness and throughput in general networks. The characterization allows us both to produce the first counter-example and trivially explain all the previous supporting examples. Surprisingly, our counter-example has the property that a fairer allocation is always more efficient. In particular it implies that maxmin fairness can achieve a higher throughput than proportional fairness. Intuitively, we might expect that increasing link capacities always raises aggregate throughput. We show that not only can throughput be reduced when some link increases its capacity, more strikingly, it can also be reduced when all links increase their capacities by the same amount. If all links increase their capacities proportionally, however, throughput will indeed increase. These examples demonstrate the intricate interactions among sources in a network setting that are missing in a single-link topology

Protocols for multi-antenna ad-hoc wireless networking in interference environments

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 231-242).A fundamental question for the design of future wireless networks concerns the nature of spectrum management and the protocols that govern use of the spectrum. In the oligopoly model, spectrum is owned and centrally managed, and the protocols tend to reflect this centralized nature. In the common's model, spectrum is a public good, and protocols must support ad hoc communication. This work presents the design, tradeoffs and parameter optimization for a new protocol (Simultaneous Transmissions in Interference (STI-MAC)) for ad hoc wireless networks. The key idea behind the STI-MAC protocol is 'channel stuffing,' that is, allowing network nodes to more efficiently use spatial, time and frequency degrees of freedom. This is achieved in three key ways. First, 'channel stuffing' is achieved through multiple antennas that are used at the receiver to mitigate interference using Minimum-Mean-Squared-Error (MMSE) receivers, allowing network nodes to transmit simultaneously in interference limited environments. The protocol also supports the use of multiple transmit antennas to beamform to the target receiver. Secondly, 'channel stuffing' is achieved through the use of a control channel that is orthogonal in time to the data channel, where nodes contend in order to participate on the data channel. And thirdly, 'channel stuffing' is achieved through a protest scheme that prevents data channel overloading. The STI-MAC protocol is analyzed via Monte-Carlo simulations in which transmitter nodes are uniformly distributed in a plane, each at a fixed distance from their target receiver; and as a function of network parameters including the number of transmit and receive antennas, the distance between a transmitter-receiver pair (link-length), the average number of transmitters whose received signal is stronger at a given receiver than its target transmitter (link-rank), number of transmitter-receiver pairs, the distribution on the requested rate, the offered load, and the transmit scheme. The STI-MAC protocol is benchmarked relative to simulations of the 802.11(n) (Wi-Fi) protocol. The key results of this work show a 3X gain in throughput relative to 802.11(n) in typical multi-antenna wireless networks that have 20 transmitter-receiver pairs, a link-length of 10 meters, four receive antennas and a single transmit antenna. We also show a reduction in delay by a factor of two when the networks are heavily loaded. We find that the link-rank is a key parameter affecting STIMAC gains over Wi-Fi. In simulations of networks with 40 transmit-receiver pairs, link-rank of three, a link-length of 10 meters, and eight transmit and receive antennas in which the transmitter beamforms to its target receiver in its strongest target channel mode, we find gains in throughput of at least 5X over the 802.11(n) protocol.by Danielle A. Hinton.Ph.D

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)

Packet latency of deterministic broadcasting in adversarial multiple access channels

We study broadcasting in multiple access channels with dynamic packet arrivals and jamming. Communication environments are represented by adversarial models that specify constraints on packet arrivals and jamming. We consider deterministic distributed broadcast algorithms and give upper bounds on the worst-case packet latency and the number of queued packets in relation to the parameters defining adversaries. Packet arrivals are determined by a rate of injections and a number of packets that can be generated in one round. Jamming is constrained by a rate with which an adversary can jam rounds and by a number of consecutive rounds that can be jammed